摘要
移动机器人在复杂环境中,利用传统A^(*)算法进行路径规划时,往往搜索效率低、转折点多、路径不平滑,且无法有效应对动态障碍物。本文提出了一种基于改进A^(*)与TEB算法融合的方案。通过设置虚拟膨胀区域、改进启发函数以及优化拐点选取策略,提高了算法的搜索效率与安全性,然后在全局最优的前提下融合TEB算法,实现移动机器人的动态路径规划。实验验证,融合算法能够有效提高搜索效率,实现路径平滑及动态避障,且满足阿克曼机器人的约束要求,具有良好的可行性与适应性。
When mobile robots use traditional A^(*) algorithm to carry out path planning in complex environment, they often have low search efficiency, many turning points, uneven path and can not effectively deal with dynamic obstacles. This paper presents a scheme based on the fusion of improved A^(*) and TEB algorithm. The search efficiency and security of the algorithm are improved by setting up virtual expansion region, improving heuristic function and optimizing inflection point selection strategy. Then, the dynamic path planning of mobile robot is realized by integrating the TEB algorithm under the premise of global optimization. Experiments verify that the fusion algorithm can effectively improve the search efficiency, achieve path smoothing and dynamic obstacle avoidance, and meet the constraints of the Ackerman robot, with good feasibility and adaptability.
作者
徐嘉骏
辛绍杰
邓寅喆
XU Jiajun;XIN Shaojie;DENG Yinzhe
出处
《计量与测试技术》
2022年第5期26-30,共5页
Metrology & Measurement Technique
