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改进的指数和成本加权混合算法用于移动机器人路径规划

Ming Hu¹, Shuhai Jiang¹, Kangqian Zhou¹

¹School of Mechanical and Electronic Engineering, Nanjing Forestry University, 159 Longpan Road, Nanjing 210037, China.

Sensors (Basel, Switzerland)

|April 26, 2025

概括

本研究介绍了一种用于移动机器人路径规划的混合算法,改进了A*算法. 改进的方法导致更平滑,更有效的路径,复杂性降低,碰撞风险降低.

科学领域:

机器人和人工智能机器人和人工智能
路径规划算法路径规划算法

背景情况:

A*算法是移动机器人路径规划的标准,但存在一些问题,如路径不光滑和大搜索空间.
现有的算法经常与现实世界的导航复杂性作斗争,需要改进的路径查找解决方案.

研究的目的:

开发一种混合路径规划算法,将改进的A*算法与动态窗口方法 (DWA) 结合起来.
为了提高路径平滑性,减少计算复杂性,提高移动机器人导航的效率和安全性.

主要方法:

用于环境建模和信息提取的量化网格障碍数据.
实现了二级贝齐尔曲线平滑,以提高转时的路径平滑度.
改进了A*算法的启发函数和子节点选择过程.

主要成果:

混合算法显示,与A*和DWA相比,搜索效率增加了10.93%,搜索节点减少了32.26%.
路径质量得到了显著改善,包括转点减少了36.36%,转角度减少了34.83%.
总路径长度减少了22.05%,改善了整体路径平滑性,降低了碰撞概率.

结论:

拟议的混合算法为移动机器人路径规划提供了更稳定,更有效的解决方案.
通过模拟和现实世界的测试验证,该算法适用于实际移动机器人定位和导航.
这些改进解决了传统A*和DWA的关键局限性,为更强大的机器人系统铺平了道路.

关键词:

动态窗口方法动态窗口方法.混合算法是混合算法.导航导航导航导航导航路径规划路径规划路径规划

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