Toward Scalable Energy-Efficient Rendezvous Optimization for Multiagent Marine Applications

Uncrewed surface vehicles (USVs) and autonomous surface vehicles (ASVs) are increasingly utilized for a variety of maritime missions, offering the benefits of enhanced safety and reduced operational costs by minimizing human risk, especially in hostile environments. Despite their clear advantages, rendezvous operations, which are essential for critical tasks such as resource exchange and recharging, present significant challenges in terms of energy efficiency and proper coordination in dynamic oceanic settings. This study proposes an energy-efficient rendezvous planning framework for autonomous vessels, focusing specifically on optimizing energy consumption and improving response times during emergency scenarios. The framework uses a multiobjective particle swarm optimization algorithm. This approach integrates propulsion optimization to reduce overall energy use in extended maneuvers while considering sea conditions and dynamic obstacles by using a visibility graph-based avoidance system. The framework proves to be robust and scalable for complex multiagent missions, consistently delivering solutions that balance energy consumption and mission time. The computation for planning with up to 15 vessels consistently remains under 5% of the total mission duration, confirming its effectiveness.