改进的RRT*路径规划算法基于移动机器人在动力学约束下的布状曲线
Kemeng Ran1, Yujun Wang1, Can Fang1
1College of Computer and Information Science, Southwest University, Chongqing 400715, China.
Sensors (Basel, Switzerland)
|December 17, 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. 改进的算法产生更快,更流,更有效的无碰撞路径,同时尊重动力学约束.
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
- 运动规划 运动规划
背景情况:
- 移动机器人导航需要高效的路径规划算法.
- 动力学约束大大复杂化了路径生成.
- 现有的算法可能缺乏速度,流性或效率.
研究的目的:
- 为移动机器人开发一个改进的路径规划算法.
- 为了快速生成高效,光滑和无碰撞的路径.
- 解决现有的路径规划方法的局限性.
主要方法:
- 使用一个增强的快速探索随机树* (RRT*) 算法.
- 纳入双向扩张战略,以更快地确定目标.
- 使用节点重新连接策略来优化路径长度和内存使用.
- 集成基于Clothoid曲线的路径变形,以避免更高水平的障碍物.
主要成果:
- 拟议的算法证明了更简单的实现和更高的计算效率.
- 实现加速路径寻找和增加模拟中的成功率.
- 与传统算法相比,生成更光滑的路径.
- 确保无碰撞路径,遵守移动机器人动力学约束.
结论:
- 增强的RRT*算法为移动机器人路径规划提供了显著的改进.
- 它为在动力学约束下导航提供了更高效,更快,更流的解决方案.
- 算法的策略增强了避难障碍和整体路径规划能力.
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