Black Swift Technologies (BST) has announced its Automated Emergency Safe Landing (AESL) functionality for UAS.

When equipped with the functionality, Black Swift’s S2 UAS is able to capture and classify images, at altitude, which allows UAS to autonomously identify a safe landing area in the event that something goes wrong, which the company notes is extremely important for safe beyond line of sight (BLOS) flights.

BST adds that this functionality processes large amounts of data quickly and efficiently, allowing objects and terrain to be identified to be avoided in order to land the aircraft without harm to people or property.

“Our emphasis is to make UAS operations safer for both operators and the public,” says Jack Elston, Ph.D., CEO of Black Swift Technologies.

“The goal of AESL is to be able to take a snapshot and within 60 seconds of something like a catastrophic engine failure, be able to identify a landing zone, calculate a landing trajectory, and safely land a UAS away from people and obstacles. We remain convinced that a thorough understanding and integration of artificial intelligence and machine learning can help serve as a catalyst for accelerating UAS growth and adoption industry-wide.”

BST developed the AESL functionality thanks to a NASA SBIR Grant that it was awarded. The company also leveraged an ongoing collaboration with a Colorado-based technology company called Luxonis LLC that specializes in embedded machine learning, artificial intelligence, and computer vision, from concept through custom hardware, firmware, software and UI/UX.

“This technology uses video or still imagery of the ground to determine what those objects are (Figure 1), and classifies them as humans, vehicles, and/or structures—things you have to avoid at all costs, even if it’s at the expense of the aircraft—to identify safe landing areas for a UAS in distress,” explains Brandon Gilles, CEO, Luxonis LLC.

“Leveraging machine vision and artificial intelligence, AESL enables a human-like perception of the world where autonomy doesn’t have to rely entirely on GPS, altimeters, or the like. This system can visually understand what’s around it and make decisions accordingly, in real-time.”

According to BST, the AESL functionality can serve as a “significant stepping stone” towards obtaining FAA exemptions for safe BLOS flights, but the company points out that the “most striking feature” that observers and users are describing is the “size of the components and their power requirements”—which are considered pretty low, BST says— for what’s actually doing this image capture/processing onboard the aircraft.

“This is a very complex solution,” says Austin Anderson, Machine Learning lead engineer, Black Swift Technologies.

“It is a robust, onboard data collection system with a very small footprint and low power requirement. It enables real-time data collection and the ability to review massive amounts of imagery that a human alone could not. Now users can gather this high level of intelligence without having to lug around a giant, power-hungry system.”

AESL functionality is currently exclusive to BST’s purpose-built UAS platform, but Elston says that it “doesn’t preclude pairing its technology with third-party systems.”