Trajectory Planning Algorithm of Manipulator in Small Space Based on Reinforcement Learning

Haoyu Wang, Huaishi Zhu, Fangfei Cao*

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

The development of reinforcement learning has driven the progress of robot control technology. In recent years, reinforcement learning has become one of the highly concerned fields in the academic community, especially the control of robotic arms in the industrial field. In order to achieve intelligent and efficient production, the emphasis is on the research of obstacle avoidance motion planning of the manipulator. However, traditional trajectory planning algorithms have problems such as slow convergence speed, low intelligence, and difficulty in achieving optimization. In this regard, this research takes the six degrees of freedom manipulator PUMA550 as the research object, and focuses on the obstacle avoidance motion planning problem of the manipulator, studies the manipulator modeling based on the improved D-H parameter method, Rapidly-exploring Random Trees (RRT) algorithm, the Q-learning algorithm and the double Q network learning alzorithm.

源语言英语
主期刊名Proceedings - 2023 China Automation Congress, CAC 2023
出版商Institute of Electrical and Electronics Engineers Inc.
5780-5785
页数6
ISBN(电子版)9798350303759
DOI
出版状态已出版 - 2023
活动2023 China Automation Congress, CAC 2023 - Chongqing, 中国
期限: 17 11月 202319 11月 2023

出版系列

姓名Proceedings - 2023 China Automation Congress, CAC 2023

会议

会议2023 China Automation Congress, CAC 2023
国家/地区中国
Chongqing
时期17/11/2319/11/23

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引用此

Wang, H., Zhu, H., & Cao, F. (2023). Trajectory Planning Algorithm of Manipulator in Small Space Based on Reinforcement Learning. 在 Proceedings - 2023 China Automation Congress, CAC 2023 (页码 5780-5785). (Proceedings - 2023 China Automation Congress, CAC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAC59555.2023.10450413