Cooperative path planning optimization for ship-drone delivery in maritime supply operations

Abstract

Drone-assisted ship supply has recently garnered widespread attention for its faster, cheaper, and greener advantages, reshaping shore-to-vessel deliveries and expected to become fundamental to future maritime logistics. Facing challenges like time-dependent locations and coordination, we introduce a novel path planning problem for supply ship-drone delivery, in which drones launch from the supply ship to serve anchored and underway vessels. We then formulate a supply ship-drone delivery model and devise a synchronized drone rendezvous strategy that determines the rendezvous points between drones and underway vessels. To address this, we propose an adaptive ship-drone path coordination algorithm (ASDPC) that accounts for the movement of both the supply ship and vessels. The supply ship path is optimized using a grid-based approach, ensuring full vessel coverage with tailored operators and enhancing search diversity and intensity. Building upon this, drone path optimization employs the receding vessel priority delivery strategy, leveraging relative motion between the supply ship and vessels to select targets with low delays and short distances. Subsequently, a removal-and-insertion approach is applied to further coordinate multi-drone paths. Besides, with supply ship and drone parameters varying, ASDPC consistently outperforms the baseline algorithms in terms of reducing delivery cost and time, indicating the satisfactory performance and practicability of ASDPC across various scenarios. Generally, this work presents a scalable framework for drone collaboration with mobile platforms to address critical challenges in coordination and synchronization with moving targets, thereby offering new perspectives for maritime logistics operations.