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Soft robotic grippers are emerging as a promising solution for handling complex objects, offering enhanced flexibility in handling various tasks with a single gripper. This study presents a novel soft gripper which uses PneuNet based hybrid actuation to improve the grasping success in scenarios involving multi-shape object handling. The proposed actuator integrates a layer jamming element (LJE) to replace the non-extensible layer in a standard PneuNet actuator, simultaneously acting as a locking mechanism capable of maintaining the bending angle without the support from PneuNet. Evaluation of the LJE’s impact revealed that the use of a single LJE increased grasping force by up to 20%, while employing multiple LJEs showed no significant improvement. The success rates of three grasping modes, flat/parallel, spherical, and cylindrical were assessed using the proposed reconfigurable gripper. Experiments showed success rates of 62%, 68%, and 56% for grasping, and 54%, 53%, and 53% for jamming hold, respectively. By selecting an ideal grasping mode for each object, average success rates increased to 90% in grasping and 84% in jamming hold. The proposed vision-based grasping mode identification method selected the optimal mode based on object size and shape, leading to improved success rates of 96% in grasping and 86% in jamming hold.
Tennakoon et al. (Mon,) studied this question.