Features Coefficients Selection-based Decision Tree Classification Model for Robot Remote Control System Performance Bottlenecks Recognition

The robot remote control architecture aims to reduce the computing load of the robot and improve the robot's task response speed. To address the robot's limited computational resources, this paper proposes a remote control architecture that migrates complex computations to a remote Linux server. Tasks such as face recognition, person tracking, Q&A, and product information recognition are processed remotely, and the robot executes commands received from the Linux server, thereby reducing its local computational load. At the same time, the Feature Coefficients Selection-based Decision Tree (FCS-DT) Classification Method is proposed for deployment on the remote Linux server. This approach enables real-time monitoring and optimization of system performance bottlenecks, further enhancing the efficiency of robot task processing, and in the later experiments, FCS- DT shows high accuracy and recognition speed, and the architecture is highly efficient for the computation of complex tasks of robots. Therefore, this architecture has practical value for improving the processing efficiency of robot complex tasks.