ABSTRACT:
Object manipulation performed by robots refers to the art of controlling the shape and location of an object through force constraints with robot end-effectors, both robot hands, and grippers. The success of task execution is usually guaranteed by the sense of touch. In this work, we present an optical tactile sensor incorporating plastic optical fibers, transparent silicone rubber, and an off-the-shelf color camera that can detect: translational and rotational shear forces, and contact location and its normal force. Contact localization is possible thanks to the shear strain. Specifically, one of the layers stretches so that its thickness decreases. The decrease in the thickness results in the color change at the point of contact. Elastic behavior of the sensing media provides a robust rotational and translational shear detection mechanism when torque and planar force, respectively, are applied onto the sensing surface. Thanks to the plastic optofibers, signal processing electronics are placed away from the sensing surface making the sensor immune to hazardous environments. Machine learning techniques were used to benchmark the sensing performance of the sensor. By implementing a multi-output CNN model, the contact type was classified into normal and shear or torsional deformation and their corresponding continuous contact features were estimated.
