A coarse-to-fine framework for accurate positioning under uncertainties—from autonomous robot to human–robot system

Abstract

Recently, with the growing trend of high-mix, low-volume manufacturing, the demand for better flexibility and autonomy without sacrificing the accuracy of industrial robots and human–robot systems has increased. In this paper, a framework based on a coarse-to-fine strategy for industrial robots and human–robot systems is proposed to push the bounds of machine autonomy and machine flexibility while simultaneously maintaining good accuracy and efficiency. Under the proposed framework, industrial robots and human operators are designated to conduct coarse global motion with the aim of implementing low-bandwidth planning-level intelligence. Simultaneously, fine local motion for tackling accumulated on-line uncertainties is realized by an add-on robotic module with the role of implementing high-bandwidth action-level intelligence. Consequently, the overall system for both applications provides good adaptability to uncertain work conditions, while concurrently realizing fast and accurate positioning. A contour following task in two dimensions, simulating simplified tasks in industrial applications (e.g., sealant application, inspection, welding), was implemented and evaluated by autonomous robot control and human–robot collaboration.