In the solid radioactive waste sorting operation, in order to solve the typical problems of disordered radioactive waste, low efficiency of remote teleoperation, and high risk of manual sorting, this paper proposed a method for grasping radioactive solid waste based on deep reinforcement learning. This method used an improved depth Q network algorithm. Through the acquired image information, the robot could continuously interact with the environment and obtain rewards, which were composed of the execution results of the robotic arm actions and the size of the radioactive activity in the radioactive area. According to the size of the Q value, the robot arm got the optimal grasping position. V-REP software established the simulation model of the UR5 robot arm, and different types of solid radioactive waste grasp training and testing were completed in the simulation environment. The simulation results show that when the radioactive solid waste is loosely placed, the method can make the grasping success rate greater than 90%, and the grasping success rate is more than 65% when the radioactive solid waste is placed tightly. The robotic arm grasping operation is not affected by the stacking of objects and the objects with high radioactivity in the radioactive area are preferentially grasped.

[2020-11-05] Printed Article /doc/2019.07.0288/v2