With the availability of high performance miniaturized electronics, sounding balloons have become a viable options to conduct scientific experiments and commercial missions in the stratosphere, acting as a reduced size, low mass, low cost alternative to large zero-pressure or superpressure balloons. This paper explores the use of deep reinforcement learning for controlling a stratospheric sounding balloon to perform station-keeping over a specified area. In particular, we implement the deep Q-network (DQN) algorithm to learn a control policy for the balloon by exploiting different wind directions at different altitudes, reached by dropping ballast or releasing lifting gas. We conduct experiments using a simulation environment and evaluate the performance of the trained DQN model in real historical data. Our results show that the DQN algorithm can effectively learn a control policy that achieves satisfactory station-keeping with a high success rate, outperforming other, more direct control approaches. Our study presents a possible solution for the control of stratospheric sounding balloons in various applications.
Navigation of Sounding Balloons with Deep Reinforcement Learning
19.06.2023
559623 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Vertical sounding balloons for long duration flights
Elsevier | 1993
|Vertical Sounding Balloons for Long Duration Flights
Online Contents | 1994
|Vertical Sounding Balloons for Long Duration Flights
British Library Conference Proceedings | 1993
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