自主机器人的轨迹优化和障碍回避使用强大而高效的快速探索随机树
Naeem Ul Islam1, Kaynat Gul2, Faiz Faizullah2
1Department of Computer Science and Engineering and (IBPI), Yuan Ze University, Taoyuan City, R.O.C (Taiwan).
PloS one
|October 11, 2024
概括
本研究介绍了用于自动驾驶车辆导航的强大高效RRT* (RE-RRT*) 算法. RE-RRT*通过减少搜索时间和提高复杂环境中的路径效率来增强运动规划.
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
- 自主系统 自主系统
背景情况:
- 运动规划是机器人技术中的一个关键挑战,特别是对于自动驾驶汽车.
- 现有的基于采样的算法具有诸如缓慢的融合和高计算复杂性等局限性.
- 这些局限性阻碍了复杂环境中的高效航行.
研究的目的:
- 为了解决当前基于采样的运动规划算法的局限性.
- 提出一个新的算法,RE-RRT*,用于稳健和高效的路径查找.
- 改进自动驾驶汽车的轨迹规划和障碍回避.
主要方法:
- 开发了强大而高效的RRT* (RE-RRT*) 算法,这是一种基于采样的新路径查找方法.
- 从初始点到目标点的位移处实施采样,以限制采样空间.
- 使用Select Parent和Rewire进程进行连续路径优化.
主要成果:
- RE-RRT*显示了更快的融合到更短的路径与更少的代.
- 该算法显著减少了冗余搜索,并提高了采样空间效率.
- 实验结果显示,在计算时间,速度和稳定性方面,比现有方法性能优越.
结论:
- 该RE-RRT*算法有效地克服了传统采样方法的局限性.
- 这种方法为自动驾驶汽车运动规划提供了更高效,更稳定的解决方案.
- 拟议的方法增强了复杂,障碍繁多的环境中的导航能力.
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