基于改进的快速探索随机树的自动引导车辆 (AGV) 路径优化方法
Zhigang Ren1,2, Anjiang Cai1, Feilong Xu2
1School of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了一种改进的快速探索随机树 (RRT) 算法,用于自动引导车辆 (AGV) 路径规划. 增强的RRT算法显著减少了路径长度和规划时间,为实际的AGV应用提供了卓越的性能.
科学领域:
- 机器人和自动化机器人与自动化
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 传统的快速探索随机树 (RRT) 算法在计算效率,融合速度,路径曲率和自动引导车辆 (AGV) 路径规划的本地最佳性方面面临挑战.
- 现有的RRT变体经常在动态环境中努力平衡规划质量与计算成本.
研究的目的:
- 通过解决效率,收和路径质量方面的局限性,提高RRT算法用于AGV路径规划的性能.
- 开发一个改进的RRT算法,集成自适应式步骤大小优化和K维树 (KD-Tree) 以实现更快,更流的路径生成.
主要方法:
- 实施适应性步骤大小优化策略,以动态调整搜索参数,提高节点选择和规划效率.
- 整合K维树 (KD-Tree) 以加速搜索最近邻居,显著减少路径规划计算时间.
- 应用立方线间波来平滑生成的最佳路径,提高整体路径质量.
主要成果:
- 与传统的RRT,RRT*和Informed-RRT*相比,改进的RRT算法显示出更高的性能.
- 实现了平均路径长度 (164.33 m) 和平均搜索时间 (3.3 s) 的显著减少.
- 观察到改善了路径流性和规划质量,使得该算法更适合于现实世界的AGV部署.
结论:
- 提议的改进的RRT算法有效地克服了AGV路径规划的传统RRT方法的局限性.
- 适应性步骤大小优化,KD树搜索和路径平滑的组合为实际的AGV导航提供了强大而高效的解决方案.
- 该算法的提高效率和路径质量使其成为现实世界自动驾驶汽车系统的有希望的方法.
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