A comparative study between a traditional coarse sense-plan-act system and a two-stage RoI recommender and Eyes-in-hand stereo system for robotized unscrewing of hard disk drives (HDDs)

Robotic unscrewing plays a pivotal role in the automation of remanufacturing processes. Despite advancements in deep learning-based screw detection, which nowadays achieve detection accuracies exceeding 98%, consistent unscrewing success remains challenging due to physical localization errors. Within prior research various vision-based methods have been developed to correct positional deviations that arise from neural network predictions. On the other hand, force-based methods have been developed to compensate for all accumulating errors during physical contact. The vision-based approaches often lack reliability, and force-based methods, although highly effective, have to date, only been validated on larger, self-centering fasteners and will result in additional wear to the unscrewing end-effector. Therefore, this study presents a comparative analysis of two robotic unscrewing pipelines designed to enhance detection accuracy, localization precision, and unscrewing time efficiency. The first baseline approach employs a single-stage, coarse-only sense– plan–act approach. The second, advanced two-stage approach integrates coarse localization with a refinement step using stereo detection, matching and triangulation. Experimental results highlight the advantages of the advanced two-stage approach, which incorporates a region-of-interest recommendation step and an eyes-in-hand stereo module for position refinement. With the pilot system developed in the presented research, vision-based precision and recall rates of 99.2% and 99.4% are obtained, with an unscrewing success rate of 99%. At the system level, these results correspond to unscrewing precision and recall of 98.2% and 98.4% with only 0.75 seconds needed to reclassify and compute the updated screw location.