Human-Robot Collaboration for Unknown Flexible Surface Exploration and Treatment Based on Mesh Iterative Learning Control

Abstract

Contact tooling operations like sanding and polishing have been high in demand for robotics and automation, as manual operations are labour-intensive with inconsistent quality. However, automating these operations remains a challenge since they are highly dependent on prior knowledge about the geometry of the workpiece. While several methods have been developed in existing research to automate the geometry learning process and adjust the contact force, human supervision is heavily required in the calibration of workpieces and the path planning of robot motion in such methods. Furthermore, the stiffness identification of the workpiece is not considered in most of these methods. This paper presents a human-robot collaboration (HRC) framework, which is able to perform surface exploration on an unknown object combining the operator's flexibility with the control precision of the robot. The operator moves the robot along the surface of the target object, and the robot recognizes the surface geometry and surface stiffness while exerting a desired contact force through control. For this purpose, a mesh iterative learning control (MILC) is developed to learn the surface stiffness, plan the exploration path, and adjust contact force through repetitive online correction based on HRC. The proof of learning convergence and the results of the simulation and experiments performed using a 7-DOF Sawyer robot demonstrate the validity of the proposed controller.