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

摘要

The research presented in this chapter presents an alternative methodology to integrate a robust invariant object recognition capability into industrial robots 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. Experimental results were obtained using two sets of four 3D working pieces of different cross-section: square, triangle, cross and star. One set had its surface curvature rounded and the other had a flat surface curvature so that these object were named of pyramidal type. Using the BOF information and training the neural network with this vector it was demonstrated that all pieces were recognised irrespective from its location an orientation within the viewable area since the contour was only taken into consideration. With this option it is not possible to differentiate the same type of object with different surface like the rounded and pyramidal shaped objects. When both information was concatenated (BOF + SFS), the robustness of the vision system improved recognising all the pieces at different location and orientation and even with 5 degrees inclination, in all cases we obtained 100% recognition rate. Current results were obtained in a light controlled environment; future work is envisaged to look at variable lighting which may impose some consideration for the SFS algorithm. It is also intended to work with on-line retraining so that recognition rates are improved and also to look at the autonomous grasping of the parts by the industrial robot.