Self-reconfiguration Strategies for Space-distributed Spacecraft
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916496262823936 |
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| author | Liu, Tianle Wang, Zhixiang Zhang, Yongwei Wang, Ziwei Liu, Zihao Zhang, Yizhai Huang, Panfeng |
| author_facet | Liu, Tianle Wang, Zhixiang Zhang, Yongwei Wang, Ziwei Liu, Zihao Zhang, Yizhai Huang, Panfeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17137 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-reconfiguration Strategies for Space-distributed Spacecraft Liu, Tianle Wang, Zhixiang Zhang, Yongwei Wang, Ziwei Liu, Zihao Zhang, Yizhai Huang, Panfeng Robotics Artificial Intelligence 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. |
| title | Self-reconfiguration Strategies for Space-distributed Spacecraft |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2411.17137 |