基于改进的RRT*称为KDB-RRT的智能代理的新型路径规划方法
Wenqing Wei1,2, Kun Wei1,2, Jianhui Zhang1,2
1School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
在复杂的环境中,KDB-RRT*通过加速融合和增强路径流性,显著改善了代理路径规划. 这种新的算法可将计划时间缩短高达97.9%,路径长度缩短超过21%.
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
- 计算机科学 计算机科学
背景情况:
- 复杂环境中的路径规划面临着缓慢的融合,冗余的路径和不良平滑等挑战.
- 像RRT*这样的现有算法在复杂的设置中经常与效率和路径质量扎.
研究的目的:
- 引入KDB-RRT*,一种新的算法,旨在克服代理路径规划中现有的方法的局限性.
- 为了提高融合速度,减少路径冗余,并改善智能代理的路径流性.
主要方法:
- KDB-RRT*将双向搜索与三层优化框架集成在一起:KD树索引,重力引导的目标偏差采样和DP/B-spline轨迹优化.
- 使用SAT,R树和主动避障系统的多级碰撞检测系统确保了强大的碰撞阻力.
- 具有Sigmoid功能的自适应式步骤尺寸调整有助于方向扩展和避开障碍物.
主要成果:
- 在复杂的环境中,KDB-RRT*与优化RRT*,RRT*-Connect和信息化RRT*相比,表现优越.
- 实现了平均规划时间 (高达97.9%),路径长度 (5.5-21.4%) 和拐点 (40-90.5%) 的减少.
- 在ROS平台上验证了实际可行性.
结论:
- 在非结构化环境中,KDB-RRT*为智能代理路径规划提供了一种高效,强大的解决方案.
- 该算法的三层优化框架为移动机器人导航系统提供了宝贵的见解.
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