Researchers develop 'neural lander' to land drones smoothly

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. For many experts developing unmanned aerial vehicles, landing multi-rotor drones smoothly remains a challenge. This is due to complex turbulence being created by the airflow from each rotor bouncing off the ground as the ground grows ever closer during a descent.