An End-to-End Trajectory Planning Strategy for Free-floating Space Robots
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings of the 40th Chinese Control Conference |
Editors | Chen Peng, Jian Sun |
Publisher | IEEE Computer Society |
Pages | 4236-4241 |
ISBN (Electronic) | 9789881563804 |
ISBN (Print) | 9781665411950 |
Publication status | Published - Jul 2021 |
Publication series
Name | Chinese Control Conference, CCC |
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Volume | 2021-July |
ISSN (Print) | 1934-1768 |
ISSN (Electronic) | 2161-2927 |
Conference
Title | 40th Chinese Control Conference (CCC 2021) |
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Place | China |
City | Shanghai |
Period | 26 - 28 July 2021 |
Link(s)
Abstract
The traditional trajectory planning methods of free-floating space robots have the dynamic singular problem and the difficulty of accurate modeling. Although learning-based approaches have achieved remarkable performance on such task, they mainly focus on single modular design such as the perception, planning, or control part. Optimization-based end-to-end method can well combine perception, planning and control, which not rely on parameters of the dynamic model and reduce the difficulty of manually adjusting modular controllers' parameters. Therefore, we developed an end-to-end trajectory planning strategy based on optimization with multiple constraints. The whole strategy consists of several multi-layer neural networks and is optimized by a deep reinforcement learning algorithm based on maximum entropy. The results of visualization show that our strategy can capture the information of robotic arm from vision directly. Moreover, we evaluate the kinematic and dynamic features of the system and testify our strategy in anti-interference experiments. The performance of our strategy demonstrates the availability and robustness of the system.
Research Area(s)
- Reinforcement learning, Space robot, Trajectory planning
Citation Format(s)
An End-to-End Trajectory Planning Strategy for Free-floating Space Robots. / Wang, Shengjie; Cao, Yuxue; Zheng, Xiang et al.
Proceedings of the 40th Chinese Control Conference. ed. / Chen Peng; Jian Sun. IEEE Computer Society, 2021. p. 4236-4241 (Chinese Control Conference, CCC; Vol. 2021-July).
Proceedings of the 40th Chinese Control Conference. ed. / Chen Peng; Jian Sun. IEEE Computer Society, 2021. p. 4236-4241 (Chinese Control Conference, CCC; Vol. 2021-July).
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with host publication) › peer-review