基于GWO-WOA的执行器严重故障多移动机器人编队重构控制

doi:10.12382/bgxb.2023.0880

摘要/Abstract

摘要：

针对执行器严重故障下多移动机器人的编队重构控制问题,提出一种基于灰狼优化-鲸鱼优化算法(Grey Wolf Optimizer-Whale Optimization Algorithm,GWO-WOA)的协同编队重构控制策略。设计一种故障观测器以检测多机器人系统中出现的执行器严重故障,并使执行器严重故障的机器人离开编队。利用匈牙利算法分配剩余机器人在期望重构编队中的位置,并用GWO-WOA规划出机器人的运动路径。提出编队重构综合控制策略,包括三部分,分别为基于一致性的编队保持控制、基于势能函数的避碰控制和基于比例积分的跟踪控制器,使多机器人在无碰撞的情形下实现了重构编队。仿真实验结果表明,所提出的编队重构控制策略能够实时监测出故障机器人,并且在形成期望重构编队的同时防止相互碰撞。

关键词: 多移动机器人系统, 非完整约束, 编队重构控制, 灰狼优化-鲸鱼优化算法, 避碰控制

Abstract:

A collaborative formation reconfiguration control strategy based on Grey Wolf Optimizer-Whale optimization algorithm (GWO-WOA) is proposed to solve the formation reconfiguration control problem of multi-robot systems with severe actuator failures. A fault observer is designed to detect the severe actuator failures in a multi-robot system, and cause the robots with severe actuator failures to leave the formation. Hungarian algorithm is used to allocate the positions of the remaining robots in the expected reconfiguration formation, and the GWO-WOA is used to plan the robots’ path. A collaborative formation reconfiguration control strategy is proposed, which includes three parts: consensus-based formation maintenance control, potential-energy-function-based collision avoidance control, and proportional-integral based tracking controller, enabling multiple robots to achieve formation reconfiguration without collision. The simulation experimental results show that the proposed formation reconfiguration control strategy can be used to monitor the faulty robots in real time and effectively prevent the robots from colliding with each other while forming a desired reconfiguration formation.

Key words: multiple mobile robot system, nonholonomic constraint, formation reconfiguration control, GWO-WOA, collision avoidance

中图分类号:

TP242.6

鞠爽, 王晶, 王灏, 周萌. 基于GWO-WOA的执行器严重故障多移动机器人编队重构控制[J]. 兵工学报, 2023, 44(S2): 114-125.

JU Shuang, WANG Jing, WANG Hao, ZHOU Meng. Formation Reconfiguration Control of Multiple Mobile Robots with Severe Actuator Faults Based on GWO-WOA[J]. Acta Armamentarii, 2023, 44(S2): 114-125.

图/表 17

图1 执行器故障情形下多机器人协同编队控制框架

Fig.1 Framework of collaborative formation control of multiple robots under actuator failure

图2 匈牙利算法求解过程图

Fig.2 Solution process of Hungarian algorithm

图3 GWO-WOA路径规划过程

Fig.3 Process of GWO-WOA path planning

表1 各个机器人初始状态

Table 1 Initial states of each robot

机器人	x/m	y/m	θ/rad
领导者	8.4	-17.5	0.52
跟随者1	11.3	-16.1	0.50
跟随者2	7.9	-16.3	0.37
跟随者3	6.9	-15.6	0.47
跟随者4	5.9	-16.2	0.58
跟随者5	6.2	-18.2	0.60
跟随者6	9.7	-17.3	0.51

图4 多机器人之间的通信拓扑结构

Fig.4 Communication topology of multi-robot system

图5 多机器人系统的期望初始编队队形

Fig.5 Initial expected formation of multi-robot system

图6 跟随者3的故障因子观测值

Fig.6 Observation values of fault factors for Follower 3

图7 多机器人系统期望的重构编队队形

Fig.7 Expected reconstructed formation of multi-robot system

图8 综合控制器式(52)控制下多机器人的运动轨迹

Fig.8 Trajectories of all robots controlledby collaborative controller (52)

图9 跟随者在x轴方向与期望位置的误差

Fig.9 Error between the current position and the expected position of follower in x-direction

图10 跟随者在y轴方向与期望位置的误差

Fig.10 Error between the current position and the expected positionof follower in y-direction

图11 跟随者与领导者之间角度的误差

Fig.11 Angle error between follower and leader

图12 各机器人之间的间距

Fig.12 Spacing between robots

图13 跟随者编队保持控制输入

Fig.13 Formation maintain control input of followers

图14 跟随者避碰控制输入

Fig.14 Collision avoidance control inputof followers

图15 跟随者的路径跟踪控制输入

Fig.15 Path tracking control input of followers

图16 综合控制器式(54)控制下多机器人的运动轨迹

Fig.16 Trajectories of all robots controlled by collaborative controller (54)

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