Learning to construct a solution for UAV path planning problem with positioning error correction
Unmanned aerial vehicles (UAVs) are advanced flight systems. However, their positioning systems cause distance-dependent errors during flight. This study seeks to solve the UAV path planning problem with positioning error correction (UPEC) with an end-to-end method. Traditional methods struggle to b...
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| Vydáno v: | Knowledge-based systems Ročník 304; s. 112569 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
25.11.2024
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| ISSN: | 0950-7051 |
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| Abstract | Unmanned aerial vehicles (UAVs) are advanced flight systems. However, their positioning systems cause distance-dependent errors during flight. This study seeks to solve the UAV path planning problem with positioning error correction (UPEC) with an end-to-end method. Traditional methods struggle to balance solution quality and computational overload, and often have limited utilisation of scenario information. To overcome these issues, we propose a path planning model (PPM) based on deep reinforcement learning to solve the UPEC. The model has a complete structure that includes a mathematical model, feature engineering, solution process, neural policy network, scenario generation, training process, and test solution mechanism. Specifically, we first establish a Markov decision process (MDP) for UPEC and apply feature engineering with effective features to support decision-making. We then introduce a path planning neural network (PPNN) to represent the MDP policy. Based on the dataset generated from the multi-rule combination validation, we train the PPNN using the proposed RL algorithm with storage pool. Furthermore, we propose a backtracking mechanism to guarantee solution feasibility during the construction process. Extensive experiments demonstrate that the proposed PPM outperforms existing state-of-the-art algorithms in terms of solution quality and timeliness, and the backtracking mechanism effectively improves the scenario completion rate. The model study indicates the efficacy of our training algorithm and the generalisation of the PPNN. Additionally, our construction process is problem-tailored and more suitable for addressing UPEC than iterative search algorithms, because it effectively mitigates the impact of invalid nodes. |
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| AbstractList | Unmanned aerial vehicles (UAVs) are advanced flight systems. However, their positioning systems cause distance-dependent errors during flight. This study seeks to solve the UAV path planning problem with positioning error correction (UPEC) with an end-to-end method. Traditional methods struggle to balance solution quality and computational overload, and often have limited utilisation of scenario information. To overcome these issues, we propose a path planning model (PPM) based on deep reinforcement learning to solve the UPEC. The model has a complete structure that includes a mathematical model, feature engineering, solution process, neural policy network, scenario generation, training process, and test solution mechanism. Specifically, we first establish a Markov decision process (MDP) for UPEC and apply feature engineering with effective features to support decision-making. We then introduce a path planning neural network (PPNN) to represent the MDP policy. Based on the dataset generated from the multi-rule combination validation, we train the PPNN using the proposed RL algorithm with storage pool. Furthermore, we propose a backtracking mechanism to guarantee solution feasibility during the construction process. Extensive experiments demonstrate that the proposed PPM outperforms existing state-of-the-art algorithms in terms of solution quality and timeliness, and the backtracking mechanism effectively improves the scenario completion rate. The model study indicates the efficacy of our training algorithm and the generalisation of the PPNN. Additionally, our construction process is problem-tailored and more suitable for addressing UPEC than iterative search algorithms, because it effectively mitigates the impact of invalid nodes. |
| ArticleNumber | 112569 |
| Author | Chen, Ming Wu, Guohua Liu, Xiaolu Xing, Lining Chun, Jie Du, Yonghao Xiang, Shang |
| Author_xml | – sequence: 1 givenname: Jie surname: Chun fullname: Chun, Jie email: chunjie21@nudt.edu.cn organization: College of Systems Engineering, National University of Defense Technology, Changsha, 410073, Hunan, China – sequence: 2 givenname: Ming surname: Chen fullname: Chen, Ming email: chenming18@nudt.edu.cn organization: College of Systems Engineering, National University of Defense Technology, Changsha, 410073, Hunan, China – sequence: 3 givenname: Xiaolu orcidid: 0000-0002-5244-790X surname: Liu fullname: Liu, Xiaolu email: lxl_sunny@nudt.edu.cn organization: College of Systems Engineering, National University of Defense Technology, Changsha, 410073, Hunan, China – sequence: 4 givenname: Shang surname: Xiang fullname: Xiang, Shang email: xiangshang@xtu.edu.cn organization: School of Public Administration, XiangTan University, Xiangtan, 411100, Hunan, China – sequence: 5 givenname: Yonghao surname: Du fullname: Du, Yonghao email: duyonghao15@nudt.edu.cn organization: College of Systems Engineering, National University of Defense Technology, Changsha, 410073, Hunan, China – sequence: 6 givenname: Guohua surname: Wu fullname: Wu, Guohua email: guohuawu@csu.edu.cn organization: School of Automation, Central South University, Changsha, 410075, Hunan, China – sequence: 7 givenname: Lining surname: Xing fullname: Xing, Lining email: lnxing@xidian.edu.cn organization: College of Electronic Engineering, Xidian University, Xi’an, 710126, Shanxi, China |
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| Keywords | Deep reinforcement learning Path planning UAV Positioning error correction Reinforcement learning algorithm |
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| Snippet | Unmanned aerial vehicles (UAVs) are advanced flight systems. However, their positioning systems cause distance-dependent errors during flight. This study seeks... |
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| SubjectTerms | Deep reinforcement learning Path planning Positioning error correction Reinforcement learning algorithm UAV |
| Title | Learning to construct a solution for UAV path planning problem with positioning error correction |
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