Efficient TD3 based path planning of mobile robot in dynamic environments using prioritized experience replay and LSTM
Abstract To address the challenges of sample utilization efficiency and managing temporal dependencies, this paper proposes an efficient path planning method for mobile robot in dynamic environments based on an improved twin delayed deep deterministic policy gradient (TD3) algorithm. The proposed me...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02244-z |
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| author | Yunhan Lin Zhijie Zhang Yijian Tan Hao Fu Huasong Min |
| author_facet | Yunhan Lin Zhijie Zhang Yijian Tan Hao Fu Huasong Min |
| author_sort | Yunhan Lin |
| collection | DOAJ |
| description | Abstract To address the challenges of sample utilization efficiency and managing temporal dependencies, this paper proposes an efficient path planning method for mobile robot in dynamic environments based on an improved twin delayed deep deterministic policy gradient (TD3) algorithm. The proposed method, named PL-TD3, integrates prioritized experience replay (PER) and long short-term memory (LSTM) neural networks, which enhance both sample efficiency and the ability to handle time-series data. To verify the effectiveness of the proposed method, simulation and practical experiments were designed and conducted. In the simulation experiments, both static and dynamic obstacles were included in the test environment, along with experiments to assess generalization capabilities. The algorithm demonstrated superior performance in terms of both execution time and path efficiency. The practical experiments, based on the assumptions from the simulation tests, further confirmed that PL-TD3 has improved the effectiveness and robustness of path planning for mobile robot in dynamic environments. |
| format | Article |
| id | doaj-art-70a6647a072f4f80bada8f87da58ed87 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-70a6647a072f4f80bada8f87da58ed872025-08-20T03:22:08ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-02244-zEfficient TD3 based path planning of mobile robot in dynamic environments using prioritized experience replay and LSTMYunhan Lin0Zhijie Zhang1Yijian Tan2Hao Fu3Huasong Min4School of Computer Science and Technology, Wuhan University of Science and TechnologySchool of Computer Science and Technology, Wuhan University of Science and TechnologySchool of Computer Science and Technology, Wuhan University of Science and TechnologySchool of Computer Science and Technology, Wuhan University of Science and TechnologyInstitute of Robotics and Intelligent Systems, Wuhan University of Science and TechnologyAbstract To address the challenges of sample utilization efficiency and managing temporal dependencies, this paper proposes an efficient path planning method for mobile robot in dynamic environments based on an improved twin delayed deep deterministic policy gradient (TD3) algorithm. The proposed method, named PL-TD3, integrates prioritized experience replay (PER) and long short-term memory (LSTM) neural networks, which enhance both sample efficiency and the ability to handle time-series data. To verify the effectiveness of the proposed method, simulation and practical experiments were designed and conducted. In the simulation experiments, both static and dynamic obstacles were included in the test environment, along with experiments to assess generalization capabilities. The algorithm demonstrated superior performance in terms of both execution time and path efficiency. The practical experiments, based on the assumptions from the simulation tests, further confirmed that PL-TD3 has improved the effectiveness and robustness of path planning for mobile robot in dynamic environments.https://doi.org/10.1038/s41598-025-02244-zPath planning in dynamic environmentTwin delayed deep deterministic policy gradient (TD3) algorithmReinforcement learningPrioritized experience replay (PER)Long short-term memory (LSTM ) |
| spellingShingle | Yunhan Lin Zhijie Zhang Yijian Tan Hao Fu Huasong Min Efficient TD3 based path planning of mobile robot in dynamic environments using prioritized experience replay and LSTM Scientific Reports Path planning in dynamic environment Twin delayed deep deterministic policy gradient (TD3) algorithm Reinforcement learning Prioritized experience replay (PER) Long short-term memory (LSTM ) |
| title | Efficient TD3 based path planning of mobile robot in dynamic environments using prioritized experience replay and LSTM |
| title_full | Efficient TD3 based path planning of mobile robot in dynamic environments using prioritized experience replay and LSTM |
| title_fullStr | Efficient TD3 based path planning of mobile robot in dynamic environments using prioritized experience replay and LSTM |
| title_full_unstemmed | Efficient TD3 based path planning of mobile robot in dynamic environments using prioritized experience replay and LSTM |
| title_short | Efficient TD3 based path planning of mobile robot in dynamic environments using prioritized experience replay and LSTM |
| title_sort | efficient td3 based path planning of mobile robot in dynamic environments using prioritized experience replay and lstm |
| topic | Path planning in dynamic environment Twin delayed deep deterministic policy gradient (TD3) algorithm Reinforcement learning Prioritized experience replay (PER) Long short-term memory (LSTM ) |
| url | https://doi.org/10.1038/s41598-025-02244-z |
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