Projects - User contributions [en]

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:41:25Z

A1707871: /* Introduction */

== Project Students ==
Muyu Wu, Abrar Ali Chowdhury, Zach Vawser

== Project Supervisors ==
Professor Peng Shi, Professor Cheng-Chew Lim

== Project Advisors ==
Xin Yuan, Yuan Sun, Yang Fei, Zhi Lian

== Abstract ==

In this project, we will be using the concepts in artificial intelligence, control theory and signal processing to create a system in which a team of robots work together through a centralised system to search for, detect, and collect target objects from an unknown environment, then deliver those targets to a destination. The aim of this system will be to test the performance of a single agent system against that of a multi agent system.

== Introduction ==
Autonomous systems are making their way into our world at a blistering pace due to their potential to make our lives easier. In their most successful applications to date, robots and automation have increased productivity, lowered production costs, and generated job opportunities in the tech sector. Some of the most famous examples of these systems include:
*Driverless Cars,
*Autonomous Vacuum Cleaners,
*Assembly Line Robotic Arms

At the core of an autonomous system is the agent, something with the ability to perceive its environment and act upon it using effectors. There may be one or more of these in a system, hence single and multi-agent systems. The primary distinction between these types of systems is that multi-agent ones are dynamic, as agents must handle each other as well as the environment.

The following three concepts are critical to autonomous systems and heavily intertwined.
First up is mapping, which is the action of an agent using its perceptions to develop a model of the environment it’s in. Localisation is where the agent uses a-priori knowledge of its environment, called a map, to determine where it is in that environment based on the landmarks it can see. With that brief introduction to agents, mapping and localisation, we now reach one of the most fundamental aspects of autonomous systems: doing both at the same time. This technique is called simultaneous localisation and mapping or SLAM, and is needed when an agent is in an environment it has no or very little a-priori knowledge of. This project also uses Computer Vision for detecting target objects and other rovers, which informs the path planning for the rovers and the arm.

== Motivation ==
Our aim for this project is to compare single agent systems with multi-agent systems in terms of efficiency in completing identical search and delivery tasks. The system we are building is comprised of multiple robots that must cooperate in an unknown environment to find and retrieve objects of interest. There are two types of robots, a manipulator (which has a mounted robotic arm), and a carrier (which has a flat platform mounted on top of it). The manipulator uses its arm to pick up the target object when it is found and load it onto the carrier. The detection of a target triggers cooperation of this form by the robots. As the task is being carried out, a map of the environment is being created using the data from the robot’s sensors. The sensors being used are a laser range detector and a camera.

== Method ==
The method using for this project is using MATLAB and V-rep co-simulating the real life environment.

=== Path Planning ===
The path planning method using in project is A star.
<gallery>
A Star.png |(A* SEARCH ALGORITHM - GEEKSFORGEEKS)[11]
</gallery>
A star path planning will considering the current cost to the next node and the total cost to the final destination.
It will provide the optimal path for agent from current location to the destination.

=== SLAM ===

=== Control of 3DOF Omnidirectional Rover ===

The system uses a three wheel drive omnidirectional rover platform with three degrees of freedom (3DOF). The four basic control algorithms were compared in terms of position control to achieve a fast and accurate response for the system.

<gallery>
Rover Design in VREP.jpg|Rover Design in VREP
Physical Rover.jpg|Physical Rover Platform
</gallery>

=== Fuzzy Controller ===

To improve the response of the rover further, a fuzzy controller is designed to restrict acceleration when the error is high and to speed up the response at lower error values. The error graphs for the x-direction, y-direction and the angular direction were analysed. The system was given a slow start since the error at the beginning is the largest. The velocity of the rover was analysed at various error levels. When the error values decreased, the fuzzy controller switches to a different amplifier value so that the system is sped up.

== Results ==

=== Testing of Control Algorithms ===
The Kp and Kd values in this example were adjusted such that the response had the shortest rise time while avoiding steady state error and overshoot. The response with the overshoot was caused by both high KP and Kd values. The response with the slowest rise time was due to an extremely low Kd value. A balanced Kp and Kd value set reulted in the response with a quick rise time without causing overshoot.
[[File:PD Controller Tuning.jpg|500px|thumb|center|PD Controller Tuning]]

=== Testing of Fuzzy Logic Controller ===
The dips in the following graph are caused by the switching of the fuzzy logic to different amplifier values, as error values decreased instead of a slow approach to the endpoint, the response was sped up with a higher amplifier value of the controller constants.

[[File:Fuzzy Logic Test.jpg|500px|thumb|center|Fuzzy Logic Test]]

=== SLAM and Path Planning ===
[[File:An Agent Based System for Target Search and Delivery System Overview.png|thumb]]

[[File:TargetDetection.jpg|thumb|Targets are successfully detected based on their red colour, and its location in coordinates are generated accurately in the simulation.]]

[[File:EnvironmnetMap.jpg|thumb|This is a map that was generated during a simulation run. Obstacles are accurately detecting using laser range sensors, and their positions in global coordinates are stored in the map as red dots.]]

[[File:RealEnvironment.jpg|thumb|The map depicted in the previous figure represents data collected from the environment depicted here.]]

[[File:Astar.jpg|thumb|The map provides agents with obstacle location information, which they can use to plan paths with an A* Search to avoid collisions.]]

=== Target Detection ===
[[File:RoverDetected.jpg|thumb|Rovers use the camera to detect the location of other rovers in the environment. They are coloured green, which is the feature that is extracted to identify the rover.]]

Depth estimation using only one camera is achieved by identifying the target object, and then measuring the size of the bounding box when the target is at known distances from the camera. Then, this data is plotted and a trendline is fitted using a Power model to obtain an equation which describes pixels vs. distance. The R values are very close to 1, indicating the line is an appropriate fit.

[[File:DistanceTarget.jpg|thumb|A plot of the distance vs. Bounding Box height for detecting target objects.]]
[[File:DistanceRover.jpg|thumb|A plot of the distance vs. Bounding Box height for rover detection.]]

== References ==
[1] N. Correll, Introduction to autonomous robots, 1st ed. Boulder, Colorado: Magellan Scientific, p. 17.
[2] Sariff, N. and Buniyamin, N., 2020. An Overview Of Autonomous Mobile Robot Path Planning Algorithms - IEEE Conference Publication. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/abstract/document/4339335> [Accessed 15 April 2020].
[3] Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., & Oluwatola, O. (2014). Brief History and Current State of Autonomous Vehicles. In Autonomous Vehicle Technology: A Guide for Policymakers (pp. 55-74). RAND Corporation. Retrieved April 23, 2020, from www.jstor.org/stable/10.7249/j.ctt5hhwgz.11
[4] "Single agent vs multi agent system in AI." Geekboots , Feb 12, 2019, . Retrieved April 23, 2020, From https://www.geekboots.com/story/single-agent-vs-multi-agent-system.
[5] Parikshit H, “Comparison – Centralized, Decentralized and Distributed Systems” GeeksforGeeks, Retrieved April 23, 2020, From https://www.geeksforgeeks.org/comparison-centralized-decentralized-and-distributed-systems/
[6] Cadena, Cesar & Carlone, Luca & Carrillo, Henry & Latif, Yasir & Scaramuzza, Davide & Neira, Jose & Reid, Ian & Leonard, John. (2016). Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age. IEEE Transactions on Robotics. 32. 10.1109/TRO.2016.2624754.
[7] J. Bobrow, S. Dubowsky and J. Gibson, "Time-Optimal Control of Robotic Manipulators Along Specified Paths", The International Journal of Robotics Research, vol. 4, no. 3, pp. 3-17, 1985. Available: 10.1177/027836498500400301.
[8] W. Miller, F. Glanz and L. Kraft, "Application of a General Learning Algorithm to the Control of Robotic Manipulators", The International Journal of Robotics Research, vol. 6, no. 2, pp. 84-98, 1987. Available: 10.1177/027836498700600207.
[9] F. Ribeiro, I. Moutinho, P. Silva, C. Fraga and N. Pereira, "Three omni-directional wheels control on a mobile robot," Control 2004,University of Bath, UK, Sept. 2004.
[10] W. Li, C. Yang, Y. Jiang, X. Liu and C. Su, "Motion Planning for Omnidirectional Wheeled Mobile Robot by Potential Field Method", Journal of Advanced Transportation, vol. 2017, pp. 1-11, 2017. Available: 10.1155/2017/4961383.
[11] "A* Search Algorithm - GeeksforGeeks", GeeksforGeeks, 2020. [Online]. Available: https://www.geeksforgeeks.org/a-search-algorithm/. [Accessed: 19- Oct- 2020].

