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在多障碍狭窄环境中改进了RRT*-Connect操纵器路径规划
Xueyi He1,2, Yimin Zhou2, Haonan Liu1,2
1School of Mechanical and Control Engineering, Guilin University of Technology, Guilin 541006, China.
本研究介绍了一种改进的RRT*-Connect算法,用于在复杂环境中高效地规划机器人手臂路径. 新方法显著减少了操纵器操作的规划时间和路径成本.
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:
- 在狭窄,充满障碍的环境中,机器人手臂路径规划带来了重大的计算挑战.
- 现有的算法在复杂的场景中经常与效率和最佳性作斗争.
研究的目的:
- 提出一个改进的快速探索随机树星连接 (IRRT*-Connect) 算法,用于增强机器人手臂路径规划.
- 解决在杂乱环境中采样效率,搜索加速和路径最佳性的局限性.
主要方法:
- 实施了启发式采样策略,整合了圆形子集和目标偏差采样,以压缩搜索空间.
- 引入了适应性步骤大小和节点拒绝策略,用于加速,无碰撞的路径生成.
- 应用立方非均的B-spline插值用于路径平滑和修剪优化以删除多余的节点.
主要成果:
- 与现有的算法相比,IRRT*-Connect显示了较短的规划时间.
- 拟议的方法实现了机器人操纵器操作的较低路径成本.
- 在Python/ROS中的成功模拟和UR5机器人臂上的物理实验验证实了这一方法.
结论:
- 在具有挑战性的环境中,IRRT*-Connect算法为机器人手臂路径规划提供了显著的改进.
- 增强的采样和搜索策略有助于更快,更优的路径生成.
- 这项工作为需要高效的运动规划的现实世界机器人应用提供了强大的解决方案.
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