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使用多策略协作双向RRT*算法的机器人武器避障路径规划
Xiangchen Ku1, Erzhou Zhu1, Sen Li1
1School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了一种改进的RRT*算法,用于更快,更流的路径规划. 改进的方法显著减少了复杂的3D环境中的规划时间,路径节点和路径长度.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能和机器学习
- 计算几何学的计算几何学
背景情况:
- 传统的RRT*算法面临的挑战包括采样偏差,缓慢的融合,低效的路径搜索和不良的路径平滑性.
- 这些局限性阻碍了复杂环境中的有效路径规划.
- 解决这些问题对于推进自主系统至关重要.
研究的目的:
- 提出一个改进的RRT*算法,克服传统方法的局限性.
- 提高路径规划效率,融合速度和路径质量 (平滑,长度,安全性).
- 为3D环境提供卓越的路径规划解决方案.
主要方法:
- 实施了动态圆样采样策略,用于适应性探索路径空间.
- 使用双向RRT*算法,具有动态目标偏差抽样和加速融合的启发式搜索.
- 集成了一种改进的人工潜力场方法,用于定向指导,并使用带有立方B-spline插曲的路径修剪来平滑路径.
主要成果:
- 与现有算法相比,改进的RRT*算法显著减少了规划时间 (58-90%) 和路径节点 (31-91%).
- 在简单和复杂的3D环境中实现了8-20%的平均路径长度减少.
- 最终生成的轨迹呈现出连续的曲率,适合于实际的跟踪应用.
结论:
- 提议的增强RRT*算法在路线规划效率和质量方面提供了实质性的改进.
- 动态采样,双向搜索,目标偏差和光滑技术的结合导致了卓越的性能.
- 该算法为复杂的3D路径规划任务提供了强大而高效的解决方案.
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