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:39:37Z

A1707871: /* Testing of Fuzzy Logic Controller */

== Project Students ==
Muyu Wu, Abrar Ali Chowdhury, Zach Vawser

== Project Supervisors ==
Professor Peng Shi, Professor Cheng-Chew Lim

== Project Advisors ==
Xin Yuan, Yuan Sun, Yang Fei, Zhi Lian

== Abstract ==

In this project, we will be using the concepts in artificial intelligence, control theory and signal processing to create a system in which a team of robots work together through a centralised system to search for, detect, and collect target objects from an unknown environment, then deliver those targets to a destination. The aim of this system will be to test the performance of a single agent system against that of a multi agent system.

== Introduction ==
Autonomous systems are making their way into our world at a blistering pace due to their potential to make our lives easier. In their most successful applications to date, robots and automation have increased productivity, lowered production costs, and generated job opportunities in the tech sector. Some of the most famous examples of these systems include:
*Driverless Cars,
*Autonomous Vacuum Cleaners,
*Assembly Line Robotic Arms (pictured below)

At the core of an autonomous system is the agent, something with the ability to perceive its environment and act upon it using effectors. There may be one or more of these in a system, hence single and multi-agent systems. The primary distinction between these types of systems is that multi-agent ones are dynamic, as agents must handle each other as well as the environment.

The following three concepts are critical to autonomous systems and heavily intertwined.
First up is mapping, which is the action of an agent using its perceptions to develop a model of the environment it’s in. Localisation is where the agent uses a-priori knowledge of its environment, called a map, to determine where it is in that environment based on the landmarks it can see. With that brief introduction to agents, mapping and localisation, we now reach one of the most fundamental aspects of autonomous systems: doing both at the same time. This technique is called simultaneous localisation and mapping or SLAM, and is needed when an agent is in an environment it has no or very little a-priori knowledge of. This project also uses Computer Vision for detecting target objects and other rovers, which informs the path planning for the rovers and the arm.

== Motivation ==
Our aim for this project is to compare single agent systems with multi-agent systems in terms of efficiency in completing identical search and delivery tasks. The system we are building is comprised of multiple robots that must cooperate in an unknown environment to find and retrieve objects of interest. There are two types of robots, a manipulator (which has a mounted robotic arm), and a carrier (which has a flat platform mounted on top of it). The manipulator uses its arm to pick up the target object when it is found and load it onto the carrier. The detection of a target triggers cooperation of this form by the robots. As the task is being carried out, a map of the environment is being created using the data from the robot’s sensors. The sensors being used are a laser range detector and a camera.

== Method ==
The method using for this project is using MATLAB and V-rep co-simulating the real life environment.

=== Path Planning ===
The path planning method using in project is A star.
<gallery>
A Star.png |(A* SEARCH ALGORITHM - GEEKSFORGEEKS)[11]
</gallery>
A star path planning will considering the current cost to the next node and the total cost to the final destination.
It will provide the optimal path for agent from current location to the destination.

=== SLAM ===

=== Control of 3DOF Omnidirectional Rover ===

The system uses a three wheel drive omnidirectional rover platform with three degrees of freedom (3DOF). The four basic control algorithms were compared in terms of position control to achieve a fast and accurate response for the system.

<gallery>
Rover Design in VREP.jpg|Rover Design in VREP
Physical Rover.jpg|Physical Rover Platform
</gallery>

=== Fuzzy Controller ===

To improve the response of the rover further, a fuzzy controller is designed to restrict acceleration when the error is high and to speed up the response at lower error values. The error graphs for the x-direction, y-direction and the angular direction were analysed. The system was given a slow start since the error at the beginning is the largest. The velocity of the rover was analysed at various error levels. When the error values decreased, the fuzzy controller switches to a different amplifier value so that the system is sped up.

== Results ==

=== Testing of Control Algorithms ===
The Kp and Kd values in this example were adjusted such that the response had the shortest rise time while avoiding steady state error and overshoot. The response with the overshoot was caused by both high KP and Kd values. The response with the slowest rise time was due to an extremely low Kd value. A balanced Kp and Kd value set reulted in the response with a quick rise time without causing overshoot.
[[File:PD Controller Tuning.jpg|500px|thumb|center|PD Controller Tuning]]

=== Testing of Fuzzy Logic Controller ===
The dips in the following graph are caused by the switching of the fuzzy logic to different amplifier values, as error values decreased instead of a slow approach to the endpoint, the response was sped up with a higher amplifier value of the controller constants.

[[File:Fuzzy Logic Test.jpg|500px|thumb|center|Fuzzy Logic Test]]

=== SLAM and Path Planning ===
[[File:An Agent Based System for Target Search and Delivery System Overview.png|thumb]]

[[File:TargetDetection.jpg|thumb|Targets are successfully detected based on their red colour, and its location in coordinates are generated accurately in the simulation.]]

[[File:EnvironmnetMap.jpg|thumb|This is a map that was generated during a simulation run. Obstacles are accurately detecting using laser range sensors, and their positions in global coordinates are stored in the map as red dots.]]

[[File:RealEnvironment.jpg|thumb|The map depicted in the previous figure represents data collected from the environment depicted here.]]

[[File:Astar.jpg|thumb|The map provides agents with obstacle location information, which they can use to plan paths with an A* Search to avoid collisions.]]

=== Target Detection ===
[[File:RoverDetected.jpg|thumb|Rovers use the camera to detect the location of other rovers in the environment. They are coloured green, which is the feature that is extracted to identify the rover.]]

