Contact-Based Pose Detection Method for Small Components to Optimise the Digital Twin-Driven Robotic Assembly Process

Robotic assembly has become pivotal in manufacturing, demanding precise pose detection for efficient operations. This paper presents a contact-based pose detection method tailored for digital twin-based optimization for robotic assembly process, focusing on small component assembly challenges. Leveraging a strain gauge-based load cell and gripper calibration, the proposed technique achieves robust pose estimation. Experimental validation demonstrates close alignment between induced and measured errors, showcasing the efficacy of the method in mitigating assembly challenges. Despite minor deviations, the approach outperforms traditional vision-based methods, promising enhanced efficiency in robotic assembly tasks. Further refinement could bolster accuracy, fostering advanced robotic assembly capabilities.