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Autonomous Technology Enables Drones To Dodge Other Air Traffic
February 20, 2020

In the drone industry, it’s called “the detect and avoid problem.” Enabling drones to sense nearby aircraft and move out of their way has long been one of the most formidable barriers between a technology narrowly confined to specialized applications and one reaching its potential.

Now, according to information provided by Virginia Tech, researchers at the Virginia Tech Mid-Atlantic Aviation Partnership (MAAP) have moved the industry closer to a solution for one of its key challenges with a real-world evaluation of an “end-to-end” detect and avoid system: A set of components that allows a drone not only to detect intruders, but to maneuver autonomously out of the way.

There are countless beneficial applications for drones,” said Mark Blanks, the director of the Virginia Tech Mid-Atlantic Aviation Partnership (MAAP). “But in order for them to be practical and scalable, the industry needs technology that’s proven it can enable much greater autonomy, especially in detect and avoid.”

MAAP is one of seven test sites designated by the Federal Aviation Administration (FAA) to lead the research supporting the integration of drones into the national airspace. The tests were the culmination of a year-long project called RAAVIN, the latest installment in a long-running collaboration between MAAP and the National Aeronautics and Space Administration (NASA) investigating potential solutions to detect and avoid.

One reason detect-and-avoid technology is so crucial for the industry is because it can enable longer-distance flights by releasing a drone from its dependence on the eyesight of its ground-based pilot or a nearby visual observer.

Federal law requires all aircraft to have a way to see and avoid other air traffic. For drones to share the airspace safely, they need to be able to meet this requirement. But while the pilot of a manned aircraft can always scan the airspace from the cockpit, the pilot of a drone loses the ability to ensure the airspace is clear as soon as the drone flies beyond the point where they can visually scan its surroundings.

Consequently, current FAA regulations for commercial drone flights stipulate that the drone must stay within the operator’s visual line of sight.

The line-of-sight requirement can be waived if the operator can make a case that the specific operation they’re proposing can be done safely. But for drones to reach their economic potential those longer flights will need to be routine, not authorized by individual approvals meted out case by case. That won’t happen until researchers can come up with a technology, or a set of technologies, that can replicate the ability of a pilot’s eyes to scan the sky.

The leading contenders are optical sensors, acoustic sensors, and radar. But sensing is only a third of the puzzle: the system also has to be able to detect unsafe conditions and direct an appropriate avoidance maneuver.

“We’re just now getting to the point where these three components — the detect, alert, and avoid piece — are mature enough to be able to assemble them and get good results from the test,” said John Coggin, MAAP’s chief engineer, who oversaw the RAAVIN project.

To put one promising system through its paces, the research team outfitted a multirotor drone with a state-of-the art Echodyne radar and a NASA detect-and-avoid software system called ICAROUS.

At a rural test facility near Blacksburg, the team executed a series of potential encounter scenarios between the drone and “intruder” aircraft — another drone, operated by NASA, and a Cessna flown by pilots from Liberty University — in which the intruder appeared to be on a path that would take it too close to the drone.

The results of the test: the radar and navigation algorithms worked together successfully, the drone maneuvered out of the way to preserve a hockey-puck-shaped buffer zone defining a safe distance between itself and other aircraft.

Kriz, Coggin, and the MAAP team are continuing to work with NASA to tackle additional challenges, optimizing the capability and reliability of the radar and algorithms to move closer to a practical solution for detect and avoid.

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