农业工程学报2026，Vol.42Issue(3)：13-25,13.DOI:10.11975/j.issn.1002-6819.202509219
RRT＊-GSQ:一种适用于复杂果园场景的混合采样路径规划算法
RRT＊-GSQ:A hybrid sampling path planning algorithm for complex orchard scenarios
摘要
Abstract
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT*-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT*,the proposed algorithm reduced the number of node evaluations by 67.57％and 62.72％,and decreased the search time by 79.72％and 78.52％,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT*.Specifically,the lateral RMSE was reduced by 41.5％in obstacle-free environments and 59.3％in obstructed environments,while the longitudinal RMSE was reduced by 57.2％and 58.5％,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6％(obstacle-free)and 58.3％(with obstructed),alongside a 5.1％and 7.2％shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.关键词
机器人/路径规划/果园/改进RRT*算法/高斯采样/自主导航Key words
robot/path planning/orchard/improved RRT* algorithm/Gaussian sampling/autonomous navigation分类
农业科技引用本文复制引用
祝清震,赵家沐阳,戴旭,余杨..RRT＊-GSQ:一种适用于复杂果园场景的混合采样路径规划算法[J].农业工程学报,2026,42(3):13-25,13.基金项目
National Natural Science Foundation of China(32301712) （32301712）
Natural Science Foundation of Jiangsu Province(BK20230548 （）
BK20250876) （）
Project of Faculty of Agricultural Equipment of Jiangsu University(NGXB20240203) （NGXB20240203）
A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-2023-87) （PAPD-2023-87）
Open Funding Project of the Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University),Ministry of Education(MAET202101). （Jiangsu University）
