A Multi-Target Trajectory Planning of a 6-DoF Free-Floating Space Robot via Reinforcement Learning
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 | 2021 IROS - IEEE/RSJ International Conference on Intelligent Robots and Systems |
Subtitle of host publication | CONFERENCE DIGEST |
Publisher | IEEE |
Pages | 3724-3730 |
ISBN (Electronic) | 978-1-6654-1714-3 |
ISBN (Print) | 978-1-6654-1715-0 |
Publication status | Published - 2021 |
Publication series
Name | IEEE International Conference on Intelligent Robots and Systems |
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ISSN (Print) | 2153-0858 |
ISSN (Electronic) | 2153-0866 |
Conference
Title | 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021 |
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Location | Online |
Place | Czech Republic |
City | Prague |
Period | 27 September - 1 October 2021 |
Link(s)
Abstract
Space robots have played an essential role in space junk removal. Compared with traditional model-based methods, model-free reinforcement learning methods are promising in tackling space capture missions, which is challenging due to the dynamic singular problem and measuring errors of dynamics parameters. Nevertheless, current research mostly focus on the single-target environment. In this paper, we propose a multi-target trajectory planning strategy of a 6-DoF free-floating space robot optimized by the Proximal Policy Optimization (PPO) algorithm. Furthermore, we adopt some augmentation techniques to improve the PPO algorithm on precision and stability of reaching multiple targets. In particular, we introduce an Action Ensembles Based on Poisson Distribution (AEP) method, which facilitates the policy to efficiently approximate the optimal policy. Our method can be easily extended to realize the task that the end-effector tracks a specific trajectory. We evaluate our approach on four tasks: circle trajectory tracking, external disturbances at joints, different masses of the base, and even single joint failure, without any further fine-tuning. The results suggest that the planning strategy has comparably high adaptability and anti-inference capacity. Qualitative results (videos) are available at [36].
Citation Format(s)
A Multi-Target Trajectory Planning of a 6-DoF Free-Floating Space Robot via Reinforcement Learning. / Wang, Shengjie; Zheng, Xiang; Cao, Yuxue et al.
2021 IROS - IEEE/RSJ International Conference on Intelligent Robots and Systems: CONFERENCE DIGEST. IEEE, 2021. p. 3724-3730 (IEEE International Conference on Intelligent Robots and Systems).
2021 IROS - IEEE/RSJ International Conference on Intelligent Robots and Systems: CONFERENCE DIGEST. IEEE, 2021. p. 3724-3730 (IEEE International Conference on Intelligent Robots and Systems).
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with host publication) › peer-review