基于改进的RRT星和TEB算法的移动机器人的路线规划
Xiong Yin1, Wentao Dong1, Xiaoming Wang1
1School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330000, China.
Scientific reports
|April 18, 2024
概括
本研究介绍了一种用于移动机器人路径规划的新型融合算法. 改进的算法大大减少了路径长度和转折点,确保了顺的,避免障碍的轨迹,尊重机器人动力学.
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:
- 移动机器人路径规划对于自主导航至关重要.
- 像RRT*这样的传统算法可能会遭受低于最佳的路径和动力学限制.
- 确保流,高效和动力学可行的轨迹是一个关键的挑战.
研究的目的:
- 开发和评估一个融合算法,将一个增强的RRT*用于全球路径生成和一个优化的TEB用于本地轨迹规划.
- 为了提高路径的最佳性,减少路径的长度和转折点,提高路径的平滑性.
- 确保生成的轨迹符合移动机器人的动力学约束.
主要方法:
- 一个增强的快速探索随机树恒星 (RRT*) 算法,具有适应性采样和节点偏差,用于加速全球路径生成和减少本地最佳性.
- 消除路径冗余和转角度约束,以最大限度地减少路径长度和提高流性.
- 移动机器人的动态建模和优化轨迹编码捆绑 (TEB) 算法,以获得动态可行的本地轨迹.
主要成果:
- 与传统的RRT*相比,改进的RRT*算法减少了5.8%的路径长度和62.5%的转折点数量*.
- 融合算法成功地生成了全球最佳的,平滑的路径,有效地绕过障碍物.
- 融合算法产生的局部轨迹与移动机器人的动力约束精确匹配.
结论:
- 拟议的融合算法为移动机器人路径规划提供了一种优越的方法.
- 改进的RRT*和优化的TEB集成可使机器人导航更高效,更流,更符合动力学要求.
- 这种方法在复杂的路径规划任务中,比传统的RRT*显著改进.
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