Study on grasping pose detection based on lightweight artificial neural network

Object grasping based on artificial neural network is a research hotspot in the field of industrial robot, and effective grasping is highly challenging in the robot control process. In the traditional robot object grasping process, it is necessary to obtain and store the three-bit model of the grabbed object in advance based on template control grasping trajectory, which leads to the grasping scene that is relatively fixed and single, lacking the flexibility of grasping. This study constructs a lightweight convolution neural network combined with artificial neural network technology based on the RGB-D camera, and points at for known objects and unknown objects two categories to study algorithm of the robot grasping position detection, which provides effective technical support to solve the problem of grasping position detection and improves the flexibility of grasping.