Projects - User contributions [en]

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T06:09:42Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.2 Trajectory tracking results

Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control.

[[File:23456zd.png|200px|Trajectory tracking results]]

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T06:09:16Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.2 Trajectory tracking results
Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control.

[[File:23456zd.png|200px|Trajectory tracking results]]

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T06:09:05Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]
5.2 Trajectory tracking results
Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control.

[[File:23456zd.png|200px|Trajectory tracking results]]

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T06:08:41Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.2 Trajectory tracking results
Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control.

[[File:23456zd.png|200px|Trajectory tracking results]]

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T06:08:19Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.2 Trajectory tracking results
Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control.

[[File:23456zd.png|200px|Trajectory tracking results]]

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T06:08:00Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.2 Trajectory tracking results
Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control
[[File:23456zd.png|200px|Trajectory tracking results]]

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T06:07:32Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.2 Trajectory tracking results
Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control
[[File:23456zd.png|200px|thumb|left|Trajectory tracking results]]

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T06:05:50Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.2 Trajectory tracking results
Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control
[[File:23456zd.png|200px|thumb|right|Quanser QBot 2 Mobile Platformt]]

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T06:05:27Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.2 Trajectory tracking results
Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control
[[File:23456zd.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T06:05:06Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.2 Trajectory tracking results
Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control
[[File:23456.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T06:00:14Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results
Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control
[[File:23456zd.png]]

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

File:23456zd.png

2017-06-05T05:59:08Z

A1675401:

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:58:36Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results
Control is the basement of navigation and path-planning of the robot, thus the higher requirement of stability, robustness, and accuracy are needed. As a further step of development to ensure correct operation of the robot, PID control using motion parameters and upcoming path steps to ensure ideal operation of the robot, with minimal overshoot and error inherent in the previous stages of motion design. To achieve desired controlling effect, the fuzzy PID method was employed to control the motions of tracking robot.
In the picture shows the simulation result of trajectory control

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:47:42Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:46:58Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model

Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:46:28Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model
Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]

Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:
[[File:2345zd.png]]

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

File:2345zd.png

2017-06-05T05:45:48Z

A1675401:

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:45:22Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model
Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png]]
Given the wheels speed are vl and vr , the robot speed is which all along the same axis. Then the following express the kinematic model for the different drive system:

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:43:51Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model
Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png|200px|thumb|bottom|The equations of QBot 2]]
[[File:234zd.png]]

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:43:15Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model
Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png|200px|thumb|bottom|The equations of QBot 2]]
[[File:234zd.jpg]]

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:34:55Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model
Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:
[[File:234zd.png|200px|thumb|bottom|The equations of QBot 2]]

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

File:234zd.png

2017-06-05T05:31:57Z

A1675401:

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:31:05Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model
Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
In the figure represent the radius of the wheels, the rotational speed of left and right wheel are wl and wr respectively. Thus, the equations of the linear speed of the left wheel and right wheel are vl=wl*r and vr=wr*r.
If only one of the two wheels rotates, the robot will rotate about the non-moving wheel. If two wheels rotate with the same speed, the robot will rotate one point which is far from the robot. The center of this point is Instantaneous Centre of Curvature (ICC). The distance from the center of the robot to the ICC is r(icc) and the distance between two wheels is d, θ is the angle of the robot, and the robot speed is vc. Then the equations of QBot 2 are:

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:22:27Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model
Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
[[File:zd.jpg]]

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

File:2.png

2017-06-05T05:20:17Z

A1675401: A1675401 uploaded a new version of "File:2.png"

File:2.png

2017-06-05T05:19:32Z

A1675401: A1675401 uploaded a new version of "File:2.png"

File:2.png

2017-06-05T05:18:42Z

A1675401: A1675401 uploaded a new version of "File:2.png"

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:15:37Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model
Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]
[[File:2.jpg]]

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

File:2.png

2017-06-05T05:15:03Z

A1675401: A1675401 uploaded a new version of "File:2.png"

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:10:45Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model
Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.
[[File:1.png|200px|thumb|bottom|Quanser QBot 2 Mobile Platformt]]

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

File:1.png

2017-06-05T05:08:42Z

A1675401: A1675401 uploaded a new version of "File:1.png"

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2017-06-05T05:06:55Z

A1675401: /* 5.Motion Control */

'''General Information'''

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220

Students: Michael Vieceli; Yuanyang Wang; Di Zhang

Superviosrs: Professor Peng Shi & Professor Cheng-Chew Lim;

Technical advisors: Dr. Hongjun Yu & Dr. Bing Yan

== 1.Introduction ==

'''1.1 Motivation'''

The motivation of the project is to investigate and produce a software system which can identify the location of a locomotive robot and to calculate and execute safe movement in an environment containing static and dynamic obstacles. With the development of modern robot techniques and the expansion of the robot applications, mobile robots have becoming widely used in modern world. It is of significant importance to design reliable, fast, and low-cost robotic control systems. Possible applications of this project include smart car autonomous navigation, industrial automation robots working in complex environment, and household robots.

