Research on Path Planning and Obstacle Avoidance for Unmanned Surface Vehicles Based on Electronic Navigational Charts

Unmanned Surface Vehicles (USVs) play a crucial role in marine missions, but their global path planning and dynamic collision avoidance capabilities in complex environments still face significant challenges, with traditional algorithms struggling to meet practical requirements. To address the collision avoidance path planning problem for USVs, this paper proposes a global-local collaborative path planning framework based on electronic navigational charts (ENCs). Firstly, an ENC environmental model is constructed following the International Hydrographic Organization (IHO) S-57 standard. The global path generation is optimized using an improved $\mathbf{A}^{*}$ algorithm, with redundant node removal and path smoothing techniques employed to enhance path quality. Second, Model Predictive Control (MPC) is introduced for local collision avoidance. By integrating the mathematical model of the USV and navigation constraints, real-time avoidance of dynamic obstacles is achieved. Finally, a hierarchical collaborative architecture is designed to integrate global path planning and local optimization. Simulation experiments demonstrate that the proposed framework effectively balances path planning efficiency and dynamic collision avoidance safety, providing a viable solution for the engineering applications of USVs.