Using Object's Contour, Form and Depth to Embed Recognition Capability into Industrial Robots

Abstract

Robot vision systems can differentiate parts by pattern matching irrespective of part orientation and location. Somemanufacturers offer 3D guidance systems using robust vision and laser systems so that a 3D programmed point canbe repeated even if the part is moved varying its location, rotation and orientation within the working space. Despitethese developments, current industrial robots are still unable to recognize objects in a robust manner; that is, todistinguish an object among equally shaped objects taking into account not only the object’s contour but also its formand depth information, which is precisely the major contribution of this research. Our hypothesis establishes that it ispossible to integrate a robust invariant object recognition capability into industrial robots by using image features fromthe object’s contour (boundary object information), its form (i.e., type of curvature or topographical surfaceinformation) and depth information (from stereo disparity maps). These features can be concatenated in order to forman invariant vector descriptor which is the input to an artificial neural network (ANN) for learning and recognitionpurposes. In this paper we present the recognition results under different working conditions using a KUKA KR16industrial robot, which validated our approach.