Researches have demonstrated that stratospheric balloons guided by AI can "pursue a long-term strategy for positioning themselves about a location on the Equator even when precise knowledge of buffeting winds is not known," which would "open up a range of commercial and scientific applications for probing Earth’s atmosphere and that of other planets."
The goal of an autonomous machine is to achieve an objective by making decisions while negotiating a dynamic environment. Given complete knowledge of a system’s current state, artificial intelligence and machine learning can excel at this, and even outperform humans at certain tasks. But real-world deployment of automated machines is hampered by environments that can be noisy and chaotic, and which are not adequately observed. The difficulty of devising long-term strategies from incomplete data can also hinder the operation of independent AI agents in real-world challenges. Fixed-volume balloons, known as super-pressure balloons, are often used to carry out unmanned experiments in the upper atmosphere. Station-keeping is the act of maintaining the position of such a balloon within a certain horizontal distance of a ground location (the station). This involves changing the balloon’s height to move it between regions in which winds blow in different directions — when the balloon is driven away from its station by winds at one height, it moves to a different height where the winds can blow it back again.
Applications as such would allow "long-term environmental monitoring of air quality over cities, of carbon fluxes from heat-stressed forests and of regions of thawing permafrost" as well as "monitoring of animal migration routes and illicit trafficking of goods and people across borders."