Abstract:
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.
