基于协同粒子群算法的航天器集群动态路径规划算法研究

Research on dynamic path planning algorithm of spacecraft cluster based on cooperative particle swarm algorithm

针对航天器集群在考虑障碍物规避前提下到达动态目标点的路径规划问题中,传统粒子群算法的搜索半径固定,会导致航天器在接近目标点时难以寻找到较优值的问题,将各成员航天器的轨道动力学问题转换为一种考虑约束下的最优化问题,提出了一种基于协同粒子群算法(CPSO)的路径规划方法:提出一种随着航天器与目标点之间距离变化而改变搜索半径的动态半径搜索法,并以此对CPSO算法进行改进。改进的CPSO算法通过动态搜索半径自主寻找当前时刻各成员航天器的最优路径,从而得到三维空间中航天器集群动态路径规划问题的最优解。仿真结果表明,采用改进的CPSO算法不仅可以得到航天器集群动态路径规划问题的最优解,还可以大大减少其路径规划中的燃料消耗量,提高各成员航天器路径的稳定性。

In order to solve the problem of path planning for the spacecraft cluster to reach the dynamic target point under the premise of considering obstacle avoidance.In view of the fixed search radius,it will be difficult for the spacecraft to find a better value when it is close to the target point.This paper converts the orbital dynamics of each member spacecraft into an optimization problem considering constraints,and proposes an improved CPSO algorithm based on coordination.The path planning method of the traditional particle swarm optimization(CPSO):The dynamic radius search method that changes the search radius by changing the distance between them,and improves the CPSO algorithm based on this.The improved CPSO algorithm autonomously finds the optimal path of each member spacecraft at the current moment through the dynamic search radius,thereby obtaining the optimal solution for the dynamic path planning of the spacecraft cluster in three-dimensional space.The simulation results show that the use of the improved CPSO algorithm can not only obtain the optimal solution to the spacecraft cluster dynamic path planning problem,but also greatly reduce the fuel consumption in its path planning and improve the path stability of each member spacecraft.