Decentralized fuzzy control of multiple nonholonomic vehicles

This work considers the problem of controlling multiple nonholonomic vehicles so that they converge to a scent source without colliding with each other. Since the control is to be implemented on a simple 8-bit microcontroller, fuzzy control rules are used to simplify a linear quadratic regulator control design. The inputs to the fuzzy controllers for each vehicle are the noisy direction to the source, the distance to the closest neighbor vehicle, and the direction to the closest vehicle. These directions are discretized into four values: forward, behind, left, and right; and the distance into three values: near, far, and gone. The values of the control at these discrete values are obtained based on the collision-avoidance repulsive forces and an attractive force towards the goal. A fuzzy inference system is used to obtain control values for inputs between the small number of discrete input values. Simulation results are provided which demonstrate that the fuzzy control law performs well compared to the exact controller. In fact, the fuzzy controller demonstrates improved robustness to noise.

[1]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[2]  Tamio Arai,et al.  Distributed and autonomous control method for generating shape of multiple mobile robot group , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[3]  Toshio Fukuda,et al.  Fusion of fuzzy, NN, GA to the intelligent robotics , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[4]  Hong Zhang,et al.  Collective Robotics: From Social Insects to Robots , 1993, Adapt. Behav..

[5]  Mohan M. Trivedi,et al.  Motion control of cooperative robotic teams through visual observation and fuzzy logic control , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[6]  Claude Samson,et al.  Robot Control: The Task Function Approach , 1991 .

[7]  Byung Hak Cho,et al.  Design of stability-guaranteed fuzzy logic controller for nuclear steam generators , 1996 .

[8]  Hajime Asama Distributed Autonomous Robotic System Configurated with Multiple Agents and Its Cooperative Behaviors , 1992, J. Robotics Mechatronics.

[9]  Rodney A. Brooks,et al.  Fast, Cheap and Out of Control: a Robot Invasion of the Solar System , 1989 .

[10]  Joo Gon Kim,et al.  An auto-tuning fuzzy rule-based visual servoing algorithm for a slave arm , 1995, Proceedings of Tenth International Symposium on Intelligent Control.

[11]  Ronald C. Arkin,et al.  Cooperation without communication: Multiagent schema-based robot navigation , 1992, J. Field Robotics.

[12]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[13]  Fabrice R. Noreils,et al.  Toward a Robot Architecture Integrating Cooperation between Mobile Robots: Application to Indoor Environment , 1993, Int. J. Robotics Res..

[14]  Isao Hayashi,et al.  A learning method of fuzzy inference rules by descent method , 1992 .

[15]  Yoichiro Maeda,et al.  Behavior-decision fuzzy algorithm for autonomous mobile robots , 1993, Inf. Sci..

[16]  Makoto Ohki,et al.  Self-Tuning of Fuzzy Reasoning by the Steepest Descent Method and Its Application to a Parallel Parking , 1996 .

[17]  Carme Torras Robot control , 1998 .

[18]  Eiichi Yoshida,et al.  Effect of grouping in local communication system of multiple mobile robots , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[19]  J. Y. S. Luh,et al.  Coordination and control of a group of small mobile robots , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.