基于强化学习的多机器人系统的环围编队控制  

Ring-around Formation Control of Multi-robot Systems Based on Reinforcement Learning

作　　者：韩艺琳 王丽丽[1] 杨洪勇[1] 范之琳 HAN Yilin;WANG Lili;YANG Hongyong;FAN Zhilin(School of Information and Electrical Engineering,Ludong University,Yantai 264025,China)

机构地区：[1]鲁东大学信息与电气工程学院,山东烟台264025

出　　处：《复杂系统与复杂性科学》2023年第3期97-102,共6页Complex Systems and Complexity Science

基　　金：国家自然科学基金(61673200)。

摘　　要：针对机器人对未知目标的编队跟踪问题,建立机器人运动控制模型,提出了基于强化学习的目标跟踪与环围控制策略。在强化学习策略驱动下,机器人探索发现目标点位置并展开跟踪,使用环围编队运动模型对机器人跟踪策略进行实时优化,实现对逃逸目标点的动态跟踪与环围控制。搭建了多机器人运动测试环境,实验表明结合强化学习的方法能够缩短多机器人编队调节时间,验证了多机器人环围编队控制策略的有效性。For the robot formation tracking problem of unknown target,a robot motion control model is established,and a target tracking and ring-around control strategy based on Reinforcement Learning(RL)is proposed to solve the problem.Driven by RL,the robot explore the location of the target point and initiate tracking.The robot tracking strategy is optimized in real time using the ring-around formation motion model to achieve dynamic tracking and ring-around control of the fleeing target point.A multi-robot motion control environment is established,and the experiments indicate that the combined RL can accelerate the multi-robot formation adjustment time and prove the efficiency of the multi-robot ring-around formation control strategy.

关 键 词：运动控制 强化学习 目标跟踪 环围控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]