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无人驾驶渡轮车的路径规划算法研究,结合改进的A*和DWA

Zhaohong Wang¹, Gang Li¹

¹School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China.

Sensors (Basel, Switzerland)

|July 13, 2024

概括

本研究介绍了无人驾驶渡轮车的改进路径规划算法,集成A*和动态窗口方法 (DWA) 以有效地避开障碍物并防止局部优化. 这种新的方法确保了实时导航和复杂环境中的适应性.

科学领域:

机器人技术和自主系统
人工智能的人工智能
导航和控制导航和控制

背景情况:

全球路径规划算法与未知的动态和静态障碍作斗争.
在大型环境中,本地规划算法经常面临本地优化问题.
无人驾驶渡轮车需要强大的和高效的路线规划,以确保安全运行.

研究的目的:

为无人驾驶渡轮车辆开发一个综合路径规划算法,将A*和动态窗口方法 (DWA) 结合起来.
增强避障能力,特别是对于未知的动态和静态障碍.
解决传统算法的局限性,包括局部优化和路径低效.

主要方法:

改进了A*算法,增强了启发式函数 (矢量角共相) 和优化了搜索社区.
使用立方半均的B线曲线平滑路径,以减少转折点.
模糊控制理论集成到DWA中,用于动态调整评估函数权重.
融合了改进的A*用于全球规划和改进的DWA用于实时避免当地障碍.

主要成果:

集成的算法成功地避免了实时未知的动态和静态障碍.
该系统实现了全球最佳路径,同时确保了有效的本地导航.
模拟结果证明了算法的有效性和适应性在各种环境图.

结论:

拟议的A*-DWA融合算法为无人驾驶渡轮车的路径规划提供了高效和强大的解决方案.
对A*和DWA的增强有效地减轻了它们各自的限制.
该算法提供实时避难障碍和适应性,这对于自主导航至关重要.

关键词:

融合算法融合算法模糊的控制模糊的控制改进了A*算法改善了DWA的使用情况.路径规划路径规划路径规划

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