Projects:2014s2-75 Formation Control of Two Autonomous Smart Cars

Aim

The aim of this project is to build a model of two smart cars which can move independently. The smart cars will not be controlled by any human intervention for its movement. The two cars should have autonomous control and be able to recognize the destination and should move to the destination in a definite path. There are two major modes in which the cars should move. The control mode and the signal relay mode. In control mode, the cars will move in a chase dodge model. That is one car chases the other one in a definite speed. The other car recognizes the chase and tries to avoid the first car. At a particular distance between them, the cars change their behavior and the dodger turns back and pursues the chaser. The chaser from the first scenario, turns away in order to evade the other car. In signal relay mode, the movements of both the cars are integrated together to send a signal from a source to destination. Car 1 carries a signal or information to a particular location. Car 2 moves from its initial position to the same location. Car 1 supplies the data to Car 2 and Car 2 carries the information from the location of the first car to the final destination.

Background and Significance

Over the years, driving has become one of the biggest life threatening risks. The rapid increase in the number of vehicles has proportionally increased the number of accidents. Studies show that about 1.24 million deaths occur due to traffic accidents in a year across the globe. Carelessness, drink driving and speeding are the major reasons for the cause of traffic accidents. Despite the safety advancements such as abs, airbags, anti-collision systems cars prove to be more dangerous than any other modes of transport such as buses, flight or trains. Apart from creating accidents, driving has additional disadvantages such as increase in stress and fatigue. Cases have been reported even of mental illness caused due to long distances and congested traffic driving. Autonomous cars could prove to be a solution for this situation. If a sophisticated system can be built, the smart cars can decrease the accidents due to human errors. Also, passengers can relax without the agony of driving. There has been significant improvement in the studies of smart cars in the recent past. Google’s autonomous smart car project has gone on to test the smart cars in real environment. GPS is an important factor in this autonomous smart car implementation. The GPS should not only provide the location and coordinates the car is driving but should provide detailed information about the environment which the car is driven in, such as color of traffic lights, curb width, height of the bump etc. Google uses its maps and satellite images for finding the path and sensors to find the inertia and wheel encoders for calculating speed. Due to constraints in finding the color of the traffic lights due to glare or rain, difficulties in detailed mapping of the whole world and in decision making smart car technology is still in its development stages. Intelligent Car is a branch of intelligent robot; it is a system which include automatic control, artificial intelligent, mechanical engineering, and image processing and computer sciences. The main difficulty in intelligent car is image processing. The accuracy of the image processing directly impact intelligent car’s driving directions, driving speed and the ability to dodge obstacles. The technique of moving target’s detecting and tracking are the main parts of image processing The image processing techniques and software have improved due to the introduction of advanced software, improved processing capabilities, digital image processing techniques and the improvement in hardware. Several methods have been introduced for processing the images and finding moving targets. The images we view are in RGB. These images are converted to HSV for processing. The cam shift algorithm, pixel processing, background subtraction, Gaussian distribution and noise elimination are some of the modern techniques used in image processing.

Motivation

This project deals with some of the ways in which the design of autonomous movements of the car can be managed. The robotic technology and artificial intelligence systems proposed in this project can be used in robotics and traffic advancements. The project gives hands on experience in dealing with the Arduino robotic technology which is a starting step for intense robotic technologies. Traffic signalling and camera systems use significant amount of image processing in the near past. The image processing used in the project will be a guideline for detecting moving target and controlling them. The project gives a guideline for using image processing technology for finding the target. The technology can be used in machine vision and medical imaging. With the use of accurate systems such as gyroscope in the future, the project can be used in remotely controlling the robots to reach and investigate inaccessible and congested areas.

Requirements

The project requirements can be classified in to hardware design and implementation and software application. The major requirements of the project are: -

• Research and design a hardware system which can serve as the model of two smart cars and a control system which is to be used as the nerve center of the process

• The hardware design should consist of a model of two smart cars which must move independently

• Design and implementation of a core algorithm to perform the chase dodge model

• Implementation of tracking system in order to track the locations of the car in real time

• Implementation of a communication system for the cars to recognize the locations from the tracking system and implementation of a drive and power system for the cars so that the cars follow the algorithm for the movements.

Conclusion

The aims of the project were completed successfully. The project was able to find a solution for designing the smart cars. The use of camera and image processing is proposed as the output from the project. The control mode in which the cars can move independently and the signal relay mode in which the cars share the tasks were successfully completed. The proposed Bluetooth triangulation method was replaced with the camera due the infeasibility in finding the range using Bluetooth triangulation. The failure of compass as a direction sensor during magnetic interferences affected the project. But the project realised that modern equipment like gyroscope can replace the compass technology. The communication part was proposed to be using Bluetooth but was replaced with Wi-Fi technology due to its increased bandwidth and also less interference.

Future Work

In future the compass module and image processing can be replaced with gyroscopes. The gyroscope is a machine which can find the direction even in the absence of earth’s magnetism and interference from other magnetic equipment. The project can be improved using multiple robots and tracking them. The features such as LIDAR technology which uses laser to find out the external environment, SONAR which sends sound waves to find out the distance and behaviour of the system, RADAR, and IR sensors can be added for increased accuracy and feedback. The image processing used in the project can be improved for high speed measurements when the vehicles move faster. A local vision system in which the camera is place above each car can track the environment.

Other applications

Apart from the system of smart cars, the project can be extended in the mode of robotic technology. The robot can be controlled remotely to access congested areas and track other targets. The image processing technology used in the project can be extended to create technologies for image processing in traffic signals and for facial recognition.