用双臂机器人通过三次正交抓取主动降低物体位姿不确定性。
Bimanual Regrasp Planning and Control for Active Reduction of Object Pose Uncertainty
- 双机械臂通过三次正交抓取，利用平面指垫接触约束物体位姿。
- 实验中位姿偏差与光学追踪系统相当，重复性良好。
- 无需相机或夹具，适合精密抓取场景。
由于物体位姿存在不确定性，精确抓取极具挑战。传统方法依赖相机和夹具，虽有效但需大量准备，如根据物体几何设计夹具并用激光工具高精度校准相机。本文提出一种不使用夹具或相机的方法，通过平行夹爪的平面指垫在开合方向上实现表面接触，从而减少不确定性。三个正交抓取动作联合约束物体的位置与姿态至唯一状态。基于此，我们开发了双臂重抓取规划与阻抗控制策略，依次寻找并利用两机械臂的三次正交抓取，主动降低物体位姿不确定性。我们在不同初始不确定性条件下进行了验证，结果表明该方法具有良好的重复性。实验中各次尝试的偏差量级与光学追踪系统相当，证明其具备出色的相对定位性能。
原文摘要 · Abstract (English)
Precisely grasping an object is a challenging task due to pose uncertainties. Conventional methods have used cameras and fixtures to reduce object uncertainty. They are effective but require intensive preparation, such as designing jigs based on the object geometry and calibrating cameras with high-precision tools fabricated using lasers. In this study, we propose a method to reduce the uncertainty of the position and orientation of a grasped object without using a fixture or a camera. Our method is based on the concept that the flat finger pads of a parallel gripper can reduce uncertainty along its opening/closing direction through flat surface contact. Three orthogonal grasps by parallel grippers with flat finger pads collectively constrain an object's position and orientation to a unique state. Guided by the concepts, we develop a regrasp planning and admittance control approach that sequentially finds and leverages three orthogonal grasps of two robotic arms to actively reduce uncertainties in the object pose. We evaluated the proposed method on different initial object uncertainties and verified that it had good repeatability. The deviation levels of the experimental trials were on the same order of magnitude as those of an optical tracking system, demonstrating strong relative inference performance.
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