Drones that can Recognize and Follow Passageways through Undergrowth and Rough Terrain

Another best example of advancement in the field of science and Artificial Intelligence has been set forward by researchers with the development of rough terrain rescue drone that navigates forest trails better than humans. Here’s good news for hikers and climbers who become lost or injured on some distant forest trails or remote hillside.

In Future, they won’t have to face such difficulties because scientists have come up with a rough rescue drone that can autonomously recognize and follow passageways through the undergrowth and rough terrain, using sophisticated artificial intelligence. The scientists ultimately envision an entire fleet of the rescue drones ‘able to swarm forests in search of missing people’.

Davide Scaramuzza, who led the project for the Dalle Molle Institute for Artificial Intelligence and the University of Zurich, said while drones flying at high altitudes are already being used commercially, drones cannot yet fly autonomously in complex environments, such as dense forests. It seems very challenging to develop drones that can prove successful in such areas because these are the places where a single mistake can lead to crash of the drone.

The researchers say that their new breed of quadrocopter could be rapidly deployed in large numbers to complement human rescue teams, and thus reduce response time. The device relies on small cameras and unique software.

Alessandro Giusti, fellow researcher, said instead of relying on sophisticated sensors, the drone uses very powerful artificial-intelligence algorithms to interpret the images to recognize man-made trails. If a trail is visible, the software steers the drone in the corresponding direction. The Swiss team used a ‘deep neural network’ that prompts a computer to learn to solve complex tasks.

Their research was incredibly complex: the team hiked along multiple trails in the Swiss Alps, capturing over 20,000 images of the environment with a helmet-mounted camera. Mr. Giusti said the deep neural network was able to find the correct direction in 85% of cases; in comparison, humans faced with the same task guessed correctly 82% of the time.

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