用混合力-位置策略实现柔性线状物体精准形状控制
A Hybrid Force-Position Strategy for Shape Control of Deformable Linear Objects With Graph Attention Networks
- 结合力空间规划与位置空间模型预测控制
- 在仿真与实测中均实现稳定高效的形状控制
- 基于图注意力网络提升动态预测精度，适合机器人操作场景
柔性线状物体（如电线、电缆）的操控在电子装配、医疗手术等应用中至关重要。但由于其无限自由度、复杂的非线性动力学以及系统的欠驱动特性，操控面临挑战。本文提出一种混合力-位置策略用于柔性线状物体的形状控制。该框架融合力与位置双重表征，采用力空间中的状态轨迹规划和位置空间中的模型预测控制（MPC）。设计了一种包含显式动作编码器、属性提取器和基于图注意力网络（Graph Attention Networks）的图处理器的动力学模型，并用于MPC以提升预测精度。仿真与真实实验结果表明，该方法能有效实现柔性线状物体的高效且稳定的形状控制。代码与视频见 https://sites.google.com/view/dlom。
原文摘要 · Abstract (English)
Manipulating deformable linear objects (DLOs) such as wires and cables is crucial in various applications like electronics assembly and medical surgeries. However, it faces challenges due to DLOs' infinite degrees of freedom, complex nonlinear dynamics, and the underactuated nature of the system. To address these issues, this paper proposes a hybrid force-position strategy for DLO shape control. The framework, combining both force and position representations of DLO, integrates state trajectory planning in the force space and Model Predictive Control (MPC) in the position space. We present a dynamics model with an explicit action encoder, a property extractor and a graph processor based on Graph Attention Networks. The model is used in the MPC to enhance prediction accuracy. Results from both simulations and real-world experiments demonstrate the effectiveness of our approach in achieving efficient and stable shape control of DLOs. Codes and videos are available at https://sites.google.com/view/dlom.
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