@inproceedings{c551c6f35f8c48218acdf842ced672e4,
title = "Research on Obstacle Avoidance Motion Planning of Space Manipulator Based on Reinforcement Learning",
abstract = "Making up for the lack of generalization and environmental simulation of traditional algorithms, a motion planning method of space manipulator based on reinforcement learning is designed. First, the standard Denavit-Hartenberg(DH) model of space manipulator is given. Further, combined with the characteristics of the space mission, the state space, action space and reward functions are designed. The Proximal Policy Optimization(PPO) is used as the framework to realize the motion planning task of the space manipulator. ISAAC GYM is chosed as the simulation platform to improve the training speed and strategy generalization ability through the setting of multiagent training and environment randomization at the same time. The simulation results show that the proposed method can realize the task of grasping the object by avoiding obstacles in the case of space microgravity, and the method has strong practicability and effectiveness.",
keywords = "deep reinforcement learning, motion planning, obstacle avoidance, space manipulator",
author = "Zixuan Zhang and Wei Dong and Chunyan Wang and Jing Sun",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",
year = "2025",
doi = "10.1007/978-981-96-2256-6_6",
language = "English",
isbn = "9789819622559",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "48--57",
editor = "Liang Yan and Haibin Duan and Yimin Deng",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 15",
address = "Germany",
}