Multi-robot learning with particle swarm optimization

We apply an adapted version of Particle Swarm Optimization to distributed unsupervised robotic learning in groups of robots with only local information. The performance of the learning technique for a simple task is compared across robot groups of various sizes, with the maximum group size allowing each robot to individually contain and manage a single PSO particle. Different PSO neighborhoods based on limitations of real robotic communication are tested in this scenario, and the effect of varying communication power is explored. The algorithms are then applied to a group learning scenario to explore their susceptibility to the credit assignment problem. Results are discussed and future work is proposed.

[1]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[2]  P. Fourie,et al.  The particle swarm optimization algorithm in size and shape optimization , 2002 .

[3]  Peter Stone,et al.  Layered Learning in Multiagent Systems , 1997, AAAI/IAAI.

[4]  Yizhen Zhang,et al.  EVOLVING ENGINEERING DESIGN TRADE-OFFS , 2003 .

[5]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[6]  Maja J. Matarić,et al.  Leaning to behave socially , 1994 .

[7]  J. Kennedy,et al.  Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[8]  Francesco Mondada,et al.  Evolution of homing navigation in a real mobile robot , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Rodney M. Goodman,et al.  Swarm robotic odor localization , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

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

[11]  Maja J. Mataric,et al.  Learning in behavior-based multi-robot systems: policies, models, and other agents , 2001, Cognitive Systems Research.

[12]  R. Arkin,et al.  Behavioral diversity in learning robot teams , 1998 .

[13]  Dave Cliff,et al.  Challenges in evolving controllers for physical robots , 1996, Robotics Auton. Syst..

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

[15]  Ian Darrell Kelly,et al.  Faster learning of control parameters through sharing experiences of autonomous mobile robots , 1998, Int. J. Syst. Sci..

[16]  Dario Floreano,et al.  Learning in Multi-Robot Scenarios through Physically Embedded Genetic Algorithms , 2002 .

[17]  Olivier Michel,et al.  Cyberbotics Ltd. Webots™: Professional Mobile Robot Simulation , 2004 .

[18]  M. Matarić.  Learning to Behave Socially , 1994 .

[19]  Olivier Michel,et al.  Cyberbotics Ltd. Webots™: Professional Mobile Robot Simulation , 2004, ArXiv.

[20]  Yizhen Zhang,et al.  Particle swarm optimization for unsupervised robotic learning , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[21]  Francesco Mondada,et al.  Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms , 1993, ISER.

[22]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[23]  Alcherio Martinoli,et al.  Relative localization and communication module for small-scale multi-robot systems , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..