Unmanned Surface Vehicle Navigation Optimization Integrating Edge Computing and AI Control

Aiming at the problem of poor stability and safety of unmanned surface vehicles (USVs) during navigation, a heading control strategy based on fuzzy adaptive PID controller and edge computing integration is proposed to achieve efficient and accurate navigation control. First, a mathematical model of the navigation dynamics of USVs is established, taking into account disturbances such as wind and waves, and incorporating feedback guidance from the GPS positioning system to design a heading control process. Second, a fuzzy adaptive PID controller is designed to adjust the control parameters in real time with edge computing nodes to improve the heading tracking accuracy and anti-interference performance. Simulation results show that the proposed control algorithm significantly outperforms traditional PID control in heading control, both under disturbed and undisturbed conditions, verifying the feasibility of the algorithm. Real-world experiments demonstrate that the fuzzy adaptive PID controller achieves smaller trajectory errors and shorter navigation times.