动态受约束的RRT算法与后路点转移用于轮式移动机器人的最短路径规划
Sisi Liu1, Zhan Zhao1, Jun Wei1
1School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
本研究介绍了一种改进的快速探索随机树 (RRT) 算法,用于移动机器人路径规划. 这种新的方法优化了使用Dubins曲线后生成的路径,提高了效率并减少了路径长度.
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:
- 轮式移动机器人带动力学约束的路径规划具有挑战性.
- 传统的快速探索随机树 (RRT) 算法可能是低效和低于最佳的.
- 需要强大的路径规划方法来处理动态环境和约束.
研究的目的:
- 开发一个增强的RRT算法,以实现轮式移动机器人的高效和最佳路径规划.
- 与现有的RRT方法相比,提高路径质量和降低计算复杂性.
- 将避免障碍物纳入优化路径规划框架.
主要方法:
- 一个修改后的RRT算法,使用Dubins曲线进行后向点转移优化.
- 在树木生长过程中基于动力学约束的父节点的选择.
- 通过利用现有的路径信息,避免未知的障碍物的新方法.
主要成果:
- 拟议的方法比传统的RRT产生更短和更优的路径.
- 在采样点,树节点和路径节点显著减少.
- 有效地避免未知障碍,提高规划效率.
结论:
- 增强的RRT算法与Dubins曲线优化为移动机器人路径规划提供了卓越的性能.
- 该方法是高效的,收良好,并且不那么依赖初始路径条件.
- 集成的避开障碍进一步提高了算法的实际适用性.
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