Drones Learning To Fly On There Own #drone #droneday
Cool article from Wired about drones pushing ‘autopilot’ to the max.
Researchers have developed a system that allows drones to autonomously navigate an obstacle course of gates with 100 percent accuracy—that is, the robots don’t crash into something and explode. Not only that, because of the clever way the researchers trained the drones, the machines can adapt if a wily human moves a gate mid-run, completing a course that looks different than when they started. They run a bit slow at the moment compared to human pilot, sure, but they’ll only get faster from here.
But a new class of machines are beginning to sense their world more like we do. Boston Dynamics, for instance, makes the famous SpotMini robot dog. This machine doesn’t use lidar because lidar is computationally and energetically expensive. So instead, a handler remote-controls the machine through an environment as cameras capture its surroundings. Armed with this information, the robot can then walk the same route autonomously, using its cameras to eyeball a now-familiar world.
This new drone system works in much the same way. You can’t bolt a bulky lidar on a drone and expect it to get off the ground, so this system also runs on cameras. The researchers trained the drones by, well, holding them and “flying” them through the obstacle course first (comical mouthed airplane noises excluded), like SpotMini first walking a route. This allowed them to collect images, tens of thousands of them. The researchers used all this data to train a neural network on how to fly through the obstacle course, not with a detailed 3-D lidar map, but with sight.
Welcome to drone day on the Adafruit blog. Every Monday we deliver the latest news, products and more from the Unmanned Aerial Vehicles (UAV), quadcopter and drone communities. Drones can be used for video & photography (dronies), civil applications, policing, farming, firefighting, military and non-military security work, such as surveillance of pipelines. Previous posts can be found via the #drone tag and our drone / UAV categories.
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