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| UAV Trajectory Planning Based on an Improved APF-RRT Algorithm |
| LI Juntaoa,b, CHEN Luyaob, HOU Xingxingb, ZHOU Yaqib
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| a. College of Economics and Management; b. College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China |
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Abstract In order to solve the problems of slow planning speed and large memory consumption of Path Planning Algorithm (RRT) for fast search random tree in the process of unmanned vehicle trajectory planning, an improved RRT algorithm combined with Artificial Potential Field Method (APF) is proposed. In this paper, a probability value is introduced into the random tree of the basic RRT algorithm to accelerate the convergence of the random tree to the target node, and then a gravity component is added to guide the random tree to grow to the target point to speed up the search process. At the same time, a repulsion field is established around the obstacle to limit the search area and reduce the randomness of the path. In this paper, we not only improve the RRT algorithm (APF-RRT), but also apply the above improvements to the RRT* algorithm (APF-RRT*). After a large number of simulation experiments, it is shown that the improved algorithm in this paper has a great improvement compared with the original algorithm and other latest improved RRT algorithms in terms of path length, optimization time and number of iterations.
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Published: 15 September 2026
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[1] 张妍妍,刘伟超.无人艇在边海防作战保障中的应用研究[J].水上安全,2024(8):7-9.
Zhang Y Y, Ling W C.Research on the application of unmanned boats in border and coastal defense combat support[J]. Water Safety,2024(8):7-9.
[2] 赖嘉雄,林忠华,陈晓昆,等.面向海水水质检测的无人船艏向角轨迹跟踪控制策略[J].广州航海学院学报,2024,32(2):26-32-49.
Lai J X, Lin Z H, Chen X K, et al.Heading angular trajectory tracking control strategy of unmanned ship for seawater quality detection[J]. Journal of Guangzhou Institute of Navigation,2024,32(2):26-32-49.
[3] 宋雷震,吕东芳.DDPG深度强化学习算法在无人船目标追踪与救援中的应用[J].黑龙江大学工程学报(中英俄文),2024,15(1):58-64.
Song L Z, Lü D F.Application of DDPG deep reinforcement learning algorithm in target tracking and rescue of unmanned ships[J]. Journal of Engineering of Heilongjiang University (Chinese, English and Russian),2024,15(1):58-64.
[4] 魏立新,张钰锟,孙浩,等.基于改进蚁群和DWA算法的机器人动态路径规划[J].控制与决策,2022,37(9):2211-2216.
Wei L X, Zhang Y K, Sun H, et al. Robot dynamic path planning based on improved ant colony and DWA algorithm[J]. Control and Decision-Making,2022,37(9):2211-2216.
[5] 邓向阳, 张立民, 方伟, 等. 基于双向汇聚引导蚁群算法的艇器人路径规划[J].系统仿真学报, 2022, 34(5): 1101-1108.
Deng X Y, Zhang L M, Fang W, et al.Path planning of boat-robot man based on two-way convergence guided ant colony algorithm[J]. Journal of System Simulation, 2022, 34(5): 1101-1108.
[6] Ajeil F H, Ibraheem I K, Sahib M A, et al. Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm[J].Applied Soft Computing, 2020, 89: 106076.
[7] Song B,Wang Z,Zou L. An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve[J]. Applied Soft Computing, 2021, 100: 106960.
[8] Duchoň F, Babinec A, Kajan M, et al. Path planning with modified a star algorithm for a mobile robot[J]. Procedia Engineering, 2014, 96: 59-69.
[9] Liu Y, Yan S, Zhao Y, et al. Improved dyna-Q: a reinforcement learning method focused via heuristic graph for AGV path planning in dynamic environments[J]. Drones, 2022, 6(11): 365.
[10] Chen P, Pei J, Lu W, et al. A deep reinforcement learning based method for real-time path planning and dynamic obstacle avoidance[J]. Neurocomputing, 2022, 497: 64-75.
[11] Yuan C, Liu G, Zhang W, et al. An efficient RRT cache method in dynamic environments for path planning[J]. Robotics and Autonomous Systems, 2020, 131: 103595.
[12] 李兆强, 张时雨.基于快速RRT算法的三维路径规划算法研究[J]. 系统仿真学报, 2022,34(3): 503-511.
Li Z Q, Zhang S Y.Research on three-dimensional path planning algorithm based on fast RRT algorithm[J]. Journal of System Simulation, 2022,34(3): 503-511.
[13] 李成进. 基于RRT算法的AGV路径规划方法研究[D].沈阳:沈阳工业大学,2023.
Li C J. Research on AGV path planning method based on RRT algorithm[D]. Shenyang: Shenyang University of Technology,2023.
[14] Jr J J K, LaValle S M. RRT-connect: an efficient approach to single-query path planning[DB/OL].[2024-03-21]. https://www.doc88.com/p-5621086225856.html.
[15] Adiyatov O, Varol H A. A novel RRT*-based algorithm for motion planning in Dynamic environments[C]//Proceeding of 2017 IEEE International Conference on Mechatronics and Automation. Takamatsu, Jepan: IEEE, 2017:1416-1421.
[16] 汪玥,周航,徐泽楷,等.基于改进人工势场法的路径规划研究[J].航空计算技术,2024,54(5):74-78.
Wang Y, Zhou H, Xu Z K, et al. Research on path planning based on improved artificial potential field method[J]. Aeronautical Computing Technology,2024,54(5):74-78. |
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