Modular satellite, which has the ability of self-repairing and accomplishing different tasks, draws more and more satellite designers’ attention recently. One of the trending topics is to design the algorithm of self-reconfigurable path planning, since searching a near-optimal path is an effective way to reduce electrical energy consumption and mechanical loss of satellites. A major thrust of this article is to examine a series of algorithms based on graph theory and deep reinforcement learning. We creatively propose the concept of link module and find the link module by calculating articulation points in the undirected connected graph of configuration. We propose a compressed algorithm of state transition and the deep reinforcement learning algorithms in the domain of self-reconfigurable modular satellites. The simulation results show the feasibility and effectiveness of the proposed planning algorithms.

具有自我修复能力并完成不同任务的模块化卫星近来引起了越来越多卫星设计者的关注。热门话题之一是设计自可重构路径规划算法，因为搜索接近最佳的路径是减少卫星电能消耗和机械损耗的有效方法。本文的主要目的是研究基于图论和深度强化学习的一系列算法。我们创造性地提出了链接模块的概念，并通过计算无向连接配置图中的铰接点来找到链接模块。我们提出了一种可自我重构的模块化卫星领域中的状态转移压缩算法和深度强化学习算法。