Model-based sensorless robot collision detection under model uncertainties with a fast dynamics identification

This article presents a novel model-based sensorless collision detection scheme for human-robot interaction. In order to recognize external impacts exerted on the manipulator with sensitivity and robustness without additional exteroceptive sensors, the method based on torque residual, which is the difference between nominal and actual torque, is adopted using only motor-side information. In contrast to classic dynamics identification procedure which requires complicated symbolic derivation, a sequential dynamics identification was proposed by decomposing robot dynamics into gravity and friction item, which is simple in symbolic expression and easy to identify with least squares method, and the remaining structure-complex torque effect. Subsequently, the remaining torque effect was reformulated to overcome the structural complexity of original expression and experimentally recovered using a machine learning approach named Lasso while keeping the involving candidates number reduced to a certain degree. Moreover, a state-dependent dynamic threshold was developed to handle the abnormal peaks in residual due to model uncertainties. The effectiveness of the proposed method was experimentally validated on a conventional industrial manipulator, which illustrates the feasibility and simplicity of the collision detection method.