Vision-based Defects Detection and Interactive Impedance Control in Robotic Automated Layup Tasks

This paper proposes a robotic automated layup framework consisting of three components: human-robot layup skill transfer, vision-based quality detection, and adaptive impedance controller. The framework aims to address challenges and limitations faced by manual Carbon Fiber Reinforced Polymer (CFRP) layup, such as high labor intensity, poor quality consistency, and limited scalability. In the proposed framework, firstly, on a horizontal plane, humans demonstrate the layup behavior by dragging the robot in gravity compensation mode. During the demonstration, the Cartesian position, orientation, and interaction forces of the robot’s end-effector are recorded synchronously. Secondly, utilizing the Mask R-CNN instance segmentation model, defects such as bubbles and wrinkles on the CFRP surface are detected. Depth feature extraction and bounding boxes are employed to determine the position and orientation of the defects. Lastly, based on hand-eye calibration and coordinate system transformation, the robotic adaptive impedance controller repeats the layup behavior demonstrated at the defect pose points for automated defect repair. The feasibility and effectiveness of the proposed framework were validated in real-world scenarios, demonstrating its suitability for automated layup tasks.