== References ==
[1] N. Correll, Introduction to autonomous robots, 1st ed. Boulder, Colorado: Magellan Scientific, p. 17.
[2] Sariff, N. and Buniyamin, N., 2020. An Overview Of Autonomous Mobile Robot Path Planning Algorithms - IEEE Conference Publication. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/abstract/document/4339335> [Accessed 15 April 2020].
[3] Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., & Oluwatola, O. (2014). Brief History and Current State of Autonomous Vehicles. In Autonomous Vehicle Technology: A Guide for Policymakers (pp. 55-74). RAND Corporation. Retrieved April 23, 2020, from www.jstor.org/stable/10.7249/j.ctt5hhwgz.11
[4] "Single agent vs multi agent system in AI." Geekboots , Feb 12, 2019, . Retrieved April 23, 2020, From https://www.geekboots.com/story/single-agent-vs-multi-agent-system.
[5] Parikshit H, “Comparison – Centralized, Decentralized and Distributed Systems” GeeksforGeeks, Retrieved April 23, 2020, From https://www.geeksforgeeks.org/comparison-centralized-decentralized-and-distributed-systems/
[6] Cadena, Cesar & Carlone, Luca & Carrillo, Henry & Latif, Yasir & Scaramuzza, Davide & Neira, Jose & Reid, Ian & Leonard, John. (2016). Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age. IEEE Transactions on Robotics. 32. 10.1109/TRO.2016.2624754.
[7] J. Bobrow, S. Dubowsky and J. Gibson, "Time-Optimal Control of Robotic Manipulators Along Specified Paths", The International Journal of Robotics Research, vol. 4, no. 3, pp. 3-17, 1985. Available: 10.1177/027836498500400301.
[8] W. Miller, F. Glanz and L. Kraft, "Application of a General Learning Algorithm to the Control of Robotic Manipulators", The International Journal of Robotics Research, vol. 6, no. 2, pp. 84-98, 1987. Available: 10.1177/027836498700600207.
[9] F. Ribeiro, I. Moutinho, P. Silva, C. Fraga and N. Pereira, "Three omni-directional wheels control on a mobile robot," Control 2004,University of Bath, UK, Sept. 2004.
[10] W. Li, C. Yang, Y. Jiang, X. Liu and C. Su, "Motion Planning for Omnidirectional Wheeled Mobile Robot by Potential Field Method", Journal of Advanced Transportation, vol. 2017, pp. 1-11, 2017. Available: 10.1155/2017/4961383.
[11] "A* Search Algorithm - GeeksforGeeks", GeeksforGeeks, 2020. [Online]. Available: https://www.geeksforgeeks.org/a-search-algorithm/. [Accessed: 19- Oct- 2020].

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:37:05Z

A1707871: /* Testing of Fuzzy Logic Controller */

== Project Students ==
Muyu Wu, Abrar Ali Chowdhury, Zach Vawser

== Project Supervisors ==
Professor Peng Shi, Professor Cheng-Chew Lim

== Project Advisors ==
Xin Yuan, Yuan Sun, Yang Fei, Zhi Lian

== Abstract ==

In this project, we will be using the concepts in artificial intelligence, control theory and signal processing to create a system in which a team of robots work together through a centralised system to search for, detect, and collect target objects from an unknown environment, then deliver those targets to a destination. The aim of this system will be to test the performance of a single agent system against that of a multi agent system.

== Introduction ==
Autonomous systems are making their way into our world at a blistering pace due to their potential to make our lives easier. In their most successful applications to date, robots and automation have increased productivity, lowered production costs, and generated job opportunities in the tech sector. Some of the most famous examples of these systems include:
*Driverless Cars,
*Autonomous Vacuum Cleaners,
*Assembly Line Robotic Arms (pictured below)

At the core of an autonomous system is the agent, something with the ability to perceive its environment and act upon it using effectors. There may be one or more of these in a system, hence single and multi-agent systems. The primary distinction between these types of systems is that multi-agent ones are dynamic, as agents must handle each other as well as the environment.

The following three concepts are critical to autonomous systems and heavily intertwined.
First up is mapping, which is the action of an agent using its perceptions to develop a model of the environment it’s in. Localisation is where the agent uses a-priori knowledge of its environment, called a map, to determine where it is in that environment based on the landmarks it can see. With that brief introduction to agents, mapping and localisation, we now reach one of the most fundamental aspects of autonomous systems: doing both at the same time. This technique is called simultaneous localisation and mapping or SLAM, and is needed when an agent is in an environment it has no or very little a-priori knowledge of. This project also uses Computer Vision for detecting target objects and other rovers, which informs the path planning for the rovers and the arm.

== Motivation ==
Our aim for this project is to compare single agent systems with multi-agent systems in terms of efficiency in completing identical search and delivery tasks. The system we are building is comprised of multiple robots that must cooperate in an unknown environment to find and retrieve objects of interest. There are two types of robots, a manipulator (which has a mounted robotic arm), and a carrier (which has a flat platform mounted on top of it). The manipulator uses its arm to pick up the target object when it is found and load it onto the carrier. The detection of a target triggers cooperation of this form by the robots. As the task is being carried out, a map of the environment is being created using the data from the robot’s sensors. The sensors being used are a laser range detector and a camera.

== Method ==
The method using for this project is using MATLAB and V-rep co-simulating the real life environment.

=== Path Planning ===
The path planning method using in project is A star.
<gallery>
A Star.png |(A* SEARCH ALGORITHM - GEEKSFORGEEKS)[11]
</gallery>
A star path planning will considering the current cost to the next node and the total cost to the final destination.
It will provide the optimal path for agent from current location to the destination.

=== SLAM ===

=== Control of 3DOF Omnidirectional Rover ===

The system uses a three wheel drive omnidirectional rover platform with three degrees of freedom (3DOF). The four basic control algorithms were compared in terms of position control to achieve a fast and accurate response for the system.

<gallery>
Rover Design in VREP.jpg|Rover Design in VREP
Physical Rover.jpg|Physical Rover Platform
</gallery>

=== Fuzzy Controller ===

To improve the response of the rover further, a fuzzy controller is designed to restrict acceleration when the error is high and to speed up the response at lower error values. The error graphs for the x-direction, y-direction and the angular direction were analysed. The system was given a slow start since the error at the beginning is the largest. The velocity of the rover was analysed at various error levels. When the error values decreased, the fuzzy controller switches to a different amplifier value so that the system is sped up.

== Results ==

=== Testing of Control Algorithms ===
The Kp and Kd values in this example were adjusted such that the response had the shortest rise time while avoiding steady state error and overshoot. The response with the overshoot was caused by both high KP and Kd values. The response with the slowest rise time was due to an extremely low Kd value. A balanced Kp and Kd value set reulted in the response with a quick rise time without causing overshoot.
[[File:PD Controller Tuning.jpg|500px|thumb|center|PD Controller Tuning]]

=== Testing of Fuzzy Logic Controller ===
[[File:Fuzzy Logic Test.jpg|500px|thumb|center|Fuzzy Logic Test]]

=== SLAM and Path Planning ===
[[File:An Agent Based System for Target Search and Delivery System Overview.png|thumb]]

[[File:TargetDetection.jpg|thumb|Targets are successfully detected based on their red colour, and its location in coordinates are generated accurately in the simulation.]]

[[File:EnvironmnetMap.jpg|thumb|This is a map that was generated during a simulation run. Obstacles are accurately detecting using laser range sensors, and their positions in global coordinates are stored in the map as red dots.]]

[[File:RealEnvironment.jpg|thumb|The map depicted in the previous figure represents data collected from the environment depicted here.]]

[[File:Astar.jpg|thumb|The map provides agents with obstacle location information, which they can use to plan paths with an A* Search to avoid collisions.]]

=== Target Detection ===
[[File:RoverDetected.jpg|thumb|Rovers use the camera to detect the location of other rovers in the environment. They are coloured green, which is the feature that is extracted to identify the rover.]]

