Joint Path Planning and Energy Replenishment Optimization for Maritime USV–UAV Collaboration Under BeiDou High-Precision Navigation

Abstract

Highlights: What are the main findings? A novel bi-level optimization framework enables closed-loop cooperative planning between a mobile USV and multiple UAVs, significantly improving mission efficiency. A hybrid intelligence algorithm (GA + MARL) effectively solves multi-objective optimization under energy and dynamic constraints. The method achieves superior performance: >93% task completion, 8–15% shorter mission time, and ~30% less waiting time for replenishment. What is the implication of the main finding? Solves the critical endurance limitation of UAVs in large-scale maritime missions, enabling longer and more complex operations. Reduces operational costs and risks through optimized energy use and minimized UAV downtime. Demonstrates the practical value of BeiDou high-precision positioning in enhancing the autonomy and reliability of unmanned marine systems. With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high coverage efficiency but suffer from limited endurance due to restricted battery capacity, making them unsuitable for large-scale tasks alone. In contrast, USVs provide long endurance and can serve as mobile motherships and energy-supply platforms, enabling UAVs to take off, land, recharge, or replace batteries. Therefore, how to achieve cooperative path planning and energy replenishment scheduling for USV–UAV systems in complex marine environments remains a crucial challenge. This study proposes a USV–UAV cooperative path planning and energy replenishment optimization method based on BeiDou high-precision positioning. First, a unified system model is established, incorporating task coverage, energy constraints, and replenishment scheduling, and formulating the problem as a multi-objective optimization model with the goals of minimizing total mission time, energy consumption, and waiting time, while maximizing task completion rate. Second, a bi-level optimization framework is designed: the upper layer optimizes the USV’s dynamic trajectory and docking positions, while the lower layer optimizes UAV path planning and battery replacement scheduling. A closed-loop interaction mechanism is introduced, enabling the system to adaptively adjust according to task execution status and UAV energy consumption, thus preventing task failures caused by battery depletion. Furthermore, an improved hybrid algorithm combining genetic optimization and multi-agent reinforcement learning is proposed, featuring adaptive task allocation and dynamic priority-based replenishment scheduling. A comprehensive reward function integrating task coverage, energy consumption, waiting time, and collision penalties is designed to enhance global optimization and intelligent coordination. Extensive simulations in representative marine scenarios demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves around higher task completion rate, shorter mission time, lower total energy consumption, and shorter waiting time. Moreover, the variance of energy consumption across UAVs is notably reduced, indicating a more balanced workload distribution. These results confirm the effectiveness and robustness of the proposed framework in large-scale, long-duration maritime missions, providing valuable insights for future intelligent ocean operations and cooperative unmanned systems.