Robot-Based Automatic Charging for Electric Vehicles Using Incremental Learning and Biomimetic Control

Abstract

With the growing popularity of electric vehicles, the demand for robot-based unmanned automatic charging has become both urgent and challenging. Two key challenges need to be addressed: how to efficiently locate the charging port, and how to compliantly insert the connector into the port. In this paper, we propose an incremental learning method based on the broad learning system to address the visual positioning error of the charging port. This method allows the robot to transfer and generalize the search skills learned in simulation to real-world scenarios. As a result, the robot can rapidly locate the charging port in real-world environments without the need for complex contact state modeling, time-consuming data collection, or model retraining. Subsequently, a biomimetic admittance controller is designed to enable the robot to adapt its compliant behavior online during the plugging process. Finally, experiments are performed on a UR robot to verify the effectiveness of our method.