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International Journal of Machine Learning and Cybernetics
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04-08-2024 | Original Article

Mobile robot path planning based on multi-experience pool deep deterministic policy gradient in unknown environment

Authors: Linxin Wei, Quanxing Xu, Ziyu Hu

Published in: International Journal of Machine Learning and Cybernetics | Issue 12/2024

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Abstract

The article introduces a sophisticated deep reinforcement learning method for path planning of autonomous mobile robots in unknown environments. It highlights the use of the Deep Deterministic Policy Gradient (DDPG) algorithm, enhanced with a multi-experience pool strategy and a dense reward function. The multi-experience pool method segments experiences based on obstacle movement, enabling the robot to better predict and avoid obstacles. The dense reward function incorporates ideas from artificial potential fields, providing a more stable and effective learning process. The algorithm was extensively tested in simulation environments and demonstrated superior performance in terms of convergence speed and success rate compared to other methods. The article also includes a real-world application of the algorithm, showcasing its practicality and potential for industrial use. The findings suggest that this approach could significantly advance the field of autonomous robotics by improving the efficiency and adaptability of path planning algorithms.
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Metadata
Title
Mobile robot path planning based on multi-experience pool deep deterministic policy gradient in unknown environment
Authors
Linxin Wei
Quanxing Xu
Ziyu Hu
Publication date
04-08-2024
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 12/2024
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-024-02281-6

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