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Artificial intelligence is a very broad science, which consists of different fields, such as machine learning, and computer vision. In recent years, the world nuclear industry has developed vigorously. At the same time, incidents of loss of radioactive sources also occur from time to time. At present, most of the search for radioactive sources adopt manual search, which is inefficient, and the searchers are vulnerable to radiation damage. Sending a robot to the search an area where there may be an uncontrolled radioactive source is different. Not only does it improve efficiency, it also protects people from radiation. Therefore, it is of great practical significance to design a radioactive source search robot. This paper mainly introduces the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing, aiming to provide some ideas and directions for the research of radioactive source intelligent search robot. In this paper, a research method for the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing is proposed, including intelligent edge computing and gamma-ray imaging algorithms, which are used to carry out related experiments on the design and implementation of radioactive sources, an intelligent search robot based on edge computing. The experimental results of this paper show that the average resolution of the radioactive source search robot is 90.55%, and the resolution results are more prominent.

This paper investigates a model-based method for robot manipulator control by describing the kinematics and dynamics of constrained robot when contacting a dynamic environment. Parameters which concern kinematic constraints of robot end egector,dynamic interactions and uncertainty of environment are represented from the environment side. By using Lagrangian formula, the whole dynamic expressions of the robotenvironment system can be derived by the variables representing the joint angles. This modeling method is suitable for hybrid control laws to fulfil the manipulation task. The simulation results obtained from PID regulatiors are presented. Those results suggest that in practice, fied PID parameters may not perform well because of the strong nonlinearity of the system and this will lead to further modified PID method such as a fuzzy logic self-tuming PID controller to achieve the timevarying tracking control of the robot manipulator.

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