研讨会 AGV路径规划基于改进的 A* 算法
Na Liu1, Chiyue Ma1, Zihang Hu1
1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
Mathematical biosciences and engineering : MBE
|March 8, 2024
概括
这项研究增强了数字车间中自动引导车辆 (AGV) 的A*算法,显著减少路径转和旅行时间,以提高物流效率.
科学领域:
- 机器人和自动化 机器人和自动化
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 自动引导车辆 (AGV) 对于数字化生产车间的物流至关重要.
- 传统的A*算法面临的挑战是过度的路径转和长时间的运输时间.
- 优化AGV路径规划对于提高车间效率至关重要.
研究的目的:
- 在数字研讨会中提出改进的A*算法用于AGV路径规划.
- 解决过度转和延长过境时间的问题.
- 提高 AGV 的物流路径的整体质量.
主要方法:
- 对A*算法的内部改进:增强节点搜索,对角距离启发式和转向权重.
- 外部优化:改进了前向搜索和贝塞尔曲线方法,应用于A*输出路径.
- 与六种传统路径规划方法进行比较分析.
主要成果:
- 内部改进的A*算法超过了六种常规方法.
- 完全改进的A*算法将转角度减少约69%,路径长度减少约10%.
- 与传统的A*算法相比,AGV运行时间减少了12秒,提高了物流效率.
结论:
- 提议的增强的A*算法显著提高了数字车间的AGV路径质量.
- 该方法有效地减少了路径转和长度,从而缩短了旅行时间.
- 这种优化提高了整体物流效率,并降低了自动化生产环境中的运营成本.
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