基于离散粒子群算法的航天器在轨服务任务分配问题研究

On-orbit Servicing Task Allocation for Spacecrafts Using Discrete Particle Swarm Optimization Algorithm

航天器任务分配对于提高多颗服务航天器之间的协同工作效率具有十分重要的意义;针对服务航天器协同目标分配问题特点,提出了一种基于离散粒子群算法的协同目标分配方法,设计了新的离散粒子群位置和速度更新公式,综合分析影响目标卫星价值、服务航天器损耗以及距离消耗等3项关键指标因素,建立了在轨服务任务分配问题的数学模型;仿真结果表明:离散粒子群算法具有收敛速度快、寻优能力强等优点,能够有效地解决多约束条件下的服务航天器协同任务分配问题,特别是在大规模的任务分配中,该方法具有很强的优越性。

Spacecraft task allcation is very important to improve the cooperative work ratio of the on--orbit servicing spacecraft. A discrete particle swarm optimization （DPSO） algorithm is put forward for cooperative task allocation problems. A new code of particles and new update strategy for the position and speed of particles are applied, By analyzing the critical index factors which contain target satellite value, servicing spacecraft attrition and distance consumption, a on--orbit spacecraft task allocation model is formulated. The simulation results show that the DPSO algorithm has fast convergence, optimization capability, and can solve the on--orbit servicing spacecraft cooperative task allocation effectively. Especially in the large--scale distribution of tasks, the method has strong advantages.