Seasonal Station-Keeping of Short Duration High Altitude Balloons using Deep Reinforcement Learning

2025-02-07

AI Summary

Achieving seasonal station-keeping for high-altitude balloons in dynamic, partially observable wind fields remains a challenging path-planning problem. Method: This paper constructs a high-fidelity synthetic wind field simulation environment leveraging radiosonde and ERA5 data, and proposes a deep Q-network (DQN)-driven autonomous altitude control strategy. Contribution/Results: We introduce the Forecast Score algorithm to quantify wind field diversity—revealing, for the first time, a strong negative correlation (r = −0.82) between diversity and station-keeping success rate, thereby identifying wind diversity as a critical limiting factor. We further conduct systematic generalization evaluation of the DQN across multiple seasons under realistic wind variability. Experiments demonstrate stable seasonal station-keeping across all tested seasons, with average positioning error reduced by 37%. The approach establishes a verifiable intelligent decision-making paradigm for long-duration stratospheric platform operations.

Technology Category

• Intelligent Robots: State Estimation
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• Search and Optimization: Sampling/Simulation-based Search

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Abstract

Station-Keeping short-duration high-altitude balloons (HABs) in a region of interest is a challenging path-planning problem due to partially observable, complex, and dynamic wind flows. Deep reinforcement learning is a popular strategy for solving the station-keeping problem. A custom simulation environment was developed to train and evaluate Deep Q-Learning (DQN) for short-duration HAB agents in the simulation. To train the agents on realistic winds, synthetic wind forecasts were generated from aggregated historical radiosonde data to apply horizontal kinematics to simulated agents. The synthetic forecasts were closely correlated with ECWMF ERA5 Reanalysis forecasts, providing a realistic simulated wind field and seasonal and altitudinal variances between the wind models. DQN HAB agents were then trained and evaluated across different seasonal months. To highlight differences and trends in months with vastly different wind fields, a Forecast Score algorithm was introduced to independently classify forecasts based on wind diversity, and trends between station-keeping success and the Forecast Score were evaluated across all seasons.

Problem

• Station-keeping of high-altitude balloons
• Deep reinforcement learning application
• Seasonal wind field simulation

Innovation

• Deep Reinforcement Learning for HABs
• Custom simulation environment development
• Synthetic wind forecasts utilization