用视觉反馈实时校正物理模型误差，实现柔性线状物3D形状精准控制。
Closed-loop shape control of deformable linear objects based on Cosserat model
- 融合柯西-拉格朗日模型与视觉反馈的闭环控制
- 实现实时误差修正与弹性参数不敏感的鲁棒控制
- 适合需要高精度柔性物体操控的机器人任务
柔性线状物体的机器人形状控制在机器人领域受到越来越多关注。尽管近期取得进展，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，闭环控制则使用较简略模型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，将物理真实的柯西-拉格朗日（Cosserat）模型融入闭环控制框架，利用视觉反馈实时修正误差。该方法结合了两类方法的优势：物理模型的精确性与视觉方法的快速计算，实现模型误差的实时修正，并对弹性参数估计不敏感。通过同时基于柯西-拉格朗日模型和视觉数据计算形变雅可比矩阵实现。为验证方法有效性，我们在一系列实验中让机器人将线状物体变形至目标形状。
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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