Autonomous navigation control based on improved adaptive filtering for agricultural robot

Abstract

Under the complex agricultural operation environment, reliable navigation system is the basic guarantee to realize the agricultural robot automated operation. This study focuses on improving navigation accuracy and control accuracy and conducts related research on autonomous navigation control of agricultural robots. This article discusses the advantages of using strict convergence criteria and combining Sage–Husa adaptive filtering with strong tracking Kalman filtering and then proposes an improved adaptive Kalman filter algorithm. The new algorithm can effectively suppress the filter divergence, improve the dynamic performance of the filter, and ensure its better filtering accuracy and strong adaptive ability to improve navigation accuracy of GPS. Further variable structure switching method is used to prevent proportional integral differential (PID) controller integral saturation phenomenon, which effectively solves the controller over-saturation problem. And combining this method with an improved adaptive filtering algorithm not only can effectively inhibit control interference but also achieve the anti-saturation effect, thereby enhancing the stability and accuracy of the control system. Finally, the simulation and experiment of the new method show that the proposed method greatly improves the ability of the filter to suppress divergence and control precision.