复杂环境下多机器人协同覆盖搜索路径规划

Multi-robot Path Planning for Collaborative Full- Coverage Search in Complex Environments

为提高复杂环境下多机器人协同搜索的覆盖效率及适应性,提出一种多机器人协同覆盖搜索路径规划策略。首先,在目标区域中利用协同进化粒子群优化(CCPSO2)算法进行传感器位置部署;其次,利用改进的K-means方法对传感器部署点进行聚类,实现有效的任务区域划分;最后,以部署的传感器位置为路径点求解旅行商问题(TSP),获取每个机器人的封闭路径,从而实现协同覆盖搜索。实验结果表明,所提算法能够在保证良好避障和覆盖周期最小化的同时,为每个机器人获得更均匀的覆盖路径,实现多机器人的有效协同覆盖搜索,且能够有效适应外部复杂环境,具有良好的鲁棒性。

In order to improve the coverage efficiency and adaptability of multi-robot collaboration search in complex environments,a multi-robot path planning strategy for collaborative full-coverage search is proposed.Firstly,the Cooperative Coevolving Particle Swarm Optimization(CCPSO2)is used for sensor deployment in the target area.Secondly,the improved K-means method is used to cluster sensor deployment points,so as to achieve effective task area division.Finally,the deployed sensor location is taken as the waypoint to solve the Traveling Salesman Problem(TSP),and the enclosed path of each robot is obtained,so as to realize collaborative full-coverage search.The experimental results show that the proposed method can obtain a more evenly-distributed coverage path for each robot while ensuring good obstacle avoidance and minimizing the coverage period,which realizes effective collaborative full-coverage search of multiple robots,and can effectively adapt to the external complex environments with good robustness.