Deep Vision Guided Mobile Robot Dual Axis PID Dynamic Real Time Adjustment Following

The real-time tracking of moving objects by social robots is crucial for ensuring object-oriented services. This paper presents a real-time following algorithm for mobile robots based on deep vision object detection. Firstly, using a binocular depth vision camera, real-time target information from a firstperson perspective is collected, and a lightweight deep learning model is employed to detect the relative position of the human body posture with respect to the social robot. Subsequently, to address the issue of unsmooth trajectory during the tracking process, a dual-axis PID algorithm is developed for cooperative control of the robot’s speed and heading angle. Given the inherent randomness in human body movement, target loss can occur during tracking. To enhance the algorithm’s robustness, a dynamic tracking mechanism is introduced. Finally, we implement a vision-driven two-wheel differential social robot following algorithm on the ROS system and validate it through three real-world experimental scenarios. The mean values of the target acquisition rate, tracked target position, and angle smoothness are found to be 0.81, 0.46, and 1.02, respectively.