Coordinative behavior in evolutionary multi-agent system by genetic algorithm

A strategy for motion planning of multiple robots as a multi-agent system is presented. The system has a decentralized configuration. All the robots cannot communicate globally at a time, but some robots can communicate locally and coordinate to avoid competition for a public resource. In such a system, it is difficult for each robot to plan its motion effectively while considering other robots, because the robots cannot predict motions of other robots as an unknown environment. Therefore, each robot only determines its motion selfishly for itself while considering a known environment. In the proposed approach, each robot plans its motion while considering the known environment and using empirical knowledge. The robot considers its unknown environment including other robots in the empirical knowledge. The genetic algorithm is applied to optimization of motion planning of each robot. Through iterations, each robot acquires knowledge empirically, using fuzzy logic. Path planning of multiple mobile robots is discussed, and simulations are performed.<<ETX>>

[1]  Nils J. Nilsson,et al.  A mobius automation: an application of artificial intelligence techniques , 1969, IJCAI 1969.

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  Rodney A. Brooks,et al.  Solving the Find-Path Problem by Good Representation of Free Space , 1983, Autonomous Robot Vehicles.

[4]  Li-Chen Fu,et al.  An efficient algorithm for finding a collision-free path among polyhedral obstacles , 1990, J. Field Robotics.

[5]  Jing Wang,et al.  Distributed computing problems in cellular robotic systems , 1990, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications.

[6]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[7]  S. Yuta,et al.  Consideration on cooperation of multiple autonomous mobile robots-introduction to modest cooperation , 1991, Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments.

[8]  Sukhan Lee,et al.  Cellular robotic collision-free path planning , 1991, Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments.

[9]  Jing Wang.  Fully distributed traffic control strategies for many-AGV systems , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[10]  Fumihito Arai,et al.  Control strategy for a network of cellular robots-determination of a master cell for cellular robotic network based on a potential energy , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[11]  Kazuhiro Kosuge,et al.  Selfish and coordinative planning for multiple mobile robots by genetic algorithm , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[12]  Kazuhiro Kosuge,et al.  New strategy for hierarchical intelligent control of robotic manipulator-hybrid neuromorphic and symbolic control , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[13]  Tomás Lozano-Pérez,et al.  An algorithm for planning collision-free paths among polyhedral obstacles , 1979, CACM.

[14]  Hajime Asama,et al.  Efficient method to generate collision free paths for an autonomous mobile robot based on new free space structuring approach , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[15]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .