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Updated: May 26, 2025

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准静态路径规划连续机器人通过采样隐含的多重组
1Department of Mechanical Engineering, Georgia Institute of Technology, Atlanta 30332, USA.
本研究引入了一种用于连续机器人 (CRs) 导航具有弹性障碍的复杂环境的路径规划的新方法. 与传统方法相比,新方法提高了规划成功率和计算效率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 计算几何学的计算几何学
背景情况:
- 连续机器人 (CR) 与刚性机器人相比,在有限的环境中提供更高的灵巧性和合规性.
- 现有的CR路径规划方法缺乏全面考虑外部弹性接触,这是一个关键的局限性.
- 接触CR的非独特的前向动力学和复杂的配置空间带来了重要的路径规划挑战.
研究的目的:
- 开发一个强大的准静态路径规划框架,用于连续机器人与弹性障碍物相互作用.
- 为了应对在弹性接触下的CR中未明确的前向动力学和复杂的配置空间的挑战.
- 为了使CR在具有可变形物体的环境中能够有效和可靠地导航.
主要方法:
- 模拟弹性障碍物作为影响机器人静态的外部潜在场.
- 在潜在场内制定机器人静态作为一个最佳的控制问题,以找到极端轨迹.
- 标志着稳定的机器人配置为光滑的分流器,并使用AtlasRRT*,一个基于分流器特定的采样规划器.
主要成果:
- 证明了稳定的机器人配置的集合形成了一个光滑的多元组.
- 展示了拟议的基于多元体的路径规划优于欧几里德空间规划.
- 在各种场景中的模拟中实现了更高的成功率和更高的计算效率.
结论:
- 拟议的在隐性分流器上准静态路径规划有效地处理连续机器人的弹性接触.
- 阿特拉斯RRT*提供了一个可行的解决方案,用于在复杂的配置空间中的路径规划,CRs与可变形环境相互作用.
- 这项研究推进了连续机器人的能力,用于需要与软或弹性结构相互作用的复杂任务.
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