Researchers at the California Institute of Technology (Caltech) have developed a system that uses a deep neural network to help autonomous drones ‘learn’ how to land more safely and quickly, while using less power.
The system was created by Caltech’s Center for Autonomous Systems and Technologies (CAST) in a collaboration between artificial intelligence (AI) and control experts. The “neural lander”, is a learning-based controller which tracks the position and speed of the drone, and modifies its landing trajectory and rotor speed accordingly to achieve the smoothest possible landing.
“This project has the potential to help drones fly more smoothly and safely, especially in the presence of unpredictable wind gusts, and eat up less battery power as drones can land more quickly,” said Soon-Jo Chung, a professor of Aerospace at the institute.
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