Human-Following Control Method Based on Adaptive Recurrent PID Controller With Self-Tuning Filter

The research on human-following robot is important for practical applications. It is a hot field of human–machine technology. This article proposes an adaptive recurrent proportional integral differential (PID) control algorithm with self-tuning filter based on vision to address the issue of insufficient recognition accuracy of specific following targets in the presence of occlusion, multiple people, or deformation. It also aims to further improve the control accuracy and immunity of a human-following robot. First, a depth camera-based red green blue (RGB) picture and a depth image are acquired. The person reidentification algorithm and the YOLOv8 algorithm are used to detect and track the targets. The spatial position information of the targets is calculated by the depth image. Additionally, the orientation proportional differential (PD) controller and the speed proportional integral (PI) controller are built. Its foundation is the discrepancy between the relative posture of the user and the robot. In order to minimize sensor data fluctuations and lessen the negative impacts of relative positional instability, a self-tuning filter is developed. To remember the relative postures between the robot and the user in the history window, an adaptive recurrent mechanism is suggested. The controller has the ability to output the control quantity in an adaptive manner based on the current system state. Finally, experiments are conducted to verify the reliability of the proposed method. The experimental findings demonstrate that the visual pedestrian tracking algorithm proposed in this article is highly adaptable. Compared to the traditional PID, fractional-order PID, and virtual spring model, our method demonstrates significant enhancements, reducing the average distance error by 64.29%, 57.14%, and 60.52% in steering scenarios, and by 42.86%, 40.00%, and 40.00% in straight-ahead scenarios, respectively.