== References ==
[1] N. Correll, Introduction to autonomous robots, 1st ed. Boulder, Colorado: Magellan Scientific, p. 17.
[2] Sariff, N. and Buniyamin, N., 2020. An Overview Of Autonomous Mobile Robot Path Planning Algorithms - IEEE Conference Publication. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/abstract/document/4339335> [Accessed 15 April 2020].
[3] Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., & Oluwatola, O. (2014). Brief History and Current State of Autonomous Vehicles. In Autonomous Vehicle Technology: A Guide for Policymakers (pp. 55-74). RAND Corporation. Retrieved April 23, 2020, from www.jstor.org/stable/10.7249/j.ctt5hhwgz.11
[4] "Single agent vs multi agent system in AI." Geekboots , Feb 12, 2019, . Retrieved April 23, 2020, From https://www.geekboots.com/story/single-agent-vs-multi-agent-system.
[5] Parikshit H, “Comparison – Centralized, Decentralized and Distributed Systems” GeeksforGeeks, Retrieved April 23, 2020, From https://www.geeksforgeeks.org/comparison-centralized-decentralized-and-distributed-systems/
[6] Cadena, Cesar & Carlone, Luca & Carrillo, Henry & Latif, Yasir & Scaramuzza, Davide & Neira, Jose & Reid, Ian & Leonard, John. (2016). Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age. IEEE Transactions on Robotics. 32. 10.1109/TRO.2016.2624754.
[7] J. Bobrow, S. Dubowsky and J. Gibson, "Time-Optimal Control of Robotic Manipulators Along Specified Paths", The International Journal of Robotics Research, vol. 4, no. 3, pp. 3-17, 1985. Available: 10.1177/027836498500400301.
[8] W. Miller, F. Glanz and L. Kraft, "Application of a General Learning Algorithm to the Control of Robotic Manipulators", The International Journal of Robotics Research, vol. 6, no. 2, pp. 84-98, 1987. Available: 10.1177/027836498700600207.
[9] F. Ribeiro, I. Moutinho, P. Silva, C. Fraga and N. Pereira, "Three omni-directional wheels control on a mobile robot," Control 2004,University of Bath, UK, Sept. 2004.
[10] W. Li, C. Yang, Y. Jiang, X. Liu and C. Su, "Motion Planning for Omnidirectional Wheeled Mobile Robot by Potential Field Method", Journal of Advanced Transportation, vol. 2017, pp. 1-11, 2017. Available: 10.1155/2017/4961383.
[11] "A* Search Algorithm - GeeksforGeeks", GeeksforGeeks, 2020. [Online]. Available: https://www.geeksforgeeks.org/a-search-algorithm/. [Accessed: 19- Oct- 2020].

File:Fuzzy Logic Test.jpg

2020-10-19T11:36:18Z

A1707871:

The dips in the graph above is the switching of the amplifier at decreasing error values for a faster response at the end.

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:34:24Z

A1707871: /* Results */

== Project Students ==
Muyu Wu, Abrar Ali Chowdhury, Zach Vawser

== Project Supervisors ==
Professor Peng Shi, Professor Cheng-Chew Lim

== Project Advisors ==
Xin Yuan, Yuan Sun, Yang Fei, Zhi Lian

== Abstract ==

In this project, we will be using the concepts in artificial intelligence, control theory and signal processing to create a system in which a team of robots work together through a centralised system to search for, detect, and collect target objects from an unknown environment, then deliver those targets to a destination. The aim of this system will be to test the performance of a single agent system against that of a multi agent system.

== Introduction ==
Autonomous systems are making their way into our world at a blistering pace due to their potential to make our lives easier. In their most successful applications to date, robots and automation have increased productivity, lowered production costs, and generated job opportunities in the tech sector. Some of the most famous examples of these systems include:
*Driverless Cars,
*Autonomous Vacuum Cleaners,
*Assembly Line Robotic Arms (pictured below)

At the core of an autonomous system is the agent, something with the ability to perceive its environment and act upon it using effectors. There may be one or more of these in a system, hence single and multi-agent systems. The primary distinction between these types of systems is that multi-agent ones are dynamic, as agents must handle each other as well as the environment.

The following three concepts are critical to autonomous systems and heavily intertwined.
First up is mapping, which is the action of an agent using its perceptions to develop a model of the environment it’s in. Localisation is where the agent uses a-priori knowledge of its environment, called a map, to determine where it is in that environment based on the landmarks it can see. With that brief introduction to agents, mapping and localisation, we now reach one of the most fundamental aspects of autonomous systems: doing both at the same time. This technique is called simultaneous localisation and mapping or SLAM, and is needed when an agent is in an environment it has no or very little a-priori knowledge of. This project also uses Computer Vision for detecting target objects and other rovers, which informs the path planning for the rovers and the arm.

== Motivation ==
Our aim for this project is to compare single agent systems with multi-agent systems in terms of efficiency in completing identical search and delivery tasks. The system we are building is comprised of multiple robots that must cooperate in an unknown environment to find and retrieve objects of interest. There are two types of robots, a manipulator (which has a mounted robotic arm), and a carrier (which has a flat platform mounted on top of it). The manipulator uses its arm to pick up the target object when it is found and load it onto the carrier. The detection of a target triggers cooperation of this form by the robots. As the task is being carried out, a map of the environment is being created using the data from the robot’s sensors. The sensors being used are a laser range detector and a camera.

== Method ==
The method using for this project is using MATLAB and V-rep co-simulating the real life environment.

=== Path Planning ===
The path planning method using in project is A star.
<gallery>
A Star.png |(A* SEARCH ALGORITHM - GEEKSFORGEEKS)[11]
</gallery>
A star path planning will considering the current cost to the next node and the total cost to the final destination.
It will provide the optimal path for agent from current location to the destination.

=== SLAM ===

=== Control of 3DOF Omnidirectional Rover ===

The system uses a three wheel drive omnidirectional rover platform with three degrees of freedom (3DOF). The four basic control algorithms were compared in terms of position control to achieve a fast and accurate response for the system.

<gallery>
Rover Design in VREP.jpg|Rover Design in VREP
Physical Rover.jpg|Physical Rover Platform
</gallery>

=== Fuzzy Controller ===

To improve the response of the rover further, a fuzzy controller is designed to restrict acceleration when the error is high and to speed up the response at lower error values. The error graphs for the x-direction, y-direction and the angular direction were analysed. The system was given a slow start since the error at the beginning is the largest. The velocity of the rover was analysed at various error levels. When the error values decreased, the fuzzy controller switches to a different amplifier value so that the system is sped up.

== Results ==

=== Testing of Control Algorithms ===
The Kp and Kd values in this example were adjusted such that the response had the shortest rise time while avoiding steady state error and overshoot. The response with the overshoot was caused by both high KP and Kd values. The response with the slowest rise time was due to an extremely low Kd value. A balanced Kp and Kd value set reulted in the response with a quick rise time without causing overshoot.
[[File:PD Controller Tuning.jpg|500px|thumb|center|PD Controller Tuning]]

=== Testing of Fuzzy Logic Controller ===

=== SLAM and Path Planning ===
[[File:An Agent Based System for Target Search and Delivery System Overview.png|thumb]]

[[File:TargetDetection.jpg|thumb|Targets are successfully detected based on their red colour, and its location in coordinates are generated accurately in the simulation.]]

[[File:EnvironmnetMap.jpg|thumb|This is a map that was generated during a simulation run. Obstacles are accurately detecting using laser range sensors, and their positions in global coordinates are stored in the map as red dots.]]

[[File:RealEnvironment.jpg|thumb|The map depicted in the previous figure represents data collected from the environment depicted here.]]

[[File:Astar.jpg|thumb|The map provides agents with obstacle location information, which they can use to plan paths with an A* Search to avoid collisions.]]

=== Target Detection ===
[[File:RoverDetected.jpg|thumb|Rovers use the camera to detect the location of other rovers in the environment. They are coloured green, which is the feature that is extracted to identify the rover.]]

