In order to realize automated picking robot, it is an important task to determine the grasping parameters (position/direction/angle) of the object. In this paper, we propose a method for approximating an object with primitive shape to determine the grasping parameters. Our method applies "object primitive" (for example, hexahedrons, cylinders, and spheres) to the object by using a 3D-deep neural network (DNN) on the surface of the object. Then, we estimate the grasping parameters based on

more »

... grasping rules. The success rate of approximating the object primitive with our method was 94.7%. This result is 6.7% higher than the 3D ShapeNets method using 3D-DNN. Also, as an experimental result of grasping simulation using Gazebo, the success rate of grasping with our method was 85.6%. This result is 17.8% higher than the GPD method using DNN.