Vision-Force Guided Robotic EV Charging: Learning-Based Localization and 6-DoF Hybrid Compliance Control for High-Precision Insertion

The automation of electric vehicle (EV) charging is a critical challenge in robotics, requiring high precision and adaptability to handle the complex geometry of charging ports. Traditional methods, such as visual servoing and force control, often struggle to achieve reliable performance due to their inability to simultaneously address positional and force-related uncertainties. To address these limitations, this paper proposes a novel coarse-to-fine framework that integrates visual and force feedback for robust and efficient peg-in-hole operations. Our method leverages a structured light camera for 6-DoF (Degrees of Freedom) pose estimation and an end-mounted force sensor for real-time force feedback. In the coarse localization stage, a learning-based object detection model identifies the charging port, while template alignment refines the pose estimation. In the fine assembly stage, a force-position hybrid control strategy ensures precise alignment and insertion. Extensive real-world experiments demonstrate the effectiveness of our approach, achieving a 96% success rate in inserting the charging gun into EV charging ports.