== References ==
[1] N. Correll, Introduction to autonomous robots, 1st ed. Boulder, Colorado: Magellan Scientific, p. 17.
[2] Sariff, N. and Buniyamin, N., 2020. An Overview Of Autonomous Mobile Robot Path Planning Algorithms - IEEE Conference Publication. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/abstract/document/4339335> [Accessed 15 April 2020].
[3] Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., & Oluwatola, O. (2014). Brief History and Current State of Autonomous Vehicles. In Autonomous Vehicle Technology: A Guide for Policymakers (pp. 55-74). RAND Corporation. Retrieved April 23, 2020, from www.jstor.org/stable/10.7249/j.ctt5hhwgz.11
[4] "Single agent vs multi agent system in AI." Geekboots , Feb 12, 2019, . Retrieved April 23, 2020, From https://www.geekboots.com/story/single-agent-vs-multi-agent-system.
[5] Parikshit H, “Comparison – Centralized, Decentralized and Distributed Systems” GeeksforGeeks, Retrieved April 23, 2020, From https://www.geeksforgeeks.org/comparison-centralized-decentralized-and-distributed-systems/
[6] Cadena, Cesar & Carlone, Luca & Carrillo, Henry & Latif, Yasir & Scaramuzza, Davide & Neira, Jose & Reid, Ian & Leonard, John. (2016). Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age. IEEE Transactions on Robotics. 32. 10.1109/TRO.2016.2624754.
[7] J. Bobrow, S. Dubowsky and J. Gibson, "Time-Optimal Control of Robotic Manipulators Along Specified Paths", The International Journal of Robotics Research, vol. 4, no. 3, pp. 3-17, 1985. Available: 10.1177/027836498500400301.
[8] W. Miller, F. Glanz and L. Kraft, "Application of a General Learning Algorithm to the Control of Robotic Manipulators", The International Journal of Robotics Research, vol. 6, no. 2, pp. 84-98, 1987. Available: 10.1177/027836498700600207.
[9] F. Ribeiro, I. Moutinho, P. Silva, C. Fraga and N. Pereira, "Three omni-directional wheels control on a mobile robot," Control 2004,University of Bath, UK, Sept. 2004.
[10] W. Li, C. Yang, Y. Jiang, X. Liu and C. Su, "Motion Planning for Omnidirectional Wheeled Mobile Robot by Potential Field Method", Journal of Advanced Transportation, vol. 2017, pp. 1-11, 2017. Available: 10.1155/2017/4961383.
[11] "A* Search Algorithm - GeeksforGeeks", GeeksforGeeks, 2020. [Online]. Available: https://www.geeksforgeeks.org/a-search-algorithm/. [Accessed: 19- Oct- 2020].

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:25:37Z

A1707871: /* Fuzzy Controller */

== Project Students ==
Muyu Wu, Abrar Ali Chowdhury, Zach Vawser

== Project Supervisors ==
Professor Peng Shi, Professor Cheng-Chew Lim

== Project Advisors ==
Xin Yuan, Yuan Sun, Yang Fei, Zhi Lian

== Abstract ==

In this project, we will be using the concepts in artificial intelligence, control theory and signal processing to create a system in which a team of robots work together through a centralised system to search for, detect, and collect target objects from an unknown environment, then deliver those targets to a destination. The aim of this system will be to test the performance of a single agent system against that of a multi agent system.

== Introduction ==
Autonomous systems are making their way into our world at a blistering pace due to their potential to make our lives easier. In their most successful applications to date, robots and automation have increased productivity, lowered production costs, and generated job opportunities in the tech sector. Some of the most famous examples of these systems include:
*Driverless Cars,
*Autonomous Vacuum Cleaners,
*Assembly Line Robotic Arms (pictured below)

At the core of an autonomous system is the agent, something with the ability to perceive its environment and act upon it using effectors. There may be one or more of these in a system, hence single and multi-agent systems. The primary distinction between these types of systems is that multi-agent ones are dynamic, as agents must handle each other as well as the environment.

The following three concepts are critical to autonomous systems and heavily intertwined.
First up is mapping, which is the action of an agent using its perceptions to develop a model of the environment it’s in. Localisation is where the agent uses a-priori knowledge of its environment, called a map, to determine where it is in that environment based on the landmarks it can see. With that brief introduction to agents, mapping and localisation, we now reach one of the most fundamental aspects of autonomous systems: doing both at the same time. This technique is called simultaneous localisation and mapping or SLAM, and is needed when an agent is in an environment it has no or very little a-priori knowledge of. This project also uses Computer Vision for detecting target objects and other rovers, which informs the path planning for the rovers and the arm.

== Motivation ==
Our aim for this project is to compare single agent systems with multi-agent systems in terms of efficiency in completing identical search and delivery tasks. The system we are building is comprised of multiple robots that must cooperate in an unknown environment to find and retrieve objects of interest. There are two types of robots, a manipulator (which has a mounted robotic arm), and a carrier (which has a flat platform mounted on top of it). The manipulator uses its arm to pick up the target object when it is found and load it onto the carrier. The detection of a target triggers cooperation of this form by the robots. As the task is being carried out, a map of the environment is being created using the data from the robot’s sensors. The sensors being used are a laser range detector and a camera.

== Method ==
The method using for this project is using MATLAB and V-rep co-simulating the real life environment.

=== Path Planning ===
The path planning method using in project is A star.
<gallery>
A Star.png |(A* SEARCH ALGORITHM - GEEKSFORGEEKS)[11]
</gallery>
A star path planning will considering the current cost to the next node and the total cost to the final destination.
It will provide the optimal path for agent from current location to the destination.

=== SLAM ===

=== Control of 3DOF Omnidirectional Rover ===

The system uses a three wheel drive omnidirectional rover platform with three degrees of freedom (3DOF). The four basic control algorithms were compared in terms of position control to achieve a fast and accurate response for the system.

<gallery>
Rover Design in VREP.jpg|Rover Design in VREP
Physical Rover.jpg|Physical Rover Platform
</gallery>

=== Fuzzy Controller ===

To improve the response of the rover further, a fuzzy controller is designed to restrict acceleration when the error is high and to speed up the response at lower error values. The error graphs for the x-direction, y-direction and the angular direction were analysed. The system was given a slow start since the error at the beginning is the largest. The velocity of the rover was analysed at various error levels. When the error values decreased, the fuzzy controller switches to a different amplifier value so that the system is sped up.

== Results ==

=== Testing of Control Algorithms ===
The Kp and Kd values in this example were adjusted such that the response had the shortest rise time while avoiding steady state error and overshoot. The response with the overshoot was caused by both high KP and Kd values. The response with the slowest rise time was due to an extremely low Kd value. A balanced Kp and Kd value set reulted in the response with a quick rise time without causing overshoot.
[[File:PD Controller Tuning.jpg|500px|thumb|center|PD Controller Tuning]]

=== SLAM and Path Planning ===
[[File:An Agent Based System for Target Search and Delivery System Overview.png|thumb]]

[[File:TargetDetection.jpg|thumb|Targets are successfully detected based on their red colour, and its location in coordinates are generated accurately in the simulation.]]

[[File:EnvironmnetMap.jpg|thumb|This is a map that was generated during a simulation run. Obstacles are accurately detecting using laser range sensors, and their positions in global coordinates are stored in the map as red dots.]]

[[File:RealEnvironment.jpg|thumb|The map depicted in the previous figure represents data collected from the environment depicted here.]]

[[File:Astar.jpg|thumb|The map provides agents with obstacle location information, which they can use to plan paths with an A* Search to avoid collisions.]]

