基于初始滑移的视觉触觉感知在手内物体旋转过程中的旋转测量
Incipient Slip-Based Rotation Measurement via Visuotactile Sensing During In-Hand Object Pivoting
AI总结：
本文针对手内物体旋转任务中初始滑移导致旋转测量误差的问题，提出基于粘着区域线特征的二维接触模型与旋转测量方法，在Tac3D传感器上实现无需训练数据的实时高精度测量。
摘要：
在以物体旋转为代表的典型手内操作任务中，实时感知旋转滑移已被证明有利于提高机器人手的灵巧性和稳定性。一种有效的策略是通过视觉触觉感知获取接触特性，从而测量旋转角度。然而，现有的旋转估计方法未考虑旋转过程中初始滑移的影响，这会引入测量误差，并使稳定接触与宏观滑移之间的边界难以确定。本文描述了旋转条件下的一种广义二维接触模型，并提出了一种基于粘着区域中线特征的旋转测量方法。该方法被应用于采用连续标记图案的Tac3D视觉触觉传感器上。实验表明，该旋转测量系统可实现平均静态测量误差0.17度、平均动态测量误差1.34度。此外，所提方法无需训练数据，并能在手内物体旋转过程中实现实时感知。
英文摘要：
In typical in-hand manipulation tasks represented by object pivoting, the real-time perception of rotational slippage has been proven beneficial for improving the dexterity and stability of robotic hands. An effective strategy is to obtain the contact properties for measuring rotation angle through visuotactile sensing. However, existing methods for rotation estimation did not consider the impact of the incipient slip during the pivoting process, which introduces measurement errors and makes it hard to determine the boundary between stable contact and macro slip. This paper describes a generalized 2-d contact model under pivoting, and proposes a rotation measurement method based on the line-features in the stick region. The proposed method was applied to the Tac3D vision-based tactile sensors using continuous marker patterns. Experiments show that the rotation measurement system could achieve an average static measurement error of 0.17 degree and an average dynamic measurement error of 1.34 degree. Besides, the proposed method requires no training data and can achieve real-time sensing during the in-hand object pivoting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
