Multi-Objective Bi-Level Rescue Task Planning Strategy for Unmanned Surface Vehicles with Dynamic Adjustment Mechanism

Unmanned Surface Vehicles (USVs) play a critical role in maritime rescue operations, capable of performing long-duration tasks in hazardous environments. To effectively plan and complete missions within limited resources, both tasks set quality, navigation safety, and execution efficiency should be simultaneously considered. This paper proposes a Multi-Objective Bi-level Task Planning Strategy tailored for USV rescue missions. The strategy integrates dynamic task filtering, nested optimization, and real-time path planning to address challenges related to task prioritization, obstacle avoidance, and energy efficiency. The framework adopts a two-level structure: the upper level employs a Simulated Annealing (SA) algorithm to rapidly filter and prioritize task points, while the lower level optimizes the task sequence using Ant Colony Optimization (ACO) and employs an improved Theta* algorithm for global path planning, making it more suitable for vessel navigation. This strategy significantly enhances task efficiency and safety, providing an intelligent and efficient solution for maritime emergency response.