均分点蚁群算法在群集机器人任务规划中的应用与研究

Application and research on swarm robots mission planning using equal division point ant colony algorithm

提出了一种求解群集机器人协作任务规划问题的均分点蚁群算法(EDPACA)。通过多组蚂蚁群相互协作搜索,构架了一种新蚁群算法的解结构,并设计了更合理的评估函数,使其在评价时充分考虑均衡任务点探测,最后利用2-opt技术解决了各子周游路径的交叉问题,获得了总代价最优的解。该算法将蚁群技术首次应用于集群机器人的任务调度规划中,成功解决了中大规模任务规划问题。仿真实验结果表明,均分点蚁群算法能提高群集机器人执行任务的效率,同时也是解决多旅行商问题的另种新思路。

An equal division point ant colony algorithm （EDPACA） was proposed to solve the swarm-robot mission planning problem. Firstly, the algorithm was designed by a novel solution construction through muhi-group ants＇ cooperative search, and a more reasonable evaluation function which sufficiently considered the equal division mission point factor. Secondly, the crossover problems of sub-circular paths were solved by the 2-opt method. It is the first time to apply an ant colony algorithm to swarm-robot mission planning, the proposed algorithm can solve large scale mission planning problems effectively. The experiment results show that EDPACA improved the efficiency of executing a large number of tasks. Additionally, it also provides a new idea to solve the multi-person traveling salesman problem.