In robotic manipulation, tactile sensors are indispensable, especially when dealing with soft objects, objects of varying dimensions, or those out of the robot's direct line of sight. Traditional tactile sensors often grapple with challenges related to cost and durability. To address these issues, our study introduces a novel approach to visuo-tactile sensing with an emphasis on economy and replacablity. Our proposed sensor, BeadSight, uses hydro-gel beads encased in a vinyl bag as an economical, easily replaceable sensing medium. When the sensor makes contact with a surface, the deformation of the hydrogel beads is observed using a rear camera. This observation is then passed through a U-net Neural Network to predict the forces acting on the surface of the bead bag, in the form of a pressure map. Our results show that the sensor can accurately predict these pressure maps, detecting the location and magnitude of forces applied to the surface. These abilities make BeadSight an effective, inexpensive, and easily replaceable tactile sensor, ideal for many robotics applications.

翻译：在机器人操作中，触觉传感器是不可或缺的，尤其是在处理柔软物体、尺寸各异的物体或机器人视线之外的物体时。传统的触觉传感器常常面临成本和耐用性方面的挑战。为了解决这些问题，我们的研究引入了一种新颖的视觉-触觉传感方法，其重点在于经济性和可替换性。我们提出的传感器BeadSight，使用封装在乙烯基袋中的水凝胶珠作为一种经济、易于更换的传感介质。当传感器与表面接触时，使用后置摄像头观察水凝胶珠的变形。然后将该观测结果输入U-net神经网络，以预测作用在珠袋表面的力，其形式为压力分布图。我们的结果表明，该传感器能够准确预测这些压力分布图，检测施加在表面上的力的位置和大小。这些能力使BeadSight成为一种高效、廉价且易于更换的触觉传感器，是许多机器人应用的理想选择。