Participants: LTU Division of Space technology and Swedish Space Corporation (SSC).

This project ended on January 24, 2020, when Kanika Garg graduated with a doctorate, after four years of collaboration. Below the abstract of her thesis “Autonomous Navigation System for High Altitude Balloons”, which can be downloaded here.

Abstract
High-altitude scientific balloons are platforms for space and environmental research as well as for testing future spacecraft and instruments. However, operating such balloons is a challenging task. Balloons work on the principle of buoyancy, and once deployed in the atmosphere, they are subjected to various dynamical and thermal forces. These forces make the balloon flight complex, as they are dependent upon various atmospheric parameters, which are not easy to estimate. Furthermore, a few hours after deployment, a high-altitude balloon reaches equilibrium in terms of buoyancy and floats in the direction of the winds, making the balloon flight uncertain as winds are not known to a great extent at such altitudes.

To alter the trajectory, the balloon has to be taken to different wind layers with different wind directions or speeds. Presently, to explore the wind layers the balloon itself is used as a probe. The two manoeuvres that are used by the balloon pilot for exploring these wind layers are ballasting and venting. However, the number of ballasting and venting operations per flight is limited due to a limited amount of lift gas and ballast material, and continuous search for wind layers is thus not possible. As a result, balloon trajectory forecasting poses several challenging problems since the subject is both complex and multidisciplinary.

Consequently, balloon mission preparation requires an accurate and reliable prediction methodology for both weather and trajectory, in order to accomplish the mission successfully. This research work focusses on two aspects of ballooning: (a) determination of balloon ascent and (b) finding an optimalsequenceofmanoeuvresinordertonavigateballoonsautonomously. To solve problem (a), a standard analytical model, a fuzzy model, and a statistical regression model are developed and compared to predict the zero-pressure balloon ascent. To solve problem (b), different sensors and data models are studied in order to understand the environment in which these scientific balloons fly and challenges associated with that. Next, reinforcement learning algorithms are applied to optimize the manoeuvres and to allow for autonomous flights.

Contact

Supervisor Prof. Thomas Kuhn
[email protected]