This paper proposes a distributed on-orbit spacecraft assembly algorithm, where future spacecraft can assemble modules with different functions on orbit to form a spacecraft structure with specific functions. This form of spacecraft organization has the advantages of reconfigurability, fast mission response and easy maintenance. Reasonable and efficient on-orbit self-reconfiguration algorithms play a crucial role in realizing the benefits of distributed spacecraft. This paper adopts the framework of imitation learning combined with reinforcement learning for strategy learning of module handling order. A robot arm motion algorithm is then designed to execute the handling sequence. We achieve the self-reconfiguration handling task by creating a map on the surface of the module, completing the path point planning of the robotic arm using A*. The joint planning of the robotic arm is then accomplished through forward and reverse kinematics. Finally, the results are presented in Unity3D.

翻译：本文提出了一种分布式在轨航天器组装算法，未来航天器可在轨道上组装具有不同功能模块，形成具备特定功能的航天器结构。这种航天器组织形式具有可重构性、任务响应快速和维护便捷等优势。合理高效的在轨自重构算法对于实现分布式航天器的效益至关重要。本文采用模仿学习与强化学习相结合的框架进行模块搬运顺序的策略学习，随后设计机械臂运动算法来执行搬运序列。通过在模块表面建立地图，利用A*算法完成机械臂路径点规划，进而通过正逆运动学实现机械臂关节规划，最终在Unity3D中呈现结果，从而完成自重构搬运任务。