Jamming-free reliable peg-in-hole assembly with unknown in-hand pose using visuotactile sensing

Abstract

This paper aims to propose a robust, jamming-free peg-in-hole assembly control method for complex components, such as threaded screws, in unstructured environments using visuotactile sensing. The approach introduces high-precision pose pre-alignment by performing joint estimation of the peg’s angle and offset from visuotactile depth images using a residual network. A dual-mask spatial averaging algorithm is formulated, providing a computationally efficient and low-latency solution for visuotactile force estimation. Furthermore, the search strategy integrates an active jamming recovery mechanism into an admittance-based spiral search, which leverages bidirectional transverse movements to release geometric constraints. Experiments were conducted using a 6-DOF robot arm equipped with visuotactile sensors to perform peg-in-hole tasks. The proposed framework achieved a 90% assembly success rate across 60 trials, with an average hole-searching time of 11.85 s. Time-domain response experiments demonstrated that our dual-mask force estimation algorithm effectively balances real-time responsiveness and noise suppression. In the step-response experiment, the proposed dual-mask force estimation algorithm achieved a low phase lag of 53.40 ms while maintaining high signal stability (Coefficient of Variation = 0.59%). This work contributes a complete jamming-free visuotactile assembly framework. By coupling a computationally efficient force estimation technique with a bidirectional escape mechanism, it provides a highly practical solution that enhances robotic assembly reliability.