Multi-robot consensus formation based on virtual spring obstacle avoidance

Yushuai Fan, Xun Li, Xin Liu, Shuo Cheng, Xiaohua Wang
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Abstract

Abstract. A systematic improvement of the multi-robot formation control algorithm has been developed to address multi-robot formation instability. First, a static obstacle avoidance model based on spring force mapping is proposed, followed by an analysis of the influence of static and dynamic obstacles on the processing of multi-robot cooperative motion. Second, a leader is introduced to the formation to save computational costs. Third, the Velocity Obstacle (VO) algorithm is improved to resolve robot collisions during the dynamic mobility process caused by the increased number of multi-robot formations. Simultaneously, the dynamic speed limit function based on the position error for formation keeping is established. Finally, simulation experiments are carried out. Results show that when 5-robot and 20-robot formations were compared in the environment without dynamic conflict, the average value of the position error of 20-robot formations only increased by 39.47 %, and the average value of the path length did not differ significantly. In the dynamic conflict environment, the maximum position error of 20-robot formations increases by 73.03 % and the path length average value increases by 7.69 %. Our proposed method can control the motion of multiple robots in both conflict-free and conflict-filled environments, resulting in an effective motion planning scheme.
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基于虚拟弹簧避障的多机器人共识形成
摘要针对多机器人编队的不稳定性,对多机器人编队控制算法进行了系统改进。首先,提出了基于弹簧力映射的静态避障模型,然后分析了静态和动态障碍物对多机器人协同运动处理的影响。其次,在编队中引入领导者,以节省计算成本。第三,改进了速度障碍(VO)算法,以解决多机器人编队数量增加导致的动态移动过程中的机器人碰撞问题。同时,建立了基于位置误差的编队保持动态限速函数。最后,进行了仿真实验。结果表明,在无动态冲突的环境中,5 个机器人编队与 20 个机器人编队相比,20 个机器人编队的位置误差平均值仅增加了 39.47%,路径长度的平均值差别不大。在动态冲突环境中,20 个机器人编队的最大位置误差增加了 73.03%,路径长度平均值增加了 7.69%。我们提出的方法可以在无冲突和充满冲突的环境中控制多个机器人的运动,是一种有效的运动规划方案。
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