A*-TEB:一个改进的A*算法,基于多机器人运动规划的TEB策略
Xu Li1, Tuanjie Li1,2, Yan Zhang2
1College of Information Engineering, Tarim University, Alar City 843300, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究引入了一种新的多机器人运动规划框架,可以增强A*和定时弹性带算法. 综合方法提高了路径效率,并减少了动态环境中的完成时间.
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
- 控制系统 控制系统
背景情况:
- 多机器人运动规划 (MRMP) 需要强大的本地规划和全球一致性,目前的方法往往无法平衡.
- 由于缺乏同时进行全球和本地规划,现有的方法在实时路径执行冲突中扎.
- 对于全球路径规划,A*算法是最受欢迎的,而定时弹性带 (TEB) 算法在本地轨迹优化和动态避障方面表现出色.
研究的目的:
- 开发一种新的运动规划框架,以协作方式解决多机器人系统的全球和本地规划.
- 以提高方向成本和动态权重来增强A*算法,以提高路径流性和效率.
- 改进TEB算法,使用层次障碍处理来实现优越的局部避开能力.
主要方法:
- 整合了改进的A*算法 (带有转向成本和动态权重) 和增强的TEB战略.
- 在TEB算法中实现层次障碍处理,以进行精细的本地路径调整.
- 通过使用机器人操作系统 (ROS) 的模拟和现实实验进行验证.
主要成果:
- 拟议的框架显示了与传统的A*算法相比的显著改进.
- 实现了平均路径长度减少5.2%,完成时间减少11.5%.
- 减少了66.7%的拐点,表明更平稳,更有效的路径.
结论:
- 新的框架有效地整合了多机器人系统的全球和地方规划.
- 与传统方法相比,增强的A*-TEB方法在动态环境中提供了卓越的性能.
- 该方法在现实世界多机器人应用中是可行的和有效的.
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