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arXiv 2602.01085cs.RO
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从形状估计变形线性物体的力相互作用

Estimating Force Interactions of Deformable Linear Objects from their Shapes

Nanyang Technological University, School of Mechanical and Aerospace Engineering(南洋理工大学机械与航空航天工程学院);University of Illinois Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校);Eureka Robotics

Qi Jing Chen, Shilin Shan, Timothy Bretl, Quang-Cuong Pham

AI总结:

本文提出了一种基于线缆形状信息的力估计方法,无需额外传感器即可准确识别变形线性物体的外部力相互作用。

摘要:

本工作介绍了一种分析方法,用于通过仅观察到的形状来检测和估计作用在变形线性物体(DLOs)上的外部力。在许多机器人-线缆交互任务中,接触并不发生在末端执行器,而是在机器人身体的其他点上。这种情况发生在机器人间接操作线缆(例如通过推挤)或线缆作为环境中被动障碍物时。准确识别这些相互作用对于安全和高效的轨迹规划至关重要,有助于防止线缆损坏、避免受限的机器人运动,并减轻潜在的危险。现有方法通常依赖于昂贵的外部力矩传感器或假设接触发生在末端执行器上以获得准确的力估计。使用从深度相机获取的线缆形状信息,并在假设线缆处于或接近静态平衡状态的假设下,我们的方法在不使用额外先验知识的情况下估计了外部力的位置和大小。这是通过利用推导出的一致性条件,并基于线缆上的力矩平衡解决线性方程组来实现的。该方法通过模拟验证,其中实现了高精度,以及通过现实世界实验,其中在选定的交互场景中展示了准确的估计。

英文摘要:

This work introduces an analytical approach for detecting and estimating external forces acting on deformable linear objects (DLOs) using only their observed shapes. In many robot-wire interaction tasks, contact occurs not at the end-effector but at other points along the robot's body. Such scenarios arise when robots manipulate wires indirectly (e.g., by nudging) or when wires act as passive obstacles in the environment. Accurately identifying these interactions is crucial for safe and efficient trajectory planning, helping to prevent wire damage, avoid restricted robot motions, and mitigate potential hazards. Existing approaches often rely on expensive external force-torque sensor or that contacts occur at the end-effector for accurate force estimation. Using wire shape information acquired from a depth camera and under the assumption that the wire is in or near its static equilibrium, our method estimates both the location and magnitude of external forces without additional prior knowledge. This is achieved by exploiting derived consistency conditions and solving a system of linear equations based on force-torque balance along the wire. The approach was validated through simulation, where it achieved high accuracy, and through real-world experiments, where accurate estimation was demonstrated in selected interaction scenarios.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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