用数学模型自动调度海洋无人机，省电省车还多采数据。
Marine Autonomous Vehicle Fleet Scheduling to Maximise Scientific Impact
- 构建混合整数线性规划模型，自动优化无人机调度。
- 可处理数十至上百架无人机，秒级求解，显著降低能耗与车辆数。
- 支持换电、中程加速等操作，适合科研与海上任务规划者使用。
海洋科学日益依赖自主水下航行器（MAVs）获取关键环境数据以理解全球海洋系统。随着规模扩大，手动规划大规模自主舰队的路径变得指数级复杂且耗时。为此，我们提出一种混合整数线性规划（MILP）模型，用于自动化与优化MAV部署调度。该模型考虑电池容量、数据采集时间窗等严格约束，旨在最大化总数据采集量，同时最小化部署车辆数与能耗。框架一大创新在于整合传统船舶航线，使MAVs可在任务中为船只提供电池更换或加速中转。计算实验表明，该模型高度可扩展：数十架无人机调度可在秒级完成，上百架亦仅需数分钟。除日常调度外，该框架还可作为仿真工具评估‘如果’场景，并分析参数变化对策略的影响。最终解决方案生成可视化结果，提升可解释性，助力利益相关方战略决策。
原文摘要 · Abstract (English)
The marine science community increasingly relies on Marine Autonomous Vehicles (MAVs) to collect the critical environmental data required to understand global ocean systems. However, as these operations scale, manually routing and planning large autonomous fleets becomes exponentially complex and time-consuming. To address this, we propose a mixed-integer linear programming (MILP) model designed to automate and optimise MAV deployment schedules. The model accounts for strict operational constraints, including battery capacities and time windows for data collection, while aiming to maximise total data collection and minimise both the number of deployed vehicles and their energy consumption. A key novelty of this framework is integrating conventional ship itineraries, allowing MAVs to support vessels with mid-mission battery swapping or accelerated transit between waypoints. Computational experiments demonstrate that the model is highly scalable, solving routing problems for fleets of dozens of MAVs in seconds, and scaling to hundreds of vehicles in only a few minutes. Beyond operational scheduling, the framework serves as a robust simulation tool for evaluating 'what-if' scenarios and analysing the impact of varying parameters on deployment strategies. Finally, the solution generates a suite of visualisations designed to enhance explainability and support strategic decision-making for stakeholders.
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