=== Target Detection ===

== References ==
[1] N. Correll, Introduction to autonomous robots, 1st ed. Boulder, Colorado: Magellan Scientific, p. 17.
[2] Sariff, N. and Buniyamin, N., 2020. An Overview Of Autonomous Mobile Robot Path Planning Algorithms - IEEE Conference Publication. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/abstract/document/4339335> [Accessed 15 April 2020].
[3] Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., & Oluwatola, O. (2014). Brief History and Current State of Autonomous Vehicles. In Autonomous Vehicle Technology: A Guide for Policymakers (pp. 55-74). RAND Corporation. Retrieved April 23, 2020, from www.jstor.org/stable/10.7249/j.ctt5hhwgz.11
[4] "Single agent vs multi agent system in AI." Geekboots , Feb 12, 2019, . Retrieved April 23, 2020, From https://www.geekboots.com/story/single-agent-vs-multi-agent-system.
[5] Parikshit H, “Comparison – Centralized, Decentralized and Distributed Systems” GeeksforGeeks, Retrieved April 23, 2020, From https://www.geeksforgeeks.org/comparison-centralized-decentralized-and-distributed-systems/
[6] Cadena, Cesar & Carlone, Luca & Carrillo, Henry & Latif, Yasir & Scaramuzza, Davide & Neira, Jose & Reid, Ian & Leonard, John. (2016). Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age. IEEE Transactions on Robotics. 32. 10.1109/TRO.2016.2624754.
[7] J. Bobrow, S. Dubowsky and J. Gibson, "Time-Optimal Control of Robotic Manipulators Along Specified Paths", The International Journal of Robotics Research, vol. 4, no. 3, pp. 3-17, 1985. Available: 10.1177/027836498500400301.
[8] W. Miller, F. Glanz and L. Kraft, "Application of a General Learning Algorithm to the Control of Robotic Manipulators", The International Journal of Robotics Research, vol. 6, no. 2, pp. 84-98, 1987. Available: 10.1177/027836498700600207.
[9] F. Ribeiro, I. Moutinho, P. Silva, C. Fraga and N. Pereira, "Three omni-directional wheels control on a mobile robot," Control 2004,University of Bath, UK, Sept. 2004.
[10] W. Li, C. Yang, Y. Jiang, X. Liu and C. Su, "Motion Planning for Omnidirectional Wheeled Mobile Robot by Potential Field Method", Journal of Advanced Transportation, vol. 2017, pp. 1-11, 2017. Available: 10.1155/2017/4961383.
[11] "A* Search Algorithm - GeeksforGeeks", GeeksforGeeks, 2020. [Online]. Available: https://www.geeksforgeeks.org/a-search-algorithm/. [Accessed: 19- Oct- 2020].

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:15:42Z

A1707871: /* Testing of Control Algorithms */

== Project Students ==
Muyu Wu, Abrar Ali Chowdhury, Zach Vawser

== Project Supervisors ==
Professor Peng Shi, Professor Cheng-Chew Lim

== Project Advisors ==
Xin Yuan, Yuan Sun, Yang Fei, Zhi Lian

== Abstract ==

In this project, we will be using the concepts in artificial intelligence, control theory and signal processing to create a system in which a team of robots work together through a centralised system to search for, detect, and collect target objects from an unknown environment, then deliver those targets to a destination. The aim of this system will be to test the performance of a single agent system against that of a multi agent system.

== Introduction ==
Autonomous systems are making their way into our world at a blistering pace due to their potential to make our lives easier. In their most successful applications to date, robots and automation have increased productivity, lowered production costs, and generated job opportunities in the tech sector. Some of the most famous examples of these systems include:
*Driverless Cars,
*Autonomous Vacuum Cleaners,
*Assembly Line Robotic Arms (pictured below)

At the core of an autonomous system is the agent, something with the ability to perceive its environment and act upon it using effectors. There may be one or more of these in a system, hence single and multi-agent systems. The primary distinction between these types of systems is that multi-agent ones are dynamic, as agents must handle each other as well as the environment.

The following three concepts are critical to autonomous systems and heavily intertwined.
First up is mapping, which is the action of an agent using its perceptions to develop a model of the environment it’s in. Localisation is where the agent uses a-priori knowledge of its environment, called a map, to determine where it is in that environment based on the landmarks it can see. With that brief introduction to agents, mapping and localisation, we now reach one of the most fundamental aspects of autonomous systems: doing both at the same time. This technique is called simultaneous localisation and mapping or SLAM, and is needed when an agent is in an environment it has no or very little a-priori knowledge of. This project also uses Computer Vision for detecting target objects and other rovers, which informs the path planning for the rovers and the arm.

== Motivation ==
Our aim for this project is to compare single agent systems with multi-agent systems in terms of efficiency in completing identical search and delivery tasks. The system we are building is comprised of multiple robots that must cooperate in an unknown environment to find and retrieve objects of interest. There are two types of robots, a manipulator (which has a mounted robotic arm), and a carrier (which has a flat platform mounted on top of it). The manipulator uses its arm to pick up the target object when it is found and load it onto the carrier. The detection of a target triggers cooperation of this form by the robots. As the task is being carried out, a map of the environment is being created using the data from the robot’s sensors. The sensors being used are a laser range detector and a camera.

== Method ==
The method using for this project is using MATLAB and V-rep co-simulating the real life environment.

=== Path Planning ===
The path planning method using in project is A star.
[[File:A Star Path finding|thumb]]

=== Control of 3DOF Omnidirectional Rover ===

The system uses a three wheel drive omnidirectional rover platform with three degrees of freedom (3DOF). The four basic control algorithms were compared in terms of position control to achieve a fast and accurate response for the system.

<gallery>
Rover Design in VREP.jpg|Rover Design in VREP
Physical Rover.jpg|Physical Rover Platform
</gallery>

=== Fuzzy Controller ===

To improve the response of the rover further, a fuzzy controller is designed to restrict acceleration when the error is high and to speed up the response at lower error values.

== Results ==

=== Testing of Control Algorithms ===
The Kp and Kd values in this example were adjusted such that the response had the shortest rise time while avoiding steady state error and overshoot. The response with the overshoot was caused by both high KP and Kd values. The response with the slowest rise time was due to an extremely low Kd value. A balanced Kp and Kd value set reulted in the response with a quick rise time without causing overshoot.
[[File:PD Controller Tuning.jpg|500px|thumb|center|PD Controller Tuning]]

=== SLAM ===
[[File:An Agent Based System for Target Search and Delivery System Overview.png|thumb]]

[[File:TargetDetection.jpg|thumb|Targets are successfully detected based on their red colour, and its location in coordinates are generated accurately in the simulation.]]

[[File:EnvironmnetMap.jpg|thumb|This is a map that was generated during a simulation run. Obstacles are accurately detecting using laser range sensors, and their positions in global coordinates are stored in the map as red dots.]]

== References ==
[1] N. Correll, Introduction to autonomous robots, 1st ed. Boulder, Colorado: Magellan Scientific, p. 17.
[2] Sariff, N. and Buniyamin, N., 2020. An Overview Of Autonomous Mobile Robot Path Planning Algorithms - IEEE Conference Publication. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/abstract/document/4339335> [Accessed 15 April 2020].
[3] Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., & Oluwatola, O. (2014). Brief History and Current State of Autonomous Vehicles. In Autonomous Vehicle Technology: A Guide for Policymakers (pp. 55-74). RAND Corporation. Retrieved April 23, 2020, from www.jstor.org/stable/10.7249/j.ctt5hhwgz.11
[4] "Single agent vs multi agent system in AI." Geekboots , Feb 12, 2019, . Retrieved April 23, 2020, From https://www.geekboots.com/story/single-agent-vs-multi-agent-system.
[5] Parikshit H, “Comparison – Centralized, Decentralized and Distributed Systems” GeeksforGeeks, Retrieved April 23, 2020, From https://www.geeksforgeeks.org/comparison-centralized-decentralized-and-distributed-systems/
[6] Cadena, Cesar & Carlone, Luca & Carrillo, Henry & Latif, Yasir & Scaramuzza, Davide & Neira, Jose & Reid, Ian & Leonard, John. (2016). Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age. IEEE Transactions on Robotics. 32. 10.1109/TRO.2016.2624754.
[7] J. Bobrow, S. Dubowsky and J. Gibson, "Time-Optimal Control of Robotic Manipulators Along Specified Paths", The International Journal of Robotics Research, vol. 4, no. 3, pp. 3-17, 1985. Available: 10.1177/027836498500400301.
[8] W. Miller, F. Glanz and L. Kraft, "Application of a General Learning Algorithm to the Control of Robotic Manipulators", The International Journal of Robotics Research, vol. 6, no. 2, pp. 84-98, 1987. Available: 10.1177/027836498700600207.
[9] F. Ribeiro, I. Moutinho, P. Silva, C. Fraga and N. Pereira, "Three omni-directional wheels control on a mobile robot," Control 2004,University of Bath, UK, Sept. 2004.
[10] W. Li, C. Yang, Y. Jiang, X. Liu and C. Su, "Motion Planning for Omnidirectional Wheeled Mobile Robot by Potential Field Method", Journal of Advanced Transportation, vol. 2017, pp. 1-11, 2017. Available: 10.1155/2017/4961383.

