Abstract
In a planetary rover system called “SMC rover”, the motion coordination between robots is a key problem to be solved. Multiagent reinforcement learning methods for multirobot coordination strategy learning are investigated. A reinforcement learning based coordination mechanism is proposed for the exploration system. Four-robot climbing a slope is studied in detail as an instance. The actions of the robots are divided into two layers and realized respectively, which simplified the complexity of the climbing task. A Q-Learning based multirobot coordination strategy mechanism is proposed for the climbing mission. An OpenGL 3D simulation platform is used to verify the strategy and the learning results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dias, M.B., Stentz, A.: A market approach to multirobot coordination. Technical Report CMU-RI-TR-01-26, Robotics Institute, Carnegie Mellon University (2001)
Yang, E., Gu, D.: Multiagent Reinforcement Learning for Multi-Robot Systems: A Survey. Technical Report CSM–404, Department of Computer Science, University of Essex (2004)
Kawakami, A., Torii, A., Motomura, K.: SMC Rover: Planetary Rover with Transformable wheels. In: Siciliano, B. (ed.) Experimental Robotics VIII. Advanced Robotics Series, pp. 498–506. Springer, Heidelberg (2003)
Damoto, R., Kawakami, A., Hirose, S.: Study of super-mechano colony: concept and basic experimental set-up. Advanced Robotics 15(4), 391–408 (2001)
Shoham, Y., Powers, R., Grenager, T.: Multi-agent reinforcement learning: a critical survey. Technical report, Stanford University (2003)
Liu, J., Jin, X., Zhang, S.: Autonomous Agents and Multi-Agent Systems: Explorations in Learning. In: Self-Organization and Adaptive Computation, November 2003, Tsinghua Univer-sity Press (2003)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An introduction. MIT Press, Cambridge, MA (1998)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: A survey. Journal of Artificial Intelligence Research (1996)
Mitchell, T.M.: Machine Learning. China Machine Press (March 2003)
Tan, M.: Multi-agent reinforcement learning: Independent vs. cooperative agents. In: Pro-ceedings of the Tenth International Conference on Machine Learning, pp. 330–337 (1993)
Zhang, Z., Ma, S.G., Li, B., Zhang, L.P., Gang, B.G.: OpenGL Based Experimental Plat-form for Simulation of Reconfigurable Planetary Robot System. Journal of System Simula-tion 17(4), 885–888 (2005)
Zhang, Z., Ma, S.G., Li, B., Zhang, L.P., Cao, B.G.: Communication Mechanism Study of a Planetary Exploration Multi-Robot System. Robot 28(3), 309–315 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zheng, Z., Shu-gen, M., Bing-gang, C., Li-ping, Z., Bin, L. (2006). Multiagent Reinforcement Learning for a Planetary Exploration Multirobot System. In: Shi, ZZ., Sadananda, R. (eds) Agent Computing and Multi-Agent Systems. PRIMA 2006. Lecture Notes in Computer Science(), vol 4088. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11802372_33
Download citation
DOI: https://doi.org/10.1007/11802372_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36707-9
Online ISBN: 978-3-540-36860-1
eBook Packages: Computer ScienceComputer Science (R0)