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移动机器人自治导航的软计算方法研究
其他题名RESEARCH OF SOFT COMPUTING ON AUTONOMOUS NAVIGATION OF MOBILE ROBOT
李佳宁
学位类型工学博士
导师易建强
2005-04-10
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业控制理论与控制工程
关键词移动机器人 自治导航 软计算 神经模糊推理网络 强化学习 Mobile Robot Autonomous Navigation Soft Computing
摘要软计算为开展移动机器人的智能研究提供了新的手段和挑战。本文以一种全方位移动机械手为背景,结合中国科学院百人计划项目“智能控制方法及应用研究”和科技部国际科技合作重点项目“智能机器人的脑功能开发”,展开软计算方法在基于行为控制结构和感知器的移动机器人自治导航领域的研究。 首先对一类智能机器人—移动机器人的相关问题进行了介绍,综述了智能控制在移动机器人体系结构领域的研究进展以及软计算方法在基于感知器的移动机器人自治导航领域的研究和应用,并介绍了本文的选题背景和内容安排。 其次,基于模块化思想,搭建了全方位移动机械手的控制系统框图。设计了一种新的以行为模型为基础的混合式体系结构,分析了行为协调模块的设计要点,并给出了两个说明性实例。 第三,从改进通常的网络结构、节点功能和推理机制出发,提出了一种扩展型神经模糊推理网络,该网络可以弥补原有网络的不精确推理和信息损失。针对基于训练数据的学习问题,分别设计了基于 Mamdani 模型的离线和在线以及基于包含确定度的 Mamdani 模型的三阶段学习算法。复杂系统模糊辨识仿真验证了网络结构和学习算法的有效性,并成功地应用于移动平台墙壁跟踪导航控制器的设计。 第四,针对扩展型神经模糊控制器的基于非训练数据的学习问题,建立了一类基于强化学习的神经模糊控制系统。考虑将强化学习转化为基于训练数据学习的实现思路,设计了神经模糊控制系统的在线学习算法。并同时建立了基于强化学习的径向基函数网络控制系统。移动平台的避障仿真验证了控制系统结构和学习算法的有效性。 第五,设计了三个复合行为导航仿真实验,验证了前述章节提出的体系结构、神经模糊控制系统结构和学习算法应用于基于感知器的移动机器人在未知环境中自治导航的有效性。 最后,总结了全文的研究成果,并展望了可以进一步开展的工作。
其他摘要This dissertation focuses on the research of soft computing on sensor-based autonomous navigation of an omni-directional mobile manipulator with behavior-based control. Firstly, relevant aspects of mobile robots, which is a family of intelligent robots, are introduced. The research status of intelligent control on architecture of mobile robot and soft computing on sensor-based autonomous navigation of mobile robots are reviewed respectively. Also, the background and roadmap of this dissertation are presented. Secondly, the frame of control system for the omni-directional mobile manipulator is established, which is based on module feature. Then a new behavior-based hybrid architecture and key points of the design for behavior coordination mechanism are presented. The validity is described by two illustrative examples. Thirdly, considering the usual structure of neural-fuzzy inference network, node function and inference mechanism, this dissertation proposes an extended neural-fuzzy inference network, which can overcome the imprecise reasoning and the loss of information. To solve the problem of training data based learning for the extended neural-fuzzy inference network, an on-line and an offline learning algorithm based on mamdani fuzzy model and a learning algorithm based on mamdani fuzzy model with certainty grades are presented respectively, which consist of three leaning phases. Simulation results of fuzzy identification for complex systems show effectiveness of the structure of extended neural-fuzzy inference network and learning algorithm, which is also applied successfully to the development of navigation controller for wall-following of mobile platform. Fourthly, to solve the problem of non-training data based learning for the extended neural-fuzzy inference network, a class of reinforcement learning based neural-fuzzy control system is established. This dissertation designs a learning algorithm for the reinforcement learning based neural-fuzzy control system from another point of view, which is trying to convert reinforcement learning problem into training data based learning problem. Also a reinforcement learning based radial-basis function network control system is established. Simulation results of obstacle-avoidance for the mobile platform in unknown environments show the validity of the structure and learning algorithm of the reinforcement learning based control. Fifthly, three navigation simulations based on composite behavior are designed, which verify the effectiveness and applicability of the presented architecture, the structure and learning algorithm of the neural-fuzzy control system for sensor-based autonomous navigation of mobile robot in unknown environments. Finally, the obtained research results are summarized and future work is addressed.
馆藏号XWLW932
其他标识符200218014603164
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5844
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
李佳宁. 移动机器人自治导航的软计算方法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2005.
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