Motion planning of radioactive source grasping robot based on memory reasoning

Abstract

Aiming at the problem that machine vision is difficult to be applied to the radioactive source grasping due to the semi-closed and strong radiation environment of the lead can, we propose a memory reasoning based reinforcement learning grasping method. The kinematics model of intelligent robot grasping system is constructed based on machine vision. The interaction between the intelligent robot and internal environment of lead cans is realized by force feedback. Through the memory reasoning decision of historical grasping data, the autonomous grasping of radioactive sources is realized. Using the Gazebo simulator in robot operating system (ROS), the Monte Carlo sampling method and reinforcement learning grasping method based on memory reasoning are simulated, respectively. The results show that the reinforcement learning grasping method based on memory reasoning achieves the average grasping efficiency of 84.67% higher than that of Monte Carlo sampling method and thus demonstrate that the reinforcement learning grasping method can effectively solve the problem of autonomous grasping of radioactive sources in lead cans.