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Bio-Inspired Shape-Adaptive Soft Robotic Grippers Augmented with Electroadhesion Functionality
(2019)
Soft robotic grippers (SRGs) have been extensively employed in robotic grasping and manipulation applications due to the fact that they are beneficial for pick-and-place of difficult-to-handle and delicate objects with various geometries and stiffness in a comfortable and safer way. This article presents a bio-inspired and shape-adaptive SRG augmented with electroadhesion (EA) functionality, FinEA, by a cost-effective combination of a Fin Ray structured two-fingered SRG with two soft-stretchable EA pads. The EA pads were manufactured by screen printing a layer of electrically conductive and elastomeric carbon black powder mixed with polydimethylsiloxane onto a dielectric substrate. The compliant Fin Ray fingers, composed of soft longitudinal beams and rigid cross beams, were structurally optimized by the finite element method using ABAQUS/CAE based on three key parameters: the open angle of the longitudinal beams, the spacing between the cross beams, and the incline angle of the cross beams. The soft beams were produced by a traditional soft lithography method, whereas the rigid cross beams were three-dimensionally printed. The resultant FinEA grippers were capable of lifting not only flat/thin materials without distorting them due to the employment of EA, but also concave and convex objects due to the passively shape-adaptive Fin Ray structure and the EA functionality. In addition, the proposed FinEA grippers were able to grasp delicate materials and objects whose diameters are larger than the overall gripper length. Furthermore, 65% more weight in shear were picked up by the FinEA gripper when 4 kV was applied compared with 0 V. The FinEA concept provides useful and alternative solution for controllable adhesion-based SRGs and may facilitate the inspiration and development of future SRGs with added functionality and enhanced versatility.
Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context - such as the current procedural phase - has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding.