WeiBing Huang

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Dan Chen

Fujian Medical University

Zhezhuang Xu

Fuzhou University

Wenpeng Zhang

National University of Defense Technology

X. Sun

Shandong University

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Fuzhou University

Education Department of Fujian Province

Jiangsu University

Fujian Medical University

Union Hospital

Liaoning Cancer Hospital & Institute

Universiti Putra Malaysia

Changhong (China)

University of Science and Technology Beijing

Xi'an Jiaotong University

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Reinforcement Learning Based End-to-end Autonomous Obstacle Avoidance for Manipulators

2021 China Automation Congress (CAC) (2023)

Wenpeng Zhang Dan Chen X. Sun WeiBing Huang Zhezhuang Xu

In this paper, a simple end-to-end obstacle avoidance method is introded for manipulators. First of all, the 2D images in the workspace are used to uniformly describe the characteristics of obstacles in the 3D space. After that, a model-free reinforcement learning algorithm DrQ-v2 is used to train the obstacle avoidance strategy, which directly outputs the joint angles to avoid the obstacles autonomously and accurately in the joint space. Finally, the simulation results demonstrate that the proposed end-to-end simple control method is more effective and convenient in handling obstacle avoidance tasks in complex dynamic environments.

Obstacle avoidance

End-to-end principle

10.1109/cac59555.2023.10451444

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