A Novel Robotic Grasp Detection Technique by Integrating YOLO and Grasp Detection Deep Neural Networks *

Abstract

This paper proposes a robotic grasp detection technique by integrating you only look once (YOLO) deep neural network (DNN) and a grasp detection DNN. In this world, there are many people who cannot move their own bodies. The reason may be an accident or physical deterioration. So we need to invest more human resources to assist their lives. With new technological advances, robots are gradually able to perfectly replicate human movements. Hence, we intend to design a remote-control fetching robot. The system combines internet of things (IoT) technology, and users can use intelligent devices to control this robot with robotic arm to get the items they want. This paper focus on detecting the grasp of robotic arm by integrating YOLO and grasp detection DNNs. At first, YOLO V-v3 is applied to achieve object detection. Then a robotic grasp detection DNN is proposed to detect the robotic grasp. After that, the point cloud information of this object is utilized to calculate the normal vector of the grasp position such that the robotic arm can fetch the target along the normal vector. Finally, experiment results are given to show the practicality of the proposed robotic grasp detection Technique.