Efficient Rescues at Sea: A Hierarchical Framework of Time-Sensitive Rescue Scheduling and Motion Planning for Unmanned Surface Vehicles
Liang Zhao, Fang Wang, Mingye Zhang, Yong Bai · IEEE Transactions on Intelligent Vehicles · 2025
Efficient and reliable planning for unmanned surface vehicles (USVs) is essential to ensure prompt maritime rescue. However, existing methods for practical maritime rescue are limited in two key aspects. On the one hand, rescue operations are time-sensitive, where the USVs must accomplish as many rescue tasks as possible during the early rounds to reduce potential risks and losses due to the delays. Furthermore, the constrained visibility of USVs may cause inadequate time to complete the necessary avoiding maneuvers, preventing the avoidance strategy from being activated timely. To address these challenges, we introduce a planning framework by integrating the time-sensitive rescue task allocation and a visually-compliant motion planner. The time-sensitive task allocation model uses an accumulated reward function to maximize early task completion, with a uniquely designed heuristic algorithm to find high-quality solutions. Furthermore, the motion planning framework integrates a sampling-based global planner with an online planner using quadratic programming. Both planners incorporate collision and visually-compliant Control Barrier Functions (CBFs) to ensure USV safety under constrained visibility. Extensive simulations show that our model quickly identifies high-quality solutions for both large and small-scale problems, outperforming current state-of-the-art methods. Semi-physical USV simulations demonstrate its effectiveness in navigating and responding to unknown environment under constrained visibility.