Self-flying glider uses machine learning to navigate air currents, soar like birds
The experiment is largely aimed at understanding how birds migrate through different wind patterns.


Representational image. PxHere[/caption]"We find it very impressive as the glider had no prior knowledge about atmospheric physics or aerodynamics," Massimo Vergassola, lead study author, told AFP.Whereas several other studies have shown how fast machines can learn strategies or form algorithms to solve complex problems, thermal updrafts change nearly constantly, making the gliders' task extra taxing.By studying how the glider learned to respond to physical stimulus in flight, Vergassola and his colleagues believe that birds might also take certain physical and visual clues to help them climb thermals, saving vital energy needed for long migrations.Species such as the bar-tailed godwit and shorebird, that can fly upward of 11,500 kilometres without stopping, would not be able to do so without these skills."The (glider's) strategy shows quite good performance in ever-changing aerial environments," Vergassola said."We believe that soaring birds could in fact perform more complex planning computations or use additional navigational cues, such as clouds."

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