让太空飞船在轨自主组装，提升任务响应与维护效率
Self-reconfiguration Strategies for Space-distributed Spacecraft
- 结合模仿学习与强化学习，优化模块装配顺序策略
- 通过A*算法规划机械臂路径，实现精准运动控制
- 适用于需要快速重构的在轨航天任务，如深空探测
本文提出一种分布式在轨航天器组装算法，使未来航天器可在轨自主集成不同功能模块，形成具备特定功能的结构。该组织方式具有可重构性、快速任务响应和易维护等优势。合理的高效在轨自重构算法对实现分布式航天器的潜力至关重要。本文采用模仿学习与强化学习相结合的框架，学习模块操作顺序策略，并设计机械臂运动算法执行操作序列。通过在模块表面构建地图，利用A*算法完成机械臂路径点规划，再通过正逆运动学实现关节协同规划。最终结果在Unity3D中呈现。
原文摘要 · Abstract (English)
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.
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