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

7 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publication2021 IROS - IEEE/RSJ International Conference on Intelligent Robots and Systems
Subtitle of host publicationCONFERENCE DIGEST
PublisherIEEE
Pages3724-3730
ISBN (Electronic)978-1-6654-1714-3
ISBN (Print)978-1-6654-1715-0
Publication statusPublished - 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Title2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
LocationOnline
PlaceCzech Republic
CityPrague
Period27 September - 1 October 2021

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).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review