Experimental study of a novel contact-based pose detection approach for digital twin-driven high-precision micro assembly

This study presents a comprehensive study of a novel Contact-Based Pose Estimation Method (CBPEM) for enhancing precision in robotic assembly processes within digital twin frameworks. Conventional pose detection methods, such as laser and computer vision-based point cloud acquisition, are often hindered by limitations related to optical properties and occlusion when detecting small or intricate components. In contrast, CBPEM leverages contact-based detection using capacitive or load cell sensors, offering a high degree of position and orientation accuracy while mitigating the optical challenges posed by traditional methods. This study focuses on the precision assembly of a concentrator photovoltaic solar unit, which consists of primary and secondary lenses, a solar cell, and a tripod leg their small size, deformability, and unique optical properties, making pose estimation particularly challenging. Experimental evaluations were conducted to compare the effectiveness of capacitive and load cell sensors in detecting contact points, assessing their precision and accuracy on flexible and rigid components. CBPEM demonstrated commendable performance with position accuracies reaching 0.05 mm and orientation accuracies around 0.4 degrees, matching or surpassing traditional optical methods. While CBPEM provides enhanced accuracy, it requires sequential data acquisition, resulting in lower detection speeds compared to optical methods. Additionally, the method’s efficacy depends on the precision of robotic components and may be impacted by complex geometries or highly deformable surfaces. Overall, CBPEM offers a robust alternative for precision pose estimation in digital twin-driven robotic assembly, particularly when dealing with components unsuited to optical methods. Future research should explore hybrid approaches to optimize detection speed and adaptability across a broader range of robotic assembly scenarios.