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根据经典路径规划算法在路径规划时的特点,发现经典路径规划算法在缩短路径长度方面性能较差,因此,提出了一种以障碍物顶点为标志的路径择优节点算法,以达到缩短无人驾驶汽车行驶里程的目的。利用Roberts算子提取障碍物边界,使用蒙特卡洛方法、层次分析法进行障碍物节点选取,并确定路径规划方向,设置安全距离函数重选路径节点,确保路径与障碍物之间保持一定安全距离。通过简单和复杂两种场景中的模拟仿真,验证了路径择优节点算法的有效性。
Abstract：According to the characteristics of classical path planning algorithms in path planning, this study finds that classical path planning algorithms perform poorly in shortening the length of the path. Therefore, a pathway merit node algorithm with obstacle vertices as markers is proposed in order to achieve the purpose of shortening the mileage of driverless vehicles. The Roberts operator is used to extract obstacle boundaries, and the Monte Carlo method and hierarchical analysis are used to carry out the obstacle node selection. Then, the path planning direction is determined, and the safe distance function is set to reselect the path nodes and ensures that a certain safe distance is maintained between the path and the obstacles. The effectiveness of the pathway merit node algorithm is verified through simulation in both simple and complex scenarios.
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基本信息:
中图分类号:U463.6
引用信息:
[1]高建鹏,高志彬.无人驾驶汽车路径择优节点算法[J].青岛理工大学学报,2026,47(04):122-130.
基金信息:
国家自然科学基金（52272311）
2024-05-23
2024
2024-05-29
2026-05-27
2026
3
2026-08-04
2026-08-04