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基于多机器人协调的搜集与围捕问题的研究
Alternative TitleResearch on the Multi-Robot System Cooperation Based on Foraging and Pursuit Game
王姝莉
Subtype工学硕士
Thesis Advisor谭民
2003-05-01
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline控制理论与控制工程
Keyword多机器人 搜集 围捕 任务分配 多样性 增强学习 Multi-robot System Foraging Pursuit Game Task Assignment Diversity Reinforcement
Abstract经讨了半个世纪的发展,机器人技术从理论到应用都取得了很大的成就,其应 用领域涵盖了航空、农业、建筑业、制造业、服务业等各个方面。随着应用领域 的延伸,多机器人系统研究也越来越受到重视。与单机器人相比,多机器人系统 具有灵活、高效以及容错能力强等优势。但是系统维数的提高,也给多机器人研 究带来了许多新的问题和挑战,例如任务分配,通讯结构的确立,传感信息融合等。本文将基于多机器人搜集、围捕两项任务,从任务分配、多样性研究以及增 强学习在多机器人系统中的应用等方面,对多机器人系统展开深入研究。具体内容如下: 首先,本文对多机器人系统的研究状况进行综述,分析了多机器人系统的特点、 主要研究问题、系统参数以及性能评价指标,最后简要介绍了本文的选题背景和 其次,结合多机器人搜集任务,本文讨论了在动态环境中的分布式异构多机器人系统的任务分配问题。针对搜集任务本身特点,我们将任务分解成‘搜索’和 ‘收集’两个子任务,并提出了基于有限状态自动机的三个搜集算法。通过在仿 真实验中,对采取不同任务分配方案的三种搜集算法进行比较,分析了环境变化、 机器人自身结构等因素对任务分配的影响。 第三,在前面提出的系统结构及搜集算法的基础上,研究了多样性等参数对多 机器人系统效率的影响。与以前研究该问题的学者不同,我们把研究范围拓展到 整个搜集任务,而不是仅限于任务执行过程中的一段时间,这样,我们发现在执 行搜集任务时,系统多样性与系统效率间的关系是变化的,而不是简单的负相关。 此外,系统中机器人的数目,环境中被搜集物的密度等参数与多机器人系统性能的关系,也在本文中进行了讨论。 第四,基于围捕任务,研究了增强学习在多机器人系统中的应用。结合任务本 身的特点,分别建立了状态集和行为集:并通过学习把两者匹配起来,使机器人 在任何状念下都能找到合适的行为。在文中,我们采用了Sarsa、Q-learning和 改进的sarsa算法,并通过一系列仿真实验,对学习算法进行了验证、分析。 本文最后对论文所取得的研究成果进行了总结,并阐述了下一步的研究工作。
Other AbstractAfter the more than half century development, Robotics technology and research come into a new age. The application field of robotics has covered manufacturing, aeronautics, military application, nuclear industry, and medical service etc. Along with the application field extension, the single robot's limit emerges, and the Multi-Robot System (MRS) begins attracting more and more attention. Compared with single robot system, MRS has the advantage of flexibility, efficiency and robustness. However, the characteristics of MRS also bring unique challenges, such as task assignment, communication structure, coordination and cooperation. Based on foraging and pursuit game, the thesis will focus on the research of task allocation, system diversity's influence and the application of reinforcement learning in the Multi-Robot System. First, the thesis will introduce the MRS's characteristics, main research fields, and key evaluation parameters. In addition, a brief introduction on the thesis's background and content will be presented. Secondly, based on the task of foraging, we address the problem of automatic task allocation among heterogeneous robots in dynamic environment. Three foraging algorithms, which adopt different task allocation strategies, will be presented and tested on our simulating platform MultiSim. By comparing their performances in simulation, the efficiency of strategy is testified. Thirdly, based on the system structure and foraging algorithms of last chapter, the farther research on the system diversity's influence on the system's performance is presented. By extend the test range to the whole foraging process in the simulation; we come to the different conclusion from the traditional research. Besides, the other factors' influence, such as robot team's size and density of attractors, are also considered in the research. Fourthly, based on the game of pursuit, the research on the reinforcement's application in MRS is presented. According to the characteristics of task, the states and behaviors sets are built respectively; the |earning's target is map the two set in a good way. In thesis, three reinforce learning algorithms, Sarsa, Q-learning and improved Sarsa, are applied to the pursuit game. By discussing their performance in simulation, the algorithms are compared and analyzed; besides, the feasibility of algorithms is also testified. Finally, a conclusion is given and future work is addressed.
shelfnumXWLW718
Other Identifier718
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/6827
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
王姝莉. 基于多机器人协调的搜集与围捕问题的研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2003.
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