Next-generation vehicles such as drones have a hard time landing. Drone controllers usually bring the drone near the ground and then drop it. How low the drone can be brought down depends on the aerodynamics of the drone and other reactions from the ground. Since drones of the future will be carrying medicines and other fragile instruments into mysterious landscapes or hilly areas, dropping the drone isn't always desirable. To address this problem of smooth landing, researchers at CalTech's Center for Autonomous Systems and Technologies (CAST), have imbibed neural networks into their approaches.

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.

Landing multi-rotor drones smoothly is difficult. Complex turbulence is created by the airflow from each rotor bouncing off the ground as the ground grows ever closer during a descent. This turbulence is not well understood nor is it easy to compensate for, particularly for autonomous drones. That is why takeoff and landing are often the two trickiest parts of a drone flight. Drones typically wobble and inch slowly toward a landing until power is finally cut, and they drop the remaining distance to the ground.