Flexible Gear Assembly with Visual Servoing and Force Feedback

Abstract

This paper presents a vision-guided two-stage approach with force feedback to achieve high-precision and flexible gear assembly. The proposed approach integrates YOLO to coarsely localize the target workpiece in a searching phase and deep reinforcement learning (DRL) to complete the insertion. Specifically, DRL addresses the challenge of partial visibility when the on-wrist camera is too close to the workpiece of a small size. Moreover, we use force feedback to improve the robustness of the vision-guided assembly process. To reduce the effort of collecting training data on real robots, we use synthetic RGB images for training YOLO and construct an offline interaction environment leveraging sampled real-world data for training DRL agents. The proposed approach was evaluated in an industrial gear assembly experiment, which requires an assembly clearance of 0.3 mm, demonstrating high robustness and efficiency in gear searching and insertion from arbitrary positions.