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

Abstract

A great effort has been made towards the integration of object recognition capability in robotics especially in humanoids, mobile robots and advanced industrial manipulators. Industrial robots today are not equipped with this capability in its standard version, but as an option. Robot vision systems can differentiate parts by pattern matching irrespective of part orientation and location and even some manufacturers offer 3D guidance using robust vision and laser systems so that a 3D programmed point can be repeated even if the part is moved varying its rotation and orientation within the working space. Despite these developments, current industrial robots are still unable to recognise objects in a robust manner; that is, to distinguish among equally shaped objects unless and alternative method is used, for instance taking into account not only the object’s contour but also its form, which is precisely the major contribution of this chapter. How objects are recognized by humans is still an open research field. There are researchers that favour the theory of object recognition via object-models like Geons (Biederman, 1987), but other researchers agree on two types of image-based models: viewpoint dependent or viewpoint invariant. But, in general there is an agreement that humans recognise objects as established by the similarity principle –among othersof the Gestalt theory of visual perception, which states that things which share visual characteristics such as shape, size, colour, texture, value or orientation will be seen as belonging together. This principle applies to human operators; for instance, when an operator is given the task to pick up a specific object from a set of similar objects; the first approaching action will probably be guided solely by visual information clues such as shape similarity. But, if further information is given (i.e. type of surface), then a finer clustering is accomplished to identify the target object. We believe that it is possible to integrate a robust invariant object recognition capability in industrial robots following the above assumptions by using image features from the object’s contour (boundary object information) and its form (i.e. type of curvature or topographical surface information). Both features can be concatenated in order to form an invariant vector descriptor which is the input to an Artificial Neural Network (ANN) for learning and recognition purposes. In previous work, it was demonstrated the feasibility of the approach 21