Textures recognition through tactile exploration for robotic applications

Abstract

Abstract-Achieving texture recognition through processing of tactile information could significantly improve robotic prehensile and manipulative capabilities. By producing an object signature based on such information, mishandling due to friction or slippage could be avoided. However, this would require acquisition and processing of tactile data in close to real time in order to function at task speed. This paper proposes a new texture-discriminating algorithm that requires very little exploratory movement. We compared the success rate of two types of exploratory movement for the recognition textures with directional properties such as grooves. Another goal of this study was to obtain an algorithm that is largely insensitive to the velocity and contact force of the sensor movement. We used a genetic algorithm to optimize the variables and the topology of our neural network. We improved the results with a new approach to majority voting that does not require numerous samples. Object classification was more than 90 % correct and most of the errors involved textures that humans are barely able to differentiate.

A Highly Sensitive Multimodal Tactile Sensing Module with Planar Structure for Dexterous Manipulation of Robots

“…The Duchaine group introduced multimodal tactile sensors that can simultaneously detect static and dynamic pressure using the fringe capacitance effect with a device design incorporating micro‐structured dielectric and comb‐shaped taxels (tactile pixels). [ 15,16 ] The modular design, where a circuit board, capacitive sensing arrays, a cover layer, and a metallic protecting case are integrated into one device, allows users to easily install tactile sensors on robotic grippers or replace them. Moreover, the developed design addressed the noise issue of the capacitive driving method by providing a proper electrical shield with covers of conductive materials (e.g., a metallic case and conductive fabric).…”