In this paper we present an early prototype of the Digger Finger that is designed to easily penetrate granular media and is equipped with the GelSight sensor. Identifying objects buried in granular media using tactile sensors is a challenging task. First, particle jamming in granular media prevents downward movement. Second, the granular media particles tend to get stuck between the sensing surface and the object of interest, distorting the actual shape of the object. To tackle these challenges we present a Digger Finger prototype. It is capable of fluidizing granular media during penetration using mechanical vibrations. It is equipped with high resolution vision based tactile sensing to identify objects buried inside granular media. We describe the experimental procedures we use to evaluate these fluidizing and buried shape recognition capabilities. A robot with such fingers can perform explosive ordnance disposal and Improvised Explosive Device (IED) detection tasks at a much a finer resolution compared to techniques like Ground Penetration Radars (GPRs). Sensors like the Digger Finger will allow robotic manipulation research to move beyond only manipulating rigid objects.
GelSight Baby Fin Ray: A Compact, Compliant, Flexible Finger with High-Resolution Tactile Sensing
Massachusetts Institute of Technology（麻省理工学院）
触觉感知与合规性的结合对于许多领域都至关重要，从水果采摘等农业用途，到分类回收等可持续性实践，再到为老年人创造安全的家庭护理机器人，让他们有尊严地度过晚年。通过触觉感知，我们可以辨别材料属性，识别纹理，并确定柔软度，同时通过顺应性，我们能够安全地与周围的物体和环境进行交互。这两种能力可以最终成为一个有用的软机器人抓手，例如最初的GelSight Fin Ray，它能够抓住各种不同的物体，也可以执行简单的家庭操作任务：酒杯重新定位。虽然最初的GelSight Fin Ray解决了将一般刚性的高分辨率传感器与柔软的顺应性结构连接的问题，但我们可以提高传感器的鲁棒性并实施技术，使这种基于摄像头的触觉传感器适用于更广泛的软体机器人设计。我们首先集成了柔性反射镜，并将刚性电子元件集成到夹持器的基座中，这大大提高了Fin Ray结构的顺应性。然后，我们合成了一种柔性和高伸长率的有机硅粘合剂基荧光涂料，它可以为我们的传感器提供良好质量的二维触觉定位结果。最后，我们将所有这些技术整合到一个新的设计中：Baby Fin Ray，我们用它来挖掘杂物，并成功地对坚果进行分类。补充视频可以在这里找到：https://youtu.be/_oD_QFtYTPM
Massachusetts Institute of Technology（麻省理工学院）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
针对柔性结构与高分辨率触觉感知协同设计的难题，提出融合有限元力学仿真与PBR光学仿真的GelSight Fin Ray传感器端到端设计框架，可优化多类设计参数，缩短原型周期，验证日常物品抓取效用。
