电子测量与仪器学报

姜媛媛,谢宏达.平滑高效的U型障碍物路径规划[J].电子测量与仪器学报,2025,39(9):126-136

平滑高效的U型障碍物路径规划

Smooth and efficient U-shaped obstacle path planningJiang YuanyuanXie Hongda

DOI：

英文关键词:JPS algorithm APF algorithm U-shaped trap smooth efficient dynamic obstacle avoidance

基金项目:安徽省重点研究与开发计划(202104g01020012)、安徽省自然科学研究项目(2023AH052270)资助

作者	单位
姜媛媛	安徽理工大学电气与信息工程学院淮南232001
谢宏达	安徽理工大学电气与信息工程学院淮南232001

Author	Institution
Jiang Yuanyuan	School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Xie Hongda	School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

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中文摘要:

针对复杂U型障碍物环境中跳点搜索算法（jump point search，JPS）路径长、拐点多和人工势场法(artificial potential field，APF)陷入U型陷阱引起的路径曲折、寻路效率低等问题,提出融合改进JPS算法和APF算法(JPS*-APF)的移动机器人路径规划算法。首先,在传统JPS算法中增加角度偏差函数并删除冗余节点,减小搜索距离和转折次数；其次,改进JPS算法的拐点作为子目标点,分段引导APF算法逃出U型陷阱,自适应生成拐角障碍物斥力或动态子目标点提高路径平滑度；然后,在目标点区域添加对称虚拟障碍物解决目标不可达、融合外部斥力和重规划策略逃出局部最优,提高寻路效率；最后,适时加入相对速度斥力保证动态避障的安全性。针对不同U/L型障碍物环境进行数值仿真,结果表明,JPS*-APF算法较IA*-APF算法平均减少了51.5%的寻路时间和7.3%的路径长度，而且JPS*-APF算法路径更平滑,能有效逃出U型陷阱并提升移动机器人的工作效率;同时通过真实环境实验测试验证了JPS*-APF算法规划的可行性。

英文摘要:

Aiming at the problems of long path length and numerous turning points of jump point search (JPS) algorithm and path tortuosity with low pathfinding efficiency of path finding caused by artificial potential field (APF) falling into U-shaped trap in complex U-shaped obstacle environment. this paper proposes a mobile robot path planning algorithm that integrates an improved JPS algorithm and APF algorithm (JPS*-APF). Firstly, an angle deviation function is introduced into traditional JPS algorithm, and redundant nodes are removed to reduce search distance and turning frequency. Secondly, turning points from improved JPS algorithm are used as sub-goals, guiding APF algorithm in segments to escape U-shaped trap. Adaptive generation of repulsive forces for corner obstacles or dynamic sub-goals enhances path smoothness. Then, symmetric virtual obstacles are added to the target area to resolve target inaccessibility, while external repulsive forces and re-planning strategies are fused to escape local optima and improve pathfinding efficiency. Finally, relative velocity repulsive forces are introduced to ensure safety during dynamic obstacle avoidance. Numerical simulations in different U/L-shaped obstacle environments demonstrate that JPS*-APF algorithm reduces pathfinding time by an average of 51.5% and path length by 7.3% compared to IA*-APF algorithm. Moreover, JPS*-APF algorithm generates smoother paths, effectively escapes U-shaped traps, and enhances mobile robot’s working efficiency. The feasibility of JPS*-APF algorithm is also validated through real-world experimental tests.

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