Autonomous Obstacle Avoidance and Target Tracking of UAV Based on Deep Reinforcement Learning
When using deep reinforcement learning algorithm to complete Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance and target tracking tasks, there are often some problems such as slow convergence speed and low success rate. Therefore, this paper proposes a new deep reinforcement learning algo...
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| Vydáno v: | Journal of intelligent & robotic systems Ročník 104; číslo 4; s. 60 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Dordrecht
Springer Netherlands
01.04.2022
Springer Springer Nature B.V |
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| ISSN: | 0921-0296, 1573-0409 |
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| Abstract | When using deep reinforcement learning algorithm to complete Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance and target tracking tasks, there are often some problems such as slow convergence speed and low success rate. Therefore, this paper proposes a new deep reinforcement learning algorithm, namely Multiple Pools Twin Delay Deep Deterministic Policy Gradient (MPTD3) algorithm. Firstly, the state space and action space of UAV are established as continuous models, which is closer to engineering practice than discrete models. Then, multiple experience pools mechanism and gradient truncation are designed to improve the convergence of the algorithm. Furthermore, the generalization ability of the algorithm is obtained by giving UAV environmental perception ability. Experimental results verify the effectiveness of the proposed method. |
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| AbstractList | When using deep reinforcement learning algorithm to complete Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance and target tracking tasks, there are often some problems such as slow convergence speed and low success rate. Therefore, this paper proposes a new deep reinforcement learning algorithm, namely Multiple Pools Twin Delay Deep Deterministic Policy Gradient (MPTD3) algorithm. Firstly, the state space and action space of UAV are established as continuous models, which is closer to engineering practice than discrete models. Then, multiple experience pools mechanism and gradient truncation are designed to improve the convergence of the algorithm. Furthermore, the generalization ability of the algorithm is obtained by giving UAV environmental perception ability. Experimental results verify the effectiveness of the proposed method. When using deep reinforcement learning algorithm to complete Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance and target tracking tasks, there are often some problems such as slow convergence speed and low success rate. Therefore, this paper proposes a new deep reinforcement learning algorithm, namely Multiple Pools Twin Delay Deep Deterministic Policy Gradient (MPTD3) algorithm. Firstly, the state space and action space of UAV are established as continuous models, which is closer to engineering practice than discrete models. Then, multiple experience pools mechanism and gradient truncation are designed to improve the convergence of the algorithm. Furthermore, the generalization ability of the algorithm is obtained by giving UAV environmental perception ability. Experimental results verify the effectiveness of the proposed method. Keywords Unmanned Aerial Vehicle (UAV) * Autonomous obstacle avoidance * Target tracking * Deep reinforcement learning * Continuous control |
| ArticleNumber | 60 |
| Audience | Academic |
| Author | Wang, Yaonan Xu, Guoqiang Wang, Zhaolei Jiang, Weilai |
| Author_xml | – sequence: 1 givenname: Guoqiang surname: Xu fullname: Xu, Guoqiang organization: College of Electrical and Information Engineering, Hunan University – sequence: 2 givenname: Weilai surname: Jiang fullname: Jiang, Weilai email: jiangweilai@hnu.edu.cn organization: College of Electrical and Information Engineering, Hunan University – sequence: 3 givenname: Zhaolei surname: Wang fullname: Wang, Zhaolei organization: Science and Technology on Aerospace Intelligent Control Laboratory, Beijing Aerospace Automatic Control Institute – sequence: 4 givenname: Yaonan surname: Wang fullname: Wang, Yaonan organization: College of Electrical and Information Engineering, Hunan University |
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| Cites_doi | 10.1038/nature14236 10.1007/s10846-020-01212-1 10.1007/s10846-018-0891-8 10.3390/rs12040640 10.1109/ICARCV.2018.8581309 10.3390/rs12223789 10.1007/s10846-021-01462-7 10.1007/s10846-019-01073-3 10.1007/s10846-019-01045-7 10.1016/j.dt.2020.11.014 10.1007/s10846-012-9761-y 10.1007/s10846-018-0898-1 10.1016/j.ast.2018.10.027 10.1109/ACCESS.2020.3011211 10.1109/TVT.2018.2890773 10.1109/LCOMM.2016.2633248 10.1109/LWC.2018.2880467 10.1109/TVT.2021.3089158 10.1155/2021/5578490 |
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| Keywords | Unmanned Aerial Vehicle (UAV) Autonomous obstacle avoidance Deep reinforcement learning Target tracking Continuous control |
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| SubjectTerms | Algorithms Analysis Artificial Intelligence Control Convergence Data mining Deep learning Drone aircraft Electrical Engineering Engineering label V Machine learning Mechanical Engineering Mechatronics Obstacle avoidance Pools Regular Paper Robotics Topical collection on Robotics Vision and Intelligent Control Tracking Unmanned aerial vehicles |
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| Title | Autonomous Obstacle Avoidance and Target Tracking of UAV Based on Deep Reinforcement Learning |
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