It is a conundrum to use robots to complete the intelligent operation of leather and clothing fabrics; this is mainly reflected in the design of an end effector that can realize this function. We have determined the design requirements and objectives of the end effector by observing the action of manually grabbing and spreading leather, and put forward an end effector composed of a reconfigurable multi-link mechanism and roller-type fingertip, which can be used to grab and spread flaky deformable object manipulation. At the same time, the reconfigurable multi-link mechanism and roller-type fingertip are introduced and analyzed in detail. By setting up the prototype of the end effector and completing the grabbing experiment of three kinds of soft pieces, we verified and tested the rationality of the prototype design. The result shows that the grabbing success rate will be affected by many factors, and further exploration is needed to further improve the performance of the end effector.

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.