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.
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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)
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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
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DOI: https://doi.org/10.1007/11802372_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36707-9
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