Data-Driven Calibration of Industrial Robots: A Comprehensive Survey

Industrial robots, as the fundamental component for intelligent manufacturing, have attracted considerable attention from both academia and industry. Since its absolute positioning accuracy can suffer from collision, wear, elastic, or inelastic deformation during its operation, a data-driven calibration (DDC) model has become a trending technique. It utilizes abundant data to decrease the difficulty in building complex system models, making it an economic and efficient approach to robot calibration. This paper conducts a comprehensive survey of the state-of-the-art DDC models with the following six-fold efforts: a) Summarizing the DDC modeling methods; b) Categorizing the latest progress of DDC optimization algorithms; c) Investigating the publicly available datasets and several typical metrics; d) Evaluating several widely adopted DDC models to demonstrate their calibration performance; e) Introducing the applications of the current DDC models; f) Discussing the progressing trend of DDC models. This paper strives to present a systematic and thorough overview of the existing DDC models from modeling to kinematic parameter optimization, thereby providing some guidance for research in this field.