Caltech engineers have developed Neural-Fly, a tornado-resistant drone that can survive just about any kind of weather. More specifically, Neural-Fly includes a deep-learning method capable of helping drones cope with new and unknown wind conditions in real-time just by simply modifying a few key parameters. It was tested at Caltech’s Center for Autonomous Systems and Technologies (CAST) using its Real Weather Wind Tunnel.
Caltech’s Real Wind Tunnel is essentially a custom 10-foot-by-10-foot array consisting of over 1,200 small computer-controlled fans that enable engineers to simulate everything from a light gust to a tornado. Neural-Fly is able to navigate these challenges by using a separation strategy that only requires one to update a few parameters of the neural network in real-time. Remember that time the Blue Waters supercomputer was used to recreate the deadly EF-5 ‘El Reno’ tornado?
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We have many different models derived from fluid mechanics, but achieving the right model fidelity and tuning that model for each vehicle, wind condition, and operating mode is challenging. On the other hand, existing machine learning methods require huge amounts of data to train yet do not match state-of-the-art flight performance achieved using classical physics-based methods. Moreover, adapting an entire deep neural network in real time is a huge, if not currently impossible task,” said Michael O’Connell, Caltech Graduate Student.
