Adaptive Rendezvous Planning Algorithm for Energy-Efficient Marine Operations

In recent years, autonomous surface vehicles (USV/ASV) have found increasingly widespread application in various maritime contexts, thanks to advances in automation and artificial intelligence. However, the limited onboard energy availability still represents an obstacle to their operational autonomy, especially when undertaking long-duration missions or covering vast oceanic areas becomes necessary. To overcome this challenge, the present work proposes a rendezvous architecture in which a mother ship, equipped with a launch and recovery system (LARS), provides energy and logistical support to the autonomous drone. The planning method is based on a global optimization model that employs Particle Swarm Optimization and aims to minimize collective energy consumption, while also taking into account the energy limits and propulsive capabilities of each vehicle, without neglecting the goal of reducing mission times. Additionally, the algorithm integrates an obstacle management system based on the visibility graph, enabling safe and effective solutions even in complex scenarios. Simulation results highlight the ability to deliver near-real-time solutions and to maintain robust performance even in particularly challenging weather conditions characterized by rapid changes.