Virtual Monitoring AI Robot Control System Based on Deep Learning

Abstract

With the continuous development of Artificial Intelligence (AI) technology, especially the advancement of Deep Learning (DL), robotics has made significant breakthroughs, especially in the field of automatic monitoring and control. In this study, a virtual monitoring robot control system based on DL is designed and implemented, aiming to improve the robot's environmental adaptability and decision-making efficiency. In this study, a hybrid model combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) is used, which is capable of efficiently processing large amounts of dynamic data from robotic sensors. Firstly, it processes and parses image data through CNN for efficient recognition of static and dynamic objects in the environment; next, it processes time-series data using RNN in order to predict the possible behaviour and trajectories of the objects. In addition, this paper develops a real-time data processing framework that can continuously optimize the model parameters to adapt to real-time changes in the environment as the robot performs its tasks. The results of the study show that the success rate of tracking and monitoring in the morning is relatively high at all density levels. At low densities, the success rates fluctuated between 91% and 92.73%, indicating that the system performs consistently in the morning under low traffic volumes. In summary, this study not only demonstrates the effectiveness of DL technology in the field of virtual monitoring robotics, but also provides a valuable reference for the future direction of robotics.