Development of a vision-based tactile sensor with micro suction cups

Abstract

Tactile sensors capable of measuring contact force distribution are being actively developed for complex manipulation by robotic hands. In recent years, vision-based tactile sensors that estimate contact conditions by observing elastomer deformation with a camera from the backside and analyzing the images have attracted much attention. In the general marker displacement method using a single camera, it is difficult to estimate the contact force in the normal direction with high resolution. By applying a microstructure array (micro suction cup array) to the surface of the elastomer that is in contact with the object, the micro suction cups are deformed by the contact force. A device was fabricated that can estimate the normal contact force to each suction cup from this deformation images using machine learning. The device can vacuum a micro suction cup array and capture its deformation from the backside. The micro suction cup array (2×2) was fabricated by transferring a master mold fabricated on a high-resolution optical fabrication 3D printer onto polydimethylsiloxane. It was shown that UV nanoimprinting with soft molds can be used to fabricate large-area micro suction cup arrays relatively easily. A regression CNN model was created to estimate the normal force applied to each suction cup from the deformation image. An average error between the measured and estimated value was 21 mN, which was sufficient for the accuracy of the estimation.