'''1.2 Background'''

As industrial environments accelerate the adoption of automated processes, visual processing techniques are arising as a method of determining trajectory and movement planning. Visual processing also provides a unique benefit in that all machines, obstacles and operators can be located and identified universally, without the requirement of a specialized communication protocol and locating system.
Although it is currently commonplace to use depth sensing 3D cameras to determine precise location and orientation of objects on production/conveyor applications, little is currently available in industry whereby a central control system uses a fixed camera to drive multiple locomotive machines.
Image processing also has the benefit of providing a second level of position feedback of moving devices. Throughout motor lifespan, operational and feedback parameters can fall out of calibration, causing unexpected behaviors to exhibit. Image processing is immune to this problem as it only detects final output of the motion control system.
Based on the different situations the robot is dealing with, path planning methods for robot navigation are generally classified into two major types: the global path planning method and the local path planning method [1]. The main difference between the two kinds of methods are whether the environment information is entirely known or partly known when operating path planning process. Research will be conducted to determine which form of methods will be utilized based on experimental situation.

'''1.3 Objectives'''

(a)Design a software system for path planning to calculate and execute traversal to destination, while adopting an efficient path and avoiding collisions with obstacles.

(b)Design a hardware system, including a camera, computers, and a Quanser robot, to perform the task.

'''1.4 Project Structure'''

The entire project is divided into three major parts:

1.Environment Analysis

2.Path Planning

3.Motion Control

and the design of a graphical user interface (GUI) as the project team has three members. Michael Vieceli is responsible for the environment analysis, Yuanyang Wang focuses on path planning, and motion control is assigned to Di Zhang.
A GUI is designed to integrate individual’s work for hardware implementation.

== 2.System Overview ==

The figure below shows the integration of the whole system with input and output labeled.
[[File:]]

'''2.1 Hardware'''

2.1.1 Camera

2.1.2 Quanser

'''2.2 Software'''

2.2.1 Image processing software

2.2.2 Path planning software

2.2.3 Motion control software

== 3.Image Processing ==

== 4.Path Planning ==

'''4.1 Method'''

The intended method is based on A-star algorithm.
A-star algorithm is devised by P.E.Hart et al in 1968 [2] as an improved algorithm of the Dijkstra algorithm. It uses heuristic searching approach to calculate optimal path. The basic thought of this algorithm is to define a measurement standard, such as the length of path, then find out the waypoint with the least cost of all the possible waypoints. From this waypoint, waypoints tree is constructed and new waypoints with the least cost are addressed until the target is reached. The A-star algorithm is a heuristic search algorithm and works well in simplified environment.

'''4.2 Result'''

== 5.Motion Control ==
5.1

5.2 Kinematic Model
Differential drive often used in two-wheel drive system which each wheel is equipped with a separate actuator. The Quanser QBot 2 Mobile Platform uses it, two separately coaxial driven wheels placed on either side of the robot body which controlled by high-performance DC motors with encoders and drops sensors.

5.3 PID and Fuzzy PID control

5.3 Trajectory tracking results

== 6.Conclusion ==

== 7.Reference ==
[1] N Wang and P Chen, ‘Path planning algorithm of level set based on grid modeling’, in: Computer Design and Applications (ICCDA), International Conference, June 25-27, 2010.

[2] P. E. Hart et al., ‘A formal basis for the heuristic determination of minimum cost paths’, In: IEEE Transactions of Systems Science and Cybernetics, vol. ssc-4, no.2, July, 1968.

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2016-08-27T10:46:20Z

A1675401:

Description: The purpose of this project is to design a robotic system for path planning in congestion area. Potential applications are in autopilot urban transportations. The robot will be located using vision measurement through a camera. The robot models are established as two-wheel discs. Virtual obstacle clusters are manually inserted into the image frame and the computer needs to calculate a suitable path to a user-defined destination. As the obstacles are considered to be physical, collision avoidance is required. The robot can be equipped with sensors and the computer captures images from the vision stream. The communication between the computer and the robots will be through WiFi. Matlab, c++, OpenGL, and OpenCV are some of the softwares, languages and libraries to choose from.

Group number:220
Students: Michael Vieceli; Yuanyang Wang; Di Zhang
Superviosrs: Prof. Peng Shi ; Prof. Cheng-Chew Lim; Dr. Hongjun Yu

Projects:2016s2-220 Path Planning and Collision Avoidance for Aduino Robots

2016-08-27T10:44:38Z

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