用触觉传感实现机器人手指的精准力反馈控制
Tactile-based force estimation for interaction control with robot fingers
- 基于触觉阵列设计数据高效校准方法，快速估计复杂曲面受力
- 在100Hz控制频率下实现0.12±0.08N误差的力跟踪精度
- 适用于需要精细操作的机器人手控场景，如灵巧抓握
精细灵巧操作依赖于对机械臂-物体交互的实时感知。触觉传感阵列能提供机械臂表面丰富的接触信息，但其应用面临两大挑战：在复杂曲面（如机器人手）上实现精确的力估计，以及将这些估计结果集成到反应式控制回路中。本文提出一种数据高效的标定方法，可在不同几何形状下实现快速、全阵列的力估计，提供考虑非线性与形变效应的在线反馈。该力估计模型被用于在线闭环控制系统，实现交互力跟踪。估计精度通过校准的力-扭矩传感器独立验证。使用配备Xela uSkin传感器的Allegro手，在100Hz的阻抗控制回路下演示了精确的力施加，最大误差达0.12±0.08 [N]，展现出在灵巧操作中的良好潜力。
原文摘要 · Abstract (English)
Fine dexterous manipulation requires reactive control based on rich sensing of manipulator-object interactions. Tactile sensing arrays provide rich contact information across the manipulator's surface. However their implementation faces two main challenges: accurate force estimation across complex surfaces like robotic hands, and integration of these estimates into reactive control loops. We present a data-efficient calibration method that enables rapid, full-array force estimation across varying geometries, providing online feedback that accounts for non-linearities and deformation effects. Our force estimation model serves as feedback in an online closed-loop control system for interaction force tracking. The accuracy of our estimates is independently validated against measurements from a calibrated force-torque sensor. Using the Allegro Hand equipped with Xela uSkin sensors, we demonstrate precise force application through an admittance control loop running at 100Hz, achieving up to 0.12+/-0.08 [N] error margin-results that show promising potential for dexterous manipulation.
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