Research on Path Tracking Technology for Tracked Unmanned Vehicles Based on DDPG-PP
Realizing path tracking is crucial for improving the accuracy and efficiency of unmanned vehicle operations. In this paper, a path tracking hierarchical control method based on DDPG-PP is proposed to improve the path tracking accuracy of tracked unmanned vehicles. Constrained by the objective of min...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/7/603 |
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| author | Yongjuan Zhao Chaozhe Guo Jiangyong Mi Lijin Wang Haidi Wang Hailong Zhang |
| author_facet | Yongjuan Zhao Chaozhe Guo Jiangyong Mi Lijin Wang Haidi Wang Hailong Zhang |
| author_sort | Yongjuan Zhao |
| collection | DOAJ |
| description | Realizing path tracking is crucial for improving the accuracy and efficiency of unmanned vehicle operations. In this paper, a path tracking hierarchical control method based on DDPG-PP is proposed to improve the path tracking accuracy of tracked unmanned vehicles. Constrained by the objective of minimizing path tracking error, with the upper controller, we adopted the DDPG method to construct an adaptive look-ahead distance optimizer in which the look-ahead distance was dynamically adjusted in real-time using a reinforcement learning strategy. Meanwhile, reinforcement learning training was carried out with randomly generated paths to improve the model’s generalization ability. Based on the optimal look-ahead distance output from the upper layer, the lower layer realizes precise closed-loop control of torque, required for steering, based on the PP method. Simulation results show that the path tracking accuracy of the proposed method is better than that of the LQR and PP methods. The proposed method reduces the average tracking error by 94.0% and 79.2% and the average heading error by 80.4% and 65.0% under complex paths compared to the LQR and PP methods, respectively. |
| format | Article |
| id | doaj-art-9482993f7d8748d6b9288e63d7bc2a39 |
| institution | DOAJ |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-9482993f7d8748d6b9288e63d7bc2a392025-08-20T02:45:37ZengMDPI AGMachines2075-17022025-07-0113760310.3390/machines13070603Research on Path Tracking Technology for Tracked Unmanned Vehicles Based on DDPG-PPYongjuan Zhao0Chaozhe Guo1Jiangyong Mi2Lijin Wang3Haidi Wang4Hailong Zhang5School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, ChinaRealizing path tracking is crucial for improving the accuracy and efficiency of unmanned vehicle operations. In this paper, a path tracking hierarchical control method based on DDPG-PP is proposed to improve the path tracking accuracy of tracked unmanned vehicles. Constrained by the objective of minimizing path tracking error, with the upper controller, we adopted the DDPG method to construct an adaptive look-ahead distance optimizer in which the look-ahead distance was dynamically adjusted in real-time using a reinforcement learning strategy. Meanwhile, reinforcement learning training was carried out with randomly generated paths to improve the model’s generalization ability. Based on the optimal look-ahead distance output from the upper layer, the lower layer realizes precise closed-loop control of torque, required for steering, based on the PP method. Simulation results show that the path tracking accuracy of the proposed method is better than that of the LQR and PP methods. The proposed method reduces the average tracking error by 94.0% and 79.2% and the average heading error by 80.4% and 65.0% under complex paths compared to the LQR and PP methods, respectively.https://www.mdpi.com/2075-1702/13/7/603tracked unmanned vehiclespath trackingreinforcement learningpure pursuit |
| spellingShingle | Yongjuan Zhao Chaozhe Guo Jiangyong Mi Lijin Wang Haidi Wang Hailong Zhang Research on Path Tracking Technology for Tracked Unmanned Vehicles Based on DDPG-PP Machines tracked unmanned vehicles path tracking reinforcement learning pure pursuit |
| title | Research on Path Tracking Technology for Tracked Unmanned Vehicles Based on DDPG-PP |
| title_full | Research on Path Tracking Technology for Tracked Unmanned Vehicles Based on DDPG-PP |
| title_fullStr | Research on Path Tracking Technology for Tracked Unmanned Vehicles Based on DDPG-PP |
| title_full_unstemmed | Research on Path Tracking Technology for Tracked Unmanned Vehicles Based on DDPG-PP |
| title_short | Research on Path Tracking Technology for Tracked Unmanned Vehicles Based on DDPG-PP |
| title_sort | research on path tracking technology for tracked unmanned vehicles based on ddpg pp |
| topic | tracked unmanned vehicles path tracking reinforcement learning pure pursuit |
| url | https://www.mdpi.com/2075-1702/13/7/603 |
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