Airbus is expanding its foray into new autonomy for aircraft, hoping to use artificial intelligence and machine learning to build certifiable systems for both urban air mobility and — eventually — single-pilot operations in commercial aircraft.
Copyright by www.aviationtoday.com
The project, now called ‘Wayfinder’ and housed under Airbus’ A³ unit in Silicon Valley, began as part of the unmanned Vahana eVTOL development project , according to Cedric Cocaud, chief engineer at Wayfinder. Initially, the goal was to leverage technologies not traditionally used in aerospace to bring awareness and decision-making capabilities to the Vahana demonstrator aircraft.
“We ended up working on machine learning algorithms using cameras, and the use case that we had was when the Vahana aircraft takes off and starts flying straight, it’s about detecting a drone in front of it and providing an avoidance path around that drone,” Cocaud told Avionics International . “The initial focus was really about how far can we see the drone, how robustly can we detect it, and then can we provide an avoidance solution in time and keep a safe distance with respect to the drone.”
In addition to building the beginnings of an airborne detect-and-avoid system, Cocaud’s team taught the aircraft to interpret the ground beneath it to recognize safe landing locations. However, according to Zach Lovering, vice president of urban air mobility systems at Airbus, these capabilities were not actively tested during the Vahana’s flight test regimen, which was successfully completely earlier this month.
“The vehicle carries a sense and avoid system which is actively collecting data to enable real-time generated trajectory deviations to avoid obstacles in flight,” Lovering told Avionics in an interview earlier this summer. “However, it will remain a passive data collection payload while the vehicle performance envelope is being evaluation.”
As the Vahana team presented their work on autonomy systems to the rest of the company, it became clear to Airbus executives that the technology was relevant for more than just the Vahana — it was actually applicable to the whole line of Airbus products.
“We decided to create our own group, about a year ago, that has this unique expertise in Airbus on machine learning for perception and decision-making,” Cocaud said. “We shifted our attention from purely focusing on UAM to focusing both on air taxis as well as single-pilot operations, meaning autonomous commercial aircraft.”
Since the rise of large-scale commercial aviation in the 1950s, requirements for the number of technical crew-members aboard an aircraft have fallen from five to two qualified pilots for short-haul flights. Spurred by both carrot and stick — the promise of cost reduction and the looming pilot shortage — the industry is exploring what systems would be necessary to enable single-pilot operations without impacting safety.
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There is, of course, significant resistance to this effort. The Air Line Pilots Association (ALPA) maintains that the most important safety assets on a passenger or cargo airliner are “at least two adequately rested, fully qualified, and well-trained pilots.” […]
Read more – www.aviationtoday.com
Airbus is expanding its foray into new autonomy for aircraft, hoping to use artificial intelligence and machine learning to build certifiable systems for both urban air mobility and — eventually — single-pilot operations in commercial aircraft.
Copyright by www.aviationtoday.com
“We ended up working on machine learning algorithms using cameras, and the use case that we had was when the Vahana aircraft takes off and starts flying straight, it’s about detecting a drone in front of it and providing an avoidance path around that drone,” Cocaud told Avionics International . “The initial focus was really about how far can we see the drone, how robustly can we detect it, and then can we provide an avoidance solution in time and keep a safe distance with respect to the drone.”
In addition to building the beginnings of an airborne detect-and-avoid system, Cocaud’s team taught the aircraft to interpret the ground beneath it to recognize safe landing locations. However, according to Zach Lovering, vice president of urban air mobility systems at Airbus, these capabilities were not actively tested during the Vahana’s flight test regimen, which was successfully completely earlier this month.
“The vehicle carries a sense and avoid system which is actively collecting data to enable real-time generated trajectory deviations to avoid obstacles in flight,” Lovering told Avionics in an interview earlier this summer. “However, it will remain a passive data collection payload while the vehicle performance envelope is being evaluation.”
As the Vahana team presented their work on autonomy systems to the rest of the company, it became clear to Airbus executives that the technology was relevant for more than just the Vahana — it was actually applicable to the whole line of Airbus products.
“We decided to create our own group, about a year ago, that has this unique expertise in Airbus on machine learning for perception and decision-making,” Cocaud said. “We shifted our attention from purely focusing on UAM to focusing both on air taxis as well as single-pilot operations, meaning autonomous commercial aircraft.”
Since the rise of large-scale commercial aviation in the 1950s, requirements for the number of technical crew-members aboard an aircraft have fallen from five to two qualified pilots for short-haul flights. Spurred by both carrot and stick — the promise of cost reduction and the looming pilot shortage — the industry is exploring what systems would be necessary to enable single-pilot operations without impacting safety.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
There is, of course, significant resistance to this effort. The Air Line Pilots Association (ALPA) maintains that the most important safety assets on a passenger or cargo airliner are “at least two adequately rested, fully qualified, and well-trained pilots.” […]
Read more – www.aviationtoday.com
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