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Self-reconfiguration Strategies for Space-distributed Spacecraft

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Published
  • Tianle Liu
  • Zhixiang Wang
  • Yongwei Zhang
  • Ziwei Wang
  • Zihao Liu
  • Yizhai Zhang
  • Panfeng Huang
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Publication date25/12/2024
Host publication2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
Pages9879-9884
Number of pages6
ISBN (electronic)9798350377705
ISBN (print)9798350377712
<mark>Original language</mark>English

Abstract

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.