广西科技大学学报2026，Vol.37Issue(3)：74-84,11.DOI:10.16375/j.cnki.cn45-1395/t.2026.03.009
基于改进Quick-RRT*算法的机器人路径规划研究
Path planning for robots based on an improved Quick-RRT* algorithm
摘要
Abstract
To address the limitations of the traditional Quick-RRT* algorithm in mobile robot path planning,including high randomness,low accuracy,and slow convergence to the optimal solution,an improved Quick-RRT* path planning algorithm was proposed.Before finding an initial path,the improved algorithm adopted the target bias sampling strategy of variable sampling area to quickly bypass the obstacles and guide the random tree to grow directly toward the target,which shortened the time to find the initial path.After the initial path was found,the ellipsoid subset sampling strategy combined with beacons was used to continuously optimize the path by performing biased sampling in the ellipsoid subset constraint sampling area and near the obstacles.A hierarchical collision detection method was used to avoid repeated invalid collision detection.Then,the reconnection radius was adaptively adjusted according to the obstacles in the neighborhood of the node and the node density to improve the convergence speed.The experimental data in three two-dimensional simulation environments,that is two-dimensional obstacle-dense environment,simple maze environment,and transverse H-shaped special environment show that compared with RRT*,Quick-RRT*,RRT*-Smart,A* algorithm,genetic algorithm and particle swarm optimization algorithm,the improved algorithm achieves shorter path length,higher planning success rate and less planning time.Finally,in the three-dimensional environment simulation experiment,the practicability and reliability of the algorithm for path planning were further verified.关键词
移动机器人/路径规划/Quick-RRT*算法/启发式采样/可变采样区域/分层碰撞检测Key words
mobile robot/path planning/Quick-RRT* algorithm/heuristic sampling/variable sampling region/hierarchical collision detection分类
信息技术与安全科学引用本文复制引用
李明泽,谭光兴,黄磊昌,李春宇..基于改进Quick-RRT*算法的机器人路径规划研究[J].广西科技大学学报,2026,37(3):74-84,11.基金项目
国家自然科学基金项目(61563005)资助 （61563005）
