Multi-Objective Path Planning for Unmanned Surface Vehicle in Stochastic Ocean Currents
Ruilin Yu, Zhiyu Luo, Jiajun Chen, Zhifu Gao, Jingyu Ru, Hongli Xu · 2025
This paper proposes a multi-objective path planning framework for unmanned surface vehicles (USVs) operating under complex and stochastic ocean current conditions. To address the challenges posed by nonlinear hydrodynamic environments, a comprehensive kinematic and dynamic model is developed, along with a state-space control formulation. The optimization objective integrates travel time, path length, and energy consumption, aiming for global path efficiency rather than traditional fixed-trajectory tracking. A dynamic programming method is adopted, with the Hamilton-Jacobi-Bellman (HJB) equation formulated to guide optimal control strategy. To solve the discretized HJB equation effectively, we employ Ant Colony Optimization (ACO), which exhibits superior convergence speed and solution quality compared to other optimization algorithms. Simulation results validate the proposed approach's capability in adapting to environmental uncertainties, achieving better trade-offs between time and energy efficiency. The method provides a flexible and robust solution for autonomous USV operations in dynamic marine environments.