Robotic grippers with visuotactile sensors have access to rich tactile information for grasping tasks but encounter difficulty in partially encompassing large objects with sufficient grip force. While hierarchical gecko-inspired adhesives are a potential technique for bridging performance gaps, they require a large contact area for efficient usage. In this work, we present a new version of an adaptive gecko gripper called Viko 2.0 that effectively combines the advantage of adhesives and visuotactile sensors. Compared with a non-hierarchical structure, a hierarchical structure with a multimaterial design achieves approximately a 1.5 times increase in normal adhesion and double in contact area. The integrated visuotactile sensor captures a deformation image of the hierarchical structure and provides a real-time measurement of contact area, shear force, and incipient slip detection at 24 Hz. The gripper is implemented on a robotic arm to demonstrate an adaptive grasping pose based on contact area, and grasps objects with a wide range of geometries and textures.
翻译:在这项工作中,我们提出了一个名为Viko 2.0的适应性壁画抓抓器新版本,它有效地结合了粘合器和配额感应器的优势。与非等级结构相比,一个具有多物质设计的等级结构在正常粘合和接触区域中达到约1.5倍的倍增。综合的壁画传感器捕捉了等级结构的变形图像,提供了接触区域的实时测量、剪动力和24Hz的早期滑动检测。拉动器在机器人臂上安装,以显示基于接触区域的适应性捉摸姿势,并捕捉具有广泛地理和文字的物体。