基于蚁群混合人工势场法的多机器人编队运动

Motion of Multi-robot Formation Based on ACO and APF

针对多机器人路径规划问题提出了一种基于蚁群混合人工势场法的集成编队路径规划方法,该方法基于领航者跟随法构造多机器人的编队队形,利用改进蚁群优化(ACO)算法为领航者提供复杂障碍物环境下的全局路径,结合改进人工势场(APF)法决策避障。在全局路径规划阶段,通过改进ACO算法提高了路径的最优性和安全性;在局部路径规划阶段,通过优化APF法的引力、斥力系数以及避障函数解决了标准APF法下机器人易陷入局部最优、易受复杂环境干扰等问题;为提高多机器人的编队运动能力,领航者采取自适应导航策略以提高路径追踪能力,跟随者自适应修正运动速度以较好地维持编队队形。实验结果表明,所提方法成功地协调了多机器人的路径规划和编队问题。

An integrated formation path planning method based on improved ant colony algorithm and artificial potential field method is proposed for multi-robot path planning,which constructs the formation of multiple robots based on the leader-follower method,uses the improved Ant Colony Optimization(ACO)to provide global paths for the leader under complex obstacle environments,and uses the improved Artificial Potential Field(APF)to make decisions for obstacle avoidance.In the global path planning stage,the ACO improves the optimality and safety of the path.In the local path planning stage,the gravitational and repulsive coefficient of APF and the obstacle avoidance function are optimized to solve the problems of the standard APF of being prone to falling into local optimums and being susceptible to the interference of complex environments.In order to improve the formation movement capability of multiple robots,the leader adopts an adaptive navigation strategy to improve the path tracking capability,and the follower adaptively corrects the motion speed to better maintain the formation.The experimental results show that the proposed method successfully coordinates the multi-robot path planning and formation problems.