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:09:57Z

A1707871: /* Testing of Control Algorithms */

== Project Students ==
Muyu Wu, Abrar Ali Chowdhury, Zach Vawser

== Project Supervisors ==
Professor Peng Shi, Professor Cheng-Chew Lim

== Project Advisors ==
Xin Yuan, Yuan Sun, Yang Fei, Zhi Lian

== Abstract ==

In this project, we will be using the concepts in artificial intelligence, control theory and signal processing to create a system in which a team of robots work together through a centralised system to search for, detect, and collect target objects from an unknown environment, then deliver those targets to a destination. The aim of this system will be to test the performance of a single agent system against that of a multi agent system.

== Introduction ==
Autonomous systems are making their way into our world at a blistering pace due to their potential to make our lives easier. In their most successful applications to date, robots and automation have increased productivity, lowered production costs, and generated job opportunities in the tech sector. Some of the most famous examples of these systems include:
*Driverless Cars,
*Autonomous Vacuum Cleaners,
*Assembly Line Robotic Arms (pictured below)

At the core of an autonomous system is the agent, something with the ability to perceive its environment and act upon it using effectors. There may be one or more of these in a system, hence single and multi-agent systems. The primary distinction between these types of systems is that multi-agent ones are dynamic, as agents must handle each other as well as the environment.

The following three concepts are critical to autonomous systems and heavily intertwined.
First up is mapping, which is the action of an agent using its perceptions to develop a model of the environment it’s in. Localisation is where the agent uses a-priori knowledge of its environment, called a map, to determine where it is in that environment based on the landmarks it can see. With that brief introduction to agents, mapping and localisation, we now reach one of the most fundamental aspects of autonomous systems: doing both at the same time. This technique is called simultaneous localisation and mapping or SLAM, and is needed when an agent is in an environment it has no or very little a-priori knowledge of. This project also uses Computer Vision for detecting target objects and other rovers, which informs the path planning for the rovers and the arm.

== Motivation ==
Our aim for this project is to compare single agent systems with multi-agent systems in terms of efficiency in completing identical search and delivery tasks. The system we are building is comprised of multiple robots that must cooperate in an unknown environment to find and retrieve objects of interest. There are two types of robots, a manipulator (which has a mounted robotic arm), and a carrier (which has a flat platform mounted on top of it). The manipulator uses its arm to pick up the target object when it is found and load it onto the carrier. The detection of a target triggers cooperation of this form by the robots. As the task is being carried out, a map of the environment is being created using the data from the robot’s sensors. The sensors being used are a laser range detector and a camera.

== Method ==
The method using for this project is using MATLAB and V-rep co-simulating the real life environment.

=== Path Planning ===
The path planning method using in project is A star.
[[File:A Star Path finding|thumb]]

=== Control of 3DOF Omnidirectional Rover ===

The system uses a three wheel drive omnidirectional rover platform with three degrees of freedom (3DOF). The four basic control algorithms were compared in terms of position control to achieve a fast and accurate response for the system.

<gallery>
Rover Design in VREP.jpg|Rover Design in VREP
Physical Rover.jpg|Physical Rover Platform
</gallery>

=== Fuzzy Controller ===

To improve the response of the rover further, a fuzzy controller is designed to restrict acceleration when the error is high and to speed up the response at lower error values.

== Results ==

=== Testing of Control Algorithms ===

[[File:PD Controller Tuning.jpg|500px|thumb|center|PD Controller Tuning]]

=== SLAM ===
[[File:An Agent Based System for Target Search and Delivery System Overview.png|thumb]]

[[File:TargetDetection.jpg|thumb|Targets are successfully detected based on their red colour, and its location in coordinates are generated accurately in the simulation.]]

[[File:EnvironmnetMap.jpg|thumb|This is a map that was generated during a simulation run. Obstacles are accurately detecting using laser range sensors, and their positions in global coordinates are stored in the map as red dots.]]

== References ==
[1] N. Correll, Introduction to autonomous robots, 1st ed. Boulder, Colorado: Magellan Scientific, p. 17.
[2] Sariff, N. and Buniyamin, N., 2020. An Overview Of Autonomous Mobile Robot Path Planning Algorithms - IEEE Conference Publication. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/abstract/document/4339335> [Accessed 15 April 2020].
[3] Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., & Oluwatola, O. (2014). Brief History and Current State of Autonomous Vehicles. In Autonomous Vehicle Technology: A Guide for Policymakers (pp. 55-74). RAND Corporation. Retrieved April 23, 2020, from www.jstor.org/stable/10.7249/j.ctt5hhwgz.11
[4] "Single agent vs multi agent system in AI." Geekboots , Feb 12, 2019, . Retrieved April 23, 2020, From https://www.geekboots.com/story/single-agent-vs-multi-agent-system.
[5] Parikshit H, “Comparison – Centralized, Decentralized and Distributed Systems” GeeksforGeeks, Retrieved April 23, 2020, From https://www.geeksforgeeks.org/comparison-centralized-decentralized-and-distributed-systems/
[6] Cadena, Cesar & Carlone, Luca & Carrillo, Henry & Latif, Yasir & Scaramuzza, Davide & Neira, Jose & Reid, Ian & Leonard, John. (2016). Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age. IEEE Transactions on Robotics. 32. 10.1109/TRO.2016.2624754.
[7] J. Bobrow, S. Dubowsky and J. Gibson, "Time-Optimal Control of Robotic Manipulators Along Specified Paths", The International Journal of Robotics Research, vol. 4, no. 3, pp. 3-17, 1985. Available: 10.1177/027836498500400301.
[8] W. Miller, F. Glanz and L. Kraft, "Application of a General Learning Algorithm to the Control of Robotic Manipulators", The International Journal of Robotics Research, vol. 6, no. 2, pp. 84-98, 1987. Available: 10.1177/027836498700600207.
[9] F. Ribeiro, I. Moutinho, P. Silva, C. Fraga and N. Pereira, "Three omni-directional wheels control on a mobile robot," Control 2004,University of Bath, UK, Sept. 2004.
[10] W. Li, C. Yang, Y. Jiang, X. Liu and C. Su, "Motion Planning for Omnidirectional Wheeled Mobile Robot by Potential Field Method", Journal of Advanced Transportation, vol. 2017, pp. 1-11, 2017. Available: 10.1155/2017/4961383.

