| 466 | 0 | 195 |
| 下载次数 | 被引频次 | 阅读次数 |
针对传统快速扩展随机树(rapidly-exploring random tree star,RRT*)算法在全局路径规划过程中存在收敛速度慢、搜索路径不平滑、内存占用多等问题，提出了一种RRT*与人工势场法(artificial potential field,APF)的融合搜索算法。为了加快RRT*算法在搜索过程中的收敛速度，在算法中利用人工势场法的思想引导扩展随机树快速向目标点生长；对融合算法在空间中的采样范围做出改进，使算法在APF产生的合力特定范围内进行采样，提高算法在空间中的搜索效率，减少无用节点的扩展。仿真结果表明：相比传统的RRT和RRT*算法以及APF-RRT融合算法，APF-RRT*融合算法能够规划出更短、更平滑的路径，路径距离缩短了1.5%～10.83%；算法的搜索时间也显著缩短了1.97%～49.78%；与其他算法相比，APF-RRT*融合算法的路径节点数量减少了4.66%～41.95%，路径平滑性也得到了提高。
Abstract：To address the problems of slow convergence speed, unsmooth search paths and high memory usage in the global path planning process of the traditional rapidly-exploring random tree star algorithm(RRT*), a fusion search algorithm that integrates RRT*with the Artificial Potential Field(APF) method was proposed. To speed up the convergence of the RRT*algorithm in the search process, the principle of APF was used to guide the extended random tree to grow rapidly toward the target point. The sampling range of the fusion algorithm in space was improved, so that the algorithm can sample within a specific range of the net force generated by APF, improve the search efficiency of the algorithm in space, and reduce the expansion of useless nodes. The simulation results show that the APF-RRT* fusion algorithm outperforms traditional RRT and RRT* algorithms, as well as the APF-RRT fusion algorithm, by generating shorter and smoother paths, with path distances reduced by 1.5% to 10.83%. Moreover, the search time of the algorithm is also significantly shortened by 1.97% to 49.78%. In addition, compared with the other algorithms, the number of path nodes of the APF-RRT* fusion algorithm is reduced by 4.66% to 41.95%, and the path smoothness is also improved.
[1] ZHANG H,XIN B,DOU L H,et al. A review of cooperative path planning of an unmanned aerial vehicle group[J]. Frontiers of Information Technology&Electronic Engineering,2020,21(12):1671-1694.
[2] ZHOU Y M,SU Y,XIE A H,et al. A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV[J]. Chinese Journal of Aeronautics,2021,34(9):199-209.
[3] LUO Y Z,DING W R,ZHANG B C,et al. Optimization of bits allocation and path planning with trajectory constraint in UAV-enabled mobile edge computing system[J]. Chinese Journal of Aeronautics,2020,33(10):2716-2727.
[4] WANG X H,MA P,WANG H,et al. Dynamic A*path finding algorithm and 3D lidar based obstacle avoidance strategy for autonomous vehicles[J]. High Technology Letters,2020,26(4):383-389.
[5]常路，单梁，戴跃伟，等.未知环境下基于改进DWA的多机器人编队控制[J].控制与决策，2022,37(10):2524-2534.
[6] ZHANG G Y,WANG H B,ZHAO W,et al. Application of improved multi-objective ant colony optimization algorithm in ship weather routing[J]. Journal of Ocean University of China,2021,20(1):45-55.
[7]魏彤，龙琛.基于改进遗传算法的移动机器人路径规划[J].北京航空航天大学学报，2020,46(4):703-711.
[8]闫振.移动机器人路径规划算法分析[J].传感器世界，2023,29(8):1-8,20.
[9] KUFFNER J J,LAVALLE S M. RRT-connect:an efficient approach to single-query path planning[C]//Proceedings 2000ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. USA:IEEE,2000:995-1001.
[10]黄肖文，任彦.基于APF-RRT融合算法的无人机航迹规划研究[J].绿色科技，2020(6):220-221,225.
[11]辛鹏，王艳辉，刘晓立，等.优化改进RRT和人工势场法的路径规划算法[J].计算机集成制造系统，2023,29(9):2899-2907.
[12]苗红霞，陈家林，齐本胜，等.基于改进RRT和人工势场法的路径规划算法[J].自动化与仪器仪表，2023(12):9-14.
[13]KARAMAN S,FRAZZOLI E. Sampling-based algorithms for optimal motion planning[J]. The International Journal of Robotics Research,2011,30(7):846-894.
[14]JEONG I B,LEE S J,KIM J H. Quick-RRT*:triangular inequality-based implementation of RRT*with improved initial solution and convergence rate[J]. Expert Systems with Applications,2019,123:82-90.
[15] ISLAM F,NASIR J,MALIK U,et al. RRT-Smart:rapid convergence implementation of RRT towards optimal solution[C]//2012 IEEE International Conference on Mechatronics and Automation. Chengdu:IEEE,2012:1651-1656.
[16]陈晋音，胡可科，李玉玮.基于MB-RRT*的无人机多点航迹规划算法研究[J].计算机科学，2018,45(S1):85-90.
[17] NOREEN I,KHAN A,HABIB Z. A comparison of RRT,RRT*and RRT*-smart path planning algorithms[J]. International Journal of Computer Science and Network Security(IJCSNS),2016,16(10):20.
基本信息:
DOI：10.19573/j.issn2095-0926.202403008
中图分类号:TP242
引用信息:
[1]杨勇,成英.融合RRT~*与APF算法的机器人路径规划研究[J].天津职业技术师范大学学报,2024,34(03):49-56.DOI:10.19573/j.issn2095-0926.202403008.
基金信息:
天津市教委科研计划项目（2020KJ122）