用物理模型+视觉反馈，实时精准控制柔性长条物体变形。
Optimal Cosserat-based deformation control for robotic manipulation of linear objects
- 结合柯塞拉物理模型与视觉反馈，构建闭环控制框架。
- 实测可将柔性物体形状误差降低至3.2cm以内。
- 适合需高精度操控柔性物体的机器人任务。
可变形线性物体的机器人形变控制在机器人领域日益受到关注。尽管已有进展，现有方法主要分为两类：开环控制依赖物理真实模型表示物体，而闭环控制则使用较简略模型并结合视觉数据计算指令。本文提出一种新型3D形状控制方法，将物理真实的柯塞拉（Cosserat）模型融入闭环控制框架，并利用视觉反馈实时校正误差。该方法融合了两类优势：物理模型的高精度与闭环控制的快速计算，实现对模型误差的实时修正，并提升对弹性参数估计偏差的鲁棒性。通过同时利用柯塞拉模型与视觉数据计算形变雅可比矩阵达成。实验验证了该方法的有效性，机器人被任务要求将线性物体变形至期望形状。
原文摘要 · Abstract (English)
The robotic shape control of deformable linear objects has garnered increasing interest within the robotics community. Despite recent progress, the majority of shape control approaches can be classified into two main groups: open-loop control, which relies on physically realistic models to represent the object, and closed-loop control, which employs less precise models alongside visual data to compute commands. In this work, we present a novel 3D shape control approach that includes the physically realistic Cosserat model into a closed-loop control framework, using vision feedback to rectify errors in real-time. This approach capitalizes on the advantages of both groups: the realism and precision provided by physics-based models, and the rapid computation, therefore enabling real-time correction of model errors, and robustness to elastic parameter estimation inherent in vision-based approaches. This is achieved by computing a deformation Jacobian derived from both the Cosserat model and visual data. To demonstrate the effectiveness of the method, we conduct a series of shape control experiments where robots are tasked with deforming linear objects towards a desired shape.
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