This article was written by Nicholas Martin, Senior Lecturer in Aerodynamics, Northumbria University, NewcastleThis article is republished from The Conversation under a Creative Commons license. Read the original article.


Birds have long inspired humans to create their own ways to fly. We know that soaring bird species that migrate long distances use thermal updrafts to stay in the air without using up energy flapping their wings. And glider pilots similarly use thermals currents and other areas of rising air in order to remain airborne for longer.

Yet, while we’ve mastered gliding through these updrafts using various instruments, the exact mechanisms that allow birds to soar are still unknown. But a team of researchers from California and Italy have made some telling steps towards answering this question using artificial intelligence (AI). And it could lead to new developments in navigation systems for aircraft, with particular implications for creating drones that can stay airborne for very long periods of time.

The aim of the study, published in Nature, was to train a small two-metre wingspan autonomous glider to fly in thermals, just like a real bird would. The glider was programmed with a kind of AI known as machine learning that enabled it to work out how to use the air currents to stay in the air for longer.

Machine learning is an alternative approach to programming a computer to do a complex task. Rather than feeding a computer (or autonomous glider in this case) a set of instructions telling it how to do something, you tell the computer how you would like it to respond and reward it when it does the right thing.

Over time it will learn what things are rewarded and will tend to do these behaviours instead. This technique is how computer programs such as Google’s AlphaGo can learn to play the board game Go and then beat professional players, a feat simply not possible with conventional programming techniques.

Glider pilots look for updrafts to stay airborne. Shutterstock

This type of machine learning is called reinforcement learning and it relies on a large amount of input data being fed to the computer in order for it to learn what actions will provide it with rewards. For the researchers programming the autonomous glider, the input data consisted of specialised instruments capable of reading the change in upwards (vertical) wind strength. The instruments were able to determine these changes along the length of the glider (longitudinally) and from one wing tip to the other (laterally). The sensors were able to make these measurements ten times every second.

This data was then used to make flight adjustments to what is known as the bank angle of the of the glider. A well-balanced aeroplane with its wings level has zero bank angle and will fly in a straight line. Tilting the wings and increasing the bank angle will make the plane turn. In the study, the glider was rewarded if the change in upward wind speed along its flight path increased. In other words, if the glider was flying into an updraft.