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Watch: NASA’s AI Drone Takes on DRL Pilot

A drone piloted by NASA’s artificial intelligence has taken on a DRL FPV pilot, with interesting results.  If futurists are to be believed, most of our jobs are under threat from the rise of robotics, artificial intelligence and automation. The ironic thing is that some of the first to go could be drone pilots; a […]

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#BLACKFRIDAY2017 deals at Seagull UAV

Up to 35% on camera control systems and camera cables !

#BLACKFRIDAY2017 at Seagull UAV

Click HERE to see all #BLACKFRIDAY2017 deals! (active from Thursday 11:59pm, CET)

The Madness will continue for 3 days! No coupons needed!

Best regards,

The Seagull UAV Team

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Swiss pilot Dario Neuenschwander wins the FAI Drone Racing World Cup title 2017

Dario Neuenschwander victorious in the FAI Drone Racing World Cup 2017, ahead of Germany’s Andreas Hahn and France’s Thomas Grout Junior pilots dominate, with three under 18s in the top five – an Air Sports World Cup first The 2017 World Cup attracts more than 430 different pilots from 37 countries Switzerland’s Dario Neuenschwander hurtled […]

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Human vs AI drone racing

From NASA JPL

The race, held on Oct. 12, capped off two years of research into drone autonomy funded by Google. The company was interested in JPL's work with vision-based navigation for spacecraft -- technologies that can also be applied to drones. To demonstrate the team's progress, JPL set up a timed trial between their A.I. and world-class drone pilot Ken Loo.

The team built three custom drones (dubbed Batman, Joker and Nightwing) and developed the complex algorithms the drones needed to fly at high speeds while avoiding obstacles. These algorithms were integrated with Google's Tango technology, which JPL also worked on.

The drones were built to racing specifications and could easily go as fast as 80 mph (129 kph) in a straight line. But on the obstacle course set up in a JPL warehouse, they could only fly at 30 or 40 mph (48 to 64 kph) before they needed to apply the brakes.

"We pitted our algorithms against a human, who flies a lot more by feel," said Rob Reid of JPL, the project's task manager. "You can actually see that the A.I. flies the drone smoothly around the course, whereas human pilots tend to accelerate aggressively, so their path is jerkier."

Compared to Loo, the drones flew more cautiously but consistently. Their algorithms are still a work in progress. For example, the drones sometimes moved so fast that motion blur caused them to lose track of their surroundings.

Loo attained higher speeds and was able to perform impressive aerial corkscrews. But he was limited by exhaustion, something the A.I.-piloted drones didn't have to deal with.

"This is definitely the densest track I've ever flown," Loo said. "One of my faults as a pilot is I get tired easily. When I get mentally fatigued, I start to get lost, even if I've flown the course 10 times."

While the A.I. and human pilot started out with similar lap times, after dozens of laps, Loo learned the course and became more creative and nimble. For the official laps, Loo averaged 11.1 seconds, compared to the autonomous drones, which averaged 13.9 seconds.

But the latter was more consistent overall. Where Loo's times varied more, the A.I was able to fly the same racing line every lap.

"Our autonomous drones can fly much faster," Reid said. "One day you might see them racing professionally!"

Without a human pilot, autonomous drones typically rely on GPS to find their way around. That's not an option for indoor spaces like warehouses or dense urban areas. A similar challenge is faced by autonomous cars.

Camera-based localization and mapping technologies have various potential applications, Reid added. These technologies might allow drones to check on inventory in warehouses or assist search and rescue operations at disaster sites. They might even be used eventually to help future robots navigate the corridors of a space station.

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