【作者】 王醒策；

【导师】 张汝波；

【作者基本信息】 哈尔滨工程大学 ， 计算机应用技术， 2002， 硕士

【摘要】 本文所阐述的内容是在二维有障空间机器人动态编队的方法，分别就以下几方面的问题进行了研究和探讨： 首先是关于多机器人进行协作的体系结构的研究。在论文中结合了分散式控制和集中式控制的优点，应用了分布式控制。控制的结构是自上而下的，共分为三层结构。 然后对各个层次的规划方法进行了研究：第一层是任务级规划。为了节省时间和空间，应用势场栅格法来解决多机器人编队的全局路径规划问题。第二层实现的是行为级规划。在这里编队的思想主要是应用Brooks的行为思想，把整个任务分解为若干个行为。编队的方法主要应用的是强化学习的方法，通过学习可以使机器人在不同的环境中采取合适行为。内外两部分强化信号被用来兼顾机器人在编队中的个体利益以及队伍的整体利益。应用学习的方法，多机器人的自适应以及自主性都得到了体现，同时体现了机器人内部的协调性。最后一层实现的是动作层规划，应用模糊控制的方法实现对不同动作的选取。 自上而下的各层中采用了不同的实现方法，充分地体现了机器人的智能。在仿真试验中进一步验证了所有方法的可行性和有效性。更多还原

【Abstract】 The content elaborated in the thesis is the technique of multirobots’ team formation in the field with obstacles. The following aspects are investigated and discussed.Firstly it is the study of the architecture structure of the multirobots’ cooperation. The system combines the advantage of the central and the dispersed architecture and uses the distributed architecture. With this multi-layer control , the task of every part is clear. And it is also a multi-level architecture, having three levels from above to the below.Then the detailed study of each level is illustrated here. The first level is the task planning level. In order to save the time and the memory, the method of potential and the grid is used to solve the global path planning problem. The second level is the planning of the robot’s behavior. Here the thought of the Brooks behavior is used to cut the team formation into several behaviors. And the way to choose the adapted behavior in different environment is mainly the Reinforcement Learning, hi the Reinforcement Learning algorithm is explained here , the inside reinforcement signals and outside reinforcement signals are applied to show the interests of the robot and its whole group. Using this way ,the self-adaptivity ,self-determination and the cooperation of the robots are all embodied clearly. The last level is the action planning, with the fuzzy way choosing the different action.hi the simulation of the experiment , the feasibility of the technology is verified further.更多还原