A novel policy for coordinating a hurricane monitoring system using a swarm of buoyancy-controlled balloons trading off communication and coverage

This paper introduces a novel architecture for hurricane monitoring aimed at maximizing the collection of critical data to enhance the accuracy of weather predictions. The proposed system deploys a swarm of controllable balloons equipped with meteorological sensors within the hurricane environment. A key challenge in this setup is managing the trade-off between maximizing area coverage for data collection and maintaining robust communication links among the balloons. To address this challenge, we propose a cost function with two conflicting components: one prioritizes area coverage, and the other focuses on repositioning to maintain communication. This cost function is optimized using an adaptive neural network-based model predictive control strategy, which enables the system to dynamically balance these competing requirements in real-time. Quantitative results from extensive simulations demonstrate the versatility and effectiveness of the proposed architecture, showing that it can achieve comprehensive communication connectivity and increased area coverage across various configurations, including different numbers of balloons and operational periods.