FRRT*-Connect:一种基于采样的双向路径规划器,可为复杂的障碍环境提供潜在的现场指导
Wenshan Yan1, Xiangrong Xu1, Aleksandar Rodić2
1School of Mechanical Engineering, Anhui University of Technology, Ma'anshan 243032, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
本研究介绍了一种改进的FRRT*-Connect算法,用于在复杂环境中高效的路径规划. 改进的方法显著提高了合速度和路径质量,在狭窄的通道中达到98%的成功率.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 计算几何学的计算几何学
- 算法设计 算法设计
背景情况:
- 在高维,复杂的环境中路径规划仍然是一个重大挑战.
- 传统的基于RRT的算法经常遭受缓慢的融合,路径冗余和局部最小值问题.
- 对于在复杂的空间中运行的自主系统来说,高效和强大的路径生成是至关重要的.
研究的目的:
- 建议改进FRRT*-Connect算法,以提高路径规划效率,精度和稳定性.
- 解决现有的基于RRT的算法在复杂和高维环境中的局限性.
- 提高在狭窄通道和复杂障碍的场景中路径生成的性能.
主要方法:
- 引入一个以目标为导向的吸引力力控制机制,使用人工潜在场来引导树木的扩张.
- 实施一个适应性的步骤大小策略,以根据环境复杂性动态调整扩张.
- 将这些改进纳入RRT*-Connect框架,以改善采样和趋同.
主要成果:
- 改进的FRRT*-Connect算法显示,平均路径长度减少了19.7%.
- 与标准RRT*-Connect算法相比,收速度提高了58.4%.
- 在狭窄通道场景中实现了98%的路径规划成功率,有效地避免了局部最小值.
结论:
- 拟议的FRRT*-Connect算法大大克服了基于RRT的传统方法的局限性.
- 这些改进带来了在路径质量,融合效率和稳定性方面更好的性能.
- 这种改进的算法为在具有挑战性的高维环境中路径规划提供了更有效的解决方案.
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