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The ultimate human vs artificial intelligence drone race

30 November 2017

Researchers at NASA’s Jet Propulsion Laboratory raced drones controlled by artificial intelligence against a professional human pilot…but who won?

JPL engineers recently finished developing three drones and the artificial intelligence needed for them to navigate an obstacle course by themselves. (Credit: NASA/JPL-Caltech)

After two years of research into drone autonomy funded by Google, JPL set up a timed trial through a twisting obstacle course between their AI and drone pilot Ken Loo.

Three custom drones were built (Batman, Joker & Nightwing) and complex algorithms were developed to help the drones fly at high speeds while avoiding obstacles. These algorithms were integrated with Google’s Tango technology, which JPL also worked on. 

Built to racing specifications, speeds of 80mph were possible in a straight line. As the course featured a twisting raceway, only speeds of 30-40mph were achieved before the brakes were applied. 

“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 AI flies the drone smoothly around the course, whereas human pilots tend to accelerate aggressively, so their path is jerkier.”

Loo attained higher speeds and was able to perform impressive aerial corkscrews. But he was limited by exhaustion, something the AI-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."

On the official laps, Loo averaged 11.1 seconds compared to the AI-controlled drones which averaged 13.9 seconds. But the latter was more consistent overall. Where Loo's times varied more, the AI 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 localisation 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.”

Video courtesy of NASA Jet Propulsion Laboratory 

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