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       随着机器人技术的不断发展和应用领域的不断拓展，多机器人系统因其所具有的柔性、并行性以及鲁棒性而受到广泛的关注，在多机器人系统的研究任务中，多机器人围捕以其天然的对抗性和入侵者运动的不可预测性而成为多机器人系统研究的热点。本文主要针对动态未知环境下的多机器人协调围捕开展研究，主要内容如下：

       首先，给出了多机器人系统的研究背景和研究意义，对多机器人系统的发展现状、主要研究内容和代表性多机器人系统进行了介绍，并综述了多机器人围捕的研究现状，最后对论文主要内容和结构安排做了介绍。

       其次，针对现有基于入侵者预测步数的围捕点建立方法，其预测步数通常根据经验确定的问题，提出一种基于动态预测入侵者运动轨迹的多机器人围捕方法。机器人基于观测的入侵者位置，采用三次样条插值法拟合入侵者的运动轨迹，以此为依据，产生预测步数相关的一系列围捕点；对于每一预测步数，按照距离均值最小的原则建立机器人与围捕点的一对一映射关系。在此基础上，以机器人到对应围捕点距离方差最小为评价标准，确定入侵者最优的预测步数，并得到机器人期望的围捕点；在各机器人基本到达期望围捕点后，机器人系统实施对入侵者的收缩围捕，所提方法的有效性通过仿真实验进行了验证。

       第三，针对建立围捕点的多机器人围捕方法可能带来的等待效应问题，提出一种基于区域划分的多机器人模糊协调围捕方法。以入侵者为中心，按照机器人到入侵者的距离，将围捕环境划分为3个区域：趋近区、追逐区和捕获区。机器人根据所处区域的不同采用针对性的处理；根据机器人、入侵者及其左右最近邻的同伴之间形成的最小夹角情况，设计了L/R模糊PD控制器和M模糊控制器用以进行运动方向的调整，最终实现机器人间的协调围捕。通过仿真进行了验证。

      第四，搭建多机器人系统实验平台。从机器人硬件组成和控制系统方面做了介绍，对基于GPS的机器人定位、目标识别与定位、以及多机器人系统通讯结构进行了阐述，并对多机器人实验平台进行了实验验证。

     最后，对本文工作进行总结，并指出需要进一步开展的研究工作。

 

   With the development of robot technology and increasing potential applications, multi-robot systems have received much attention due to their flexibility, parallelism, and robustness. Multi-robot hunting is specifically researched due to its characteristics of antagonism and unpredicted motion of the invader. This thesis focuses on the research of multi-robot hunting in dynamic unknown environments. The contents are as follows:

Firstly, the research background and its significance of multi-robot systems are given. The research development and main research contents of multi-robot systems are introduced, and the representative multi-robot systems are demsontrated. Then multi-robot hunting is reviewed. The contents and structure of this thesis are also introduced. 

  Secondly, aiming at the problem of the encirclement points based hunting method with the prediction of the invader where the prediction step is empirically determined, a hunting approach based on dynamic prediction of target motion is proposed where the prediction step is optimized according to current environment. Based on the observed positions of the invader, its motion trajectory is acquired by cubic spline interpolation. Then, a series of encirclement points corresponding to different prediction steps are obtained. For each prediction step, the one-to-one mapping between the robots and encirclement points is established by minimizing the average distances. On this basis, an optimized prediction step is determined according to the variance of the distances between the robots and their respective encirclement points. Afterward, each robot obtains its desired encirclement point, and moves towards this point. After the robots basically arrive at their desired positions, they shrink to capture the target. The effectiveness of the proposed approach is verified by simulations.

  Thirdly, aiming at the problem of waiting effect caused by the encirclement points based multi-robot hunting method, a multi-robot fuzzy coordinated hunting approach based on regional division is proposed. Taking the invader as the center, the surrounding environment is divided into three zones according to the distance between the robot and the invader, and they are approaching zone, chasing zone and capturing zone. The robot in different zones shall adopt respective targeted solution. Based on the minimal angles formed among the robot, the invader, and the left and right nearest neighbors of the robot, L/R fuzzy PD controller and M fuzzy controller are designed to adjust the moving direction of the robot for coordinated hunting among the robots. The proposed approach is verified by simulations.

  Fourthly, an experimental platform of multi-robot system is built. The hardware components and control system are presented. The robot localization based on GPS, invader identification and positioning, as well as communication structure are then expounded, respectively. On this basis, the multi-robot experimental platform is tested.

  Finally, the conclusions are given and future work is listed.