ABSTRACT
To achieve precise grasping and spreading of irregular sheet-like soft objects (such as leather) by robots, this study addresses several challenges, including the irregularity of leather edges and the ambiguity of feature recognition points. To tackle these issues, this paper proposes an innovative method that involves alternately grasping the lowest point twice and using planar techniques to effectively spread the leather. We improved the YOLOV8 algorithm by incorporating the BIFPN network structure and the WIOU loss function, and trained a dedicated dataset for the lowest grasping points and planar grasping points, thereby achieving high-precision recognition. Additionally, we determined the optimal posture for grasping the lowest point and constructed an experimental platform, successfully conducting multiple rounds of leather grasping and spreading experiments with a success rate of 72%. Through an in-depth analysis of the failed experiments, this study reveals the limitations of the current methods and provides valuable guidance for future research.