Unocculuded object grasping by using visual data

Automatic grasping objects can become important in the areas such as industrial processes, processes which are dangerous for human, or the operations which should be executed in the places, small for people work. In this study, it is aimed to design a robotic system for grasping unocculuded certain objects by using visual data. For this aim an experimental process was implemented. Visual data process can be divided in two main parts: identification and three dimensional positioning. Identification issue suffers from several conditions as rotation, camera position, and location of the subject in the frame. Also obtaining the features invariant from these conditions is important. Therefore Zernike moment method can be used to overcome these negativities. In order to identify the objects an artificial neural network was used to classify the objects by using Zernike moment coefficients. In the experimental system a parallel axis stereovision subsystem, a DSPFPGA embedded media processor, and five-axis robot arm were used. The success rate of artificial neural network was 98%. After identifying the objects, a sequential algebra were performed in the DSP part of the media processor and the position of the object according to robot arm reference point was extracted. After all, desired object in the instant frame was grasped and placed in different location by the robot arm