Nonrepetitive-Path Iterative Learning and Control for Human-Guided Robotic Operations on Unknown Surfaces

Automation of abrasive machining operations (AMO) has become a challenging aspect in the remanufacturing industry where it is required to conduct operations on a surface of which the exact dimensions are unknown. In such cases, skilled human workers have to step in to perform labor-intensive tasks with inconsistent quality. In existing research work, collaborative robots are used to partially automate such operations under human supervision. However, these methods do not perform learning and control simultaneously and are often affected by the interactions of the human operator. In this paper, a novel learning and control scheme is proposed where the robot explores an unknown surface iteratively while achieving the desired contact control performance under supervision and occasional interference from the human operator. The unknown surface is divided into sub-regions, and the learning and control parameters are updated each time the robot visits each sub-region. This method is independent of the path of the robot and thus is unaffected by the irregularities introduced by a human operator's interactions. The proposed method is applied to force control, stiffness learning, and orientation adaptation cases. The validity of this method is shown via simulations as well as experiments conducted using a Kinova Gen3 7-DOF robot.