Drones can fly autonomously in controlled environments or via remote control. To
In its work, Neural-Fly uses a strategyseparation, with the help of which only a few parameters of the neural network need to be updated in real time. This effect, as noted by the researchers, is achieved using a meta-learning algorithm that pre-trains the neural network that only certain key parameters need to be updated to effectively adapt to a changing environment.
“The direct and specific influence of variouswind conditions on aircraft dynamics, performance and stability cannot be accurately characterized with a simple mathematical model,” Chang says. “Instead of trying to qualify and quantify every impact of the turbulent and unpredictable wind conditions we encounter in air travel, we use a combined deep learning and adaptive control approach that allows the aircraft to learn from previous experience and adapt to new conditions on the fly.” .
Researchers tested drones connectedto the Neural-Fly, using the Real Weather wind tunnel. It's a massive experimental rig, integrating more than a thousand tiny computer-controlled fans, that allows engineers to simulate everything from a slight breeze to a full-blown storm.
Scientists note that in just 12 minutes of flightautonomous quadrocopters have learned to respond to strong winds. The drones were tasked with moving along the "eight" regardless of wind gusts, which could reach 12 m per second. The results showed that Neural-Fly quadcopters made up to four times fewer errors while following this trajectory than drones equipped with similar adaptive control algorithms that detect and respond to aerodynamic effects without deep learning.
The researchers believe that the technology will allowdrones to fully and autonomously perform necessary, but everyday tasks, such as delivering parcels or air transportation of drivers injured in an accident.
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