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Reactive fuzzy controller design by Q-learning for mobile robot navigation 被引量：5

Reactive fuzzy controller design by Q-learning for mobile robot navigation
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摘要 In this paper a learning mechanism for reactive fuzzy controller design of a mobile robot navigating in unknown environments is proposed. The fuzzy logical controller is constructed based on the kinematics model of a real robot. The approach to learning the fuzzy rule base by relatively simple and less computational Q-learning is described in detail. After analyzing the credit assignment problem caused by the rules collision, a remedy is presented. Furthermore, time-varying parameters are used to increase the learning speed. Simulation results prove the mechanism can learn fuzzy navigation rules successfully only using scalar reinforcement signal and the rule base learned is proved to be correct and feasible on real robot platforms. In this paper a learning mechanism for reactive fuzzy controller design of a mobile robot navigating in unknown environments is proposed. The fuzzy logical controller is constructed based on the kinematics model of a real robot. The approach to learning the fuzzy rule base by relatively simple and less computational Q-learning is described in detail. After analyzing the credit assignment problem caused by the rules collision, a remedy is presented. Furthermore, time-varying parameters are used to increase the learning speed. Simulation results prove the mechanism can learn fuzzy navigation rules successfully only using scalar reinforcement signal and the rule base learned is proved to be correct and feasible on real robot platforms.
作者 张文志 吕恬生
机构地区 Robot Institute Shanghai Jiaotong University
出处 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第3期319-324,共6页 哈尔滨工业大学学报（英文版）
关键词 模糊逻辑 模糊控制 机器人运动 知识系统 fuzzy logical reinforcement learning Q-learning mobile robot navigation
分类号 TP242 [自动化与计算机技术—检测技术与自动化装置]
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参考文献10

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同被引文献49

  • 1Amanda LAMPTON,John VALASEK,Mrinal KUMAR.Multiresolution state-space discretization for Q-Learning with pseudorandomized discretization[J].控制理论与应用（英文版）,2011,9(3):431-439. 被引量：1
  • 2彭一准,原魁,刘俊承,邹伟.室内移动机器人的三层规划导航策略[J].电机与控制学报,2006,10(4):380-384. 被引量：3
  • 3段勇,徐心和.基于模糊神经网络的强化学习及其在机器人导航中的应用[J].控制与决策,2007,22(5):525-529. 被引量：13
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引证文献5

二级引证文献47

Journal of Harbin Institute of Technology(New Series)
2005年 第3期

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