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:09:43Z

A1707871: /* Testing of Control Algorithms */

== Project Students ==
Muyu Wu, Abrar Ali Chowdhury, Zach Vawser

== Project Supervisors ==
Professor Peng Shi, Professor Cheng-Chew Lim

== Project Advisors ==
Xin Yuan, Yuan Sun, Yang Fei, Zhi Lian

== Abstract ==

In this project, we will be using the concepts in artificial intelligence, control theory and signal processing to create a system in which a team of robots work together through a centralised system to search for, detect, and collect target objects from an unknown environment, then deliver those targets to a destination. The aim of this system will be to test the performance of a single agent system against that of a multi agent system.

== Introduction ==
Autonomous systems are making their way into our world at a blistering pace due to their potential to make our lives easier. In their most successful applications to date, robots and automation have increased productivity, lowered production costs, and generated job opportunities in the tech sector. Some of the most famous examples of these systems include:
*Driverless Cars,
*Autonomous Vacuum Cleaners,
*Assembly Line Robotic Arms (pictured below)

At the core of an autonomous system is the agent, something with the ability to perceive its environment and act upon it using effectors. There may be one or more of these in a system, hence single and multi-agent systems. The primary distinction between these types of systems is that multi-agent ones are dynamic, as agents must handle each other as well as the environment.

The following three concepts are critical to autonomous systems and heavily intertwined.
First up is mapping, which is the action of an agent using its perceptions to develop a model of the environment it’s in. Localisation is where the agent uses a-priori knowledge of its environment, called a map, to determine where it is in that environment based on the landmarks it can see. With that brief introduction to agents, mapping and localisation, we now reach one of the most fundamental aspects of autonomous systems: doing both at the same time. This technique is called simultaneous localisation and mapping or SLAM, and is needed when an agent is in an environment it has no or very little a-priori knowledge of. This project also uses Computer Vision for detecting target objects and other rovers, which informs the path planning for the rovers and the arm.

== Motivation ==
Our aim for this project is to compare single agent systems with multi-agent systems in terms of efficiency in completing identical search and delivery tasks. The system we are building is comprised of multiple robots that must cooperate in an unknown environment to find and retrieve objects of interest. There are two types of robots, a manipulator (which has a mounted robotic arm), and a carrier (which has a flat platform mounted on top of it). The manipulator uses its arm to pick up the target object when it is found and load it onto the carrier. The detection of a target triggers cooperation of this form by the robots. As the task is being carried out, a map of the environment is being created using the data from the robot’s sensors. The sensors being used are a laser range detector and a camera.

== Method ==
The method using for this project is using MATLAB and V-rep co-simulating the real life environment.

=== Path Planning ===
The path planning method using in project is A star.
[[File:A Star Path finding|thumb]]

=== Control of 3DOF Omnidirectional Rover ===

The system uses a three wheel drive omnidirectional rover platform with three degrees of freedom (3DOF). The four basic control algorithms were compared in terms of position control to achieve a fast and accurate response for the system.

<gallery>
Rover Design in VREP.jpg|Rover Design in VREP
Physical Rover.jpg|Physical Rover Platform
</gallery>

=== Fuzzy Controller ===

To improve the response of the rover further, a fuzzy controller is designed to restrict acceleration when the error is high and to speed up the response at lower error values.

== Results ==

=== Testing of Control Algorithms ===

[[File:PD Controller Tuning.jpg|100px|thumb|center|PD Controller Tuning]]

=== SLAM ===
[[File:An Agent Based System for Target Search and Delivery System Overview.png|thumb]]

[[File:TargetDetection.jpg|thumb|Targets are successfully detected based on their red colour, and its location in coordinates are generated accurately in the simulation.]]

[[File:EnvironmnetMap.jpg|thumb|This is a map that was generated during a simulation run. Obstacles are accurately detecting using laser range sensors, and their positions in global coordinates are stored in the map as red dots.]]

== References ==
[1] N. Correll, Introduction to autonomous robots, 1st ed. Boulder, Colorado: Magellan Scientific, p. 17.
[2] Sariff, N. and Buniyamin, N., 2020. An Overview Of Autonomous Mobile Robot Path Planning Algorithms - IEEE Conference Publication. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/abstract/document/4339335> [Accessed 15 April 2020].
[3] Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., & Oluwatola, O. (2014). Brief History and Current State of Autonomous Vehicles. In Autonomous Vehicle Technology: A Guide for Policymakers (pp. 55-74). RAND Corporation. Retrieved April 23, 2020, from www.jstor.org/stable/10.7249/j.ctt5hhwgz.11
[4] "Single agent vs multi agent system in AI." Geekboots , Feb 12, 2019, . Retrieved April 23, 2020, From https://www.geekboots.com/story/single-agent-vs-multi-agent-system.
[5] Parikshit H, “Comparison – Centralized, Decentralized and Distributed Systems” GeeksforGeeks, Retrieved April 23, 2020, From https://www.geeksforgeeks.org/comparison-centralized-decentralized-and-distributed-systems/
[6] Cadena, Cesar & Carlone, Luca & Carrillo, Henry & Latif, Yasir & Scaramuzza, Davide & Neira, Jose & Reid, Ian & Leonard, John. (2016). Simultaneous Localization And Mapping: Present, Future, and the Robust-Perception Age. IEEE Transactions on Robotics. 32. 10.1109/TRO.2016.2624754.
[7] J. Bobrow, S. Dubowsky and J. Gibson, "Time-Optimal Control of Robotic Manipulators Along Specified Paths", The International Journal of Robotics Research, vol. 4, no. 3, pp. 3-17, 1985. Available: 10.1177/027836498500400301.
[8] W. Miller, F. Glanz and L. Kraft, "Application of a General Learning Algorithm to the Control of Robotic Manipulators", The International Journal of Robotics Research, vol. 6, no. 2, pp. 84-98, 1987. Available: 10.1177/027836498700600207.
[9] F. Ribeiro, I. Moutinho, P. Silva, C. Fraga and N. Pereira, "Three omni-directional wheels control on a mobile robot," Control 2004,University of Bath, UK, Sept. 2004.
[10] W. Li, C. Yang, Y. Jiang, X. Liu and C. Su, "Motion Planning for Omnidirectional Wheeled Mobile Robot by Potential Field Method", Journal of Advanced Transportation, vol. 2017, pp. 1-11, 2017. Available: 10.1155/2017/4961383.

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:09:24Z

A1707871: /* Testing of Control Algorithms */

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:06:13Z

A1707871: /* Testing of Control Algorithms */

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:06:00Z

A1707871: /* Testing of Control Algorithms */

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:05:22Z

A1707871: /* Testing of Control Algorithms */

File:PD Controller Tuning.jpg

2020-10-19T11:04:34Z

A1707871:

Responses compared with various Kp and Kd values for the PD controller

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T11:00:19Z

A1707871: /* Testing of Control Algorithms */

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T10:57:57Z

A1707871: /* Results */

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T10:51:57Z

A1707871: /* Control of 3WD Omnidirectional Rover */

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T10:42:43Z

A1707871: /* Control of 3WD Omnidirectional Rover */

File:Physical Rover.jpg

2020-10-19T10:42:02Z

A1707871:

Following is a picture of the rover platform with an attached manipulator.

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T10:39:55Z

A1707871: /* Control of 3WD Omnidirectional Rover */

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T10:39:12Z

A1707871: /* Control of 3WD Omnidirectional Rover */

File:Rover Design in VREP.jpg

2020-10-19T10:35:39Z

A1707871:

This is a picture of the rover with the manipulator attached to it. In front of it is a target that is to be picked up. The simulation emulates a pick up process that attaches the target onto the manipulator.

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-10-19T10:30:29Z

A1707871: /* Method */

Projects:2020s1-2330 An Agent-based System for Target Searching and Delivering

2020-04-23T14:07:17Z

A1707871: