A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms

Abstract

Robot grasping is one of the most important abilities of modern intelligent robots, especially industrial robots. However, most of the existing robot arm’s grasp detection work is highly dependent on their edge computing ability, and the safety problems in the process of grasp detection are not considered enough. In this paper, we propose a new robotic arm grasping detection model with an edge-cloud collaboration method. With the scheme of multi-object multi-grasp, our model improves the mission success ratio of grasping. The model can not only complete the compression of full-resolution images but also achieve image compression at a limited bit rate. The image compression ratio reaches 2.03%; the structural difference value is higher than 0.91, and our average detection speed reaches 13.62 fps. Furthermore, we have packaged our model as a functional package of the ROS operating system, which can be easily used in actual robotic arm operations. Our solution can be fully applied to other work of robots to promote the development of the field of robotics.