Paper Title
Vision based Extraction of Grasping Region of Objects

Many applications of robotics include the grasping and manipulation of objects. Working in assembly robotic environments, the robot has to accurately not only locate the part but also to recognize it in readiness for grasping. This paper is mainly focused on the extraction of grasping region that is robust to changes in appearance of objects that have different shapes, sizes by using predefined rules. The classification performance of Backpropagation Neural Network (BPNN) classifier based on PCA features for grasping objects are evaluated. The experiments are carried out by using MATLAB programming language. The proposed system has been tested successfully to a dataset of 200 images for seven type and achieves good classification accuracy for 2D images. This system can examine types of the hand tools. It can also be evaluated the size of tool and the locations of the tool at which to grasp the object. This system can be easily adopted to grasp a number of common hand tools with large, medium and small size such as wrenches, screw drivers, brushes, washers and nails and hex keys, etc. Keywords: Appearance, Backpropagation Neural Network, classification, grasping region.