4 research outputs found
Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger
Robotic fingers made of soft material and compliant structures usually lead
to superior adaptation when interacting with the unstructured physical
environment. In this paper, we present an embedded sensing solution using
optical fibers for an omni-adaptive soft robotic finger with exceptional
adaptation in all directions. In particular, we managed to insert a pair of
optical fibers inside the finger's structural cavity without interfering with
its adaptive performance. The resultant integration is scalable as a versatile,
low-cost, and moisture-proof solution for physically safe human-robot
interaction. In addition, we experimented with our finger design for an object
sorting task and identified sectional diameters of 94\% objects within the
6mm error and measured 80\% of the structural strains within 0.1mm/mm
error. The proposed sensor design opens many doors in future applications of
soft robotics for scalable and adaptive physical interactions in the
unstructured environment.Comment: 8 pages, 6 figures, full-length version of a submission to IEEE
RoboSoft 202
Automated Recycling Separation Enabled by Soft Robotic Material Classification
Single-stream recycling is currently an extremely labor intensive process due to the need for manual object sorting. Soft robotics offers a natural solution as compliant robots require less computation to plan paths and grasp objects in a cluttered environment. However, most soft robots are not robust enough to handle the many sharp objects present in a recycling facility. In this work, we present a soft sensorized robotic gripper which is fully electrically driven and can detect the difference between paper, metal and plastic. By combining handed shearing auxetics with high deformation capacitive pressure and strain sensors, we present a new puncture resistant soft robotic gripper. Our materials classifier has 85% accuracy with a stationary gripper and 63% accuracy in a simulated recycling pipeline. This classifier works over a variety of objects, including those that would fool a purely vision-based system.National Science Foundation (Grant 1830901