AGV的全球动态路径规划基于改进的A*算法和动态窗口方法的融合.
Te Wang1, Aijuan Li1, Dongjin Guo2
1School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究引入了一种新的融合算法,用于自动引导车辆 (AGV) 路径规划和障碍回避. 改进的A*和动态窗口方法组合提高了动态环境中的效率和安全性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 自动引导车辆 (AGV) 需要高效的全球路线规划和实时的动态障碍回避.
- 传统的算法在复杂,动态的环境中面临着难以预测的障碍.
研究的目的:
- 开发一个融合算法,结合改进的A*算法和动态窗口方法,用于AGV全球最佳路径规划和动态避障.
- 提高路线规划效率,减少搜索范围,提高避障能力.
主要方法:
- 动态加权A*算法的启发式函数,以缩小搜索范围.
- 实施路径优化方法以消除冗余节点和转折点.
- 将改进的A*算法与动态窗口方法集成在一起,以在全球路径中避免局部动态障碍.
主要成果:
- 与传统的A*算法相比,改进的A*算法显示,计划时间减少了26.3%,搜索范围减少了57.9%.
- 路径长度减少了7.2%,路径节点 (85.7%) 和转折点 (71.4%) 显著减少.
- 融合算法成功地在不同地图环境中实时避开移动和未知的静态障碍.
结论:
- 拟议的融合算法有效地解决了AGV的全球路径规划和动态避障需求.
- 增强的A*算法显著提高了规划效率和路径质量.
- 综合方法可确保在复杂,动态的环境中实时导航和避开障碍物.
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