基于四层网络成本结构图的狭窄道路场景中的路径规划
Ping Wang1,2, Hao Zhang1, Youming Tang3
1School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究引入了一种新的路径规划方法,用于在狭窄的空间中使用自动驾驶汽车 (AGV). 这种方法通过将Voronoi骨集成到成本图中来提高安全性和效率,从而改善了AGV导航.
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:
- 在狭窄的环境中,自动引导车辆 (AGV) 的路径规划面临着安全距离和路径平滑性的挑战.
- 传统的成本映射方法难以充分解决狭窄空间的复杂性.
研究的目的:
- 开发一个改进的路径规划算法,专门设计用于狭窄道路场景的AGV.
- 为了提高AGV导航在受限制环境中的安全性,效率和稳定性.
主要方法:
- 基于Voronoi骨架的自定义层与传统成本图集成,以创建一个四层网络成本图.
- 应用改进的A*算法用于全球路径规划和B-spline平滑用于路径优化.
- 提取关键的沃罗诺伊骨架节点,以生成一个定制层,以准确地区分障碍物影响.
主要成果:
- 在狭窄道路场景中,AGV路径规划安全性得到了82%的改善.
- 空间转折点的数量减少了55.85%,导致轨迹更平滑.
- 路径规划时间缩短了48.98%,提高了实时性能.
结论:
- 拟议的方法显著提高了在狭窄的道路上AGV路径规划的稳定性和实时性能.
- 综合的沃罗诺伊骨架和成本图方法确保了更安全和更优的AGV运动.
- 这种算法为有限的工业环境中复杂的AGV导航挑战提供了可行的解决方案.
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