News & Events

05-05-22

To be truly useful, drones—that is, autonomous flying vehicles—will need to learn to navigate real-world weather and wind conditions. A team of engineers from Caltech has developed Neural-Fly, a deep-learning method that can help drones cope with new and unknown wind conditions in real time just by updating a few key parameters. [Caltech story]

Tags: research highlights GALCIT CMS Yisong Yue Soon-Jo Chung Animashree Anandkumar Xichen Shi Guanya Shi Michael O'Connell Kamyar Azizzadenesheli

05-23-19

Professors Chung, Anandkumar, and Yue have teamed up to develop a system that uses a deep neural network to help autonomous drones "learn" how to land more safely and quickly, while gobbling up less power. The system they have created, dubbed the "Neural Lander," is a learning-based controller that tracks the position and speed of the drone, and modifies its landing trajectory and rotor speed accordingly to achieve the smoothest possible landing. The new system could prove crucial to projects currently under development at CAST, including an autonomous medical transport that could land in difficult-to-reach locations (such as a gridlocked traffic). "The importance of being able to land swiftly and smoothly when transporting an injured individual cannot be overstated," says Professor Gharib who is the director of CAST; and one of the lead researchers of the air ambulance project. [Caltech story]

Tags: research highlights Morteza Gharib Yisong Yue Soon-Jo Chung Animashree Anandkumar