Task-Coordinated Path Optimization for Grouped Unmanned Surface Vehicle Formations

This study proposes an integrated task–path cooperative optimization method to address the suboptimal solutions caused by decoupled task allocation and path planning for grouped multi-USV formations. First, an integrated optimization model is established within a hierarchical dynamic closed-loop framework, incorporating a persistent ocean current disturbance of 0.12 m/s to ensure practical environmental realism. Furthermore, efficient solution algorithms are developed: an enhanced Hungarian algorithm for task allocation and a Sine Cosine Algorithm-optimized Artificial Potential Field (SCA-APF) method to resolve local minima. The simulation results demonstrate that the proposed method reduces the weighted total cost by 11.1% and improves task allocation efficiency by over 80.5% compared to improved genetic algorithms. In dynamic environments, the framework achieves an over 99% task completion rate. Crucially, the system maintains real-time responsiveness with per-step computation times below 0.1 s even for a swarm size of N = 32, proving its scalability and suitability for large-scale maritime coordination.