Most small autonomous fixed-wing aircraft are guided by a commercial controller, which works well for simple paths. But high winds, for instance, can cause a pilotless aircraft to go off course and crash. Demanding paths for demanding applications require new innovations for suitable performance to expand uses of such drones.
Drones equipped with better onboard intelligence offer a more capable option for collecting data in high-risk environments, like precision sampling of storms or surveying land in mountainous and hazardous terrains.
The onboard intelligence is showing promise for giving a specific kind of drone—a small, autonomous fixed-wing aircraft—an ability to follow a flight path in the face of complex aircraft dynamics and unforeseen environmental disturbances.
Now, a team of researchers is working to address this problem by developing a type of onboard guidance algorithm for precision path-following control of fixed-wing uncrewed aircraft in challenging environments. Alex Hirst is lead author of the study. Hirst is a Draper Scholar and Ph.D. student in aerospace engineering at the University of Colorado Boulder.
“The vast majority of small fixed-wing aircraft are flying very simple paths,” Hirst says. “What we want is to tease out more capability of these aircraft. Can we unlock human-level performance autonomously? Can we make it possible for an autonomous aircraft to go where humans can’t?”
Small fixed-wing aircraft typically have a one- to two-meter wingspan and a cruise speed of about 20 meters per second. Given this size, environmental conditions have a large effect on the overall aircraft performance.
In developing the controller, the team used a combination of custom firmware, software and robotics technology. They staged a battery of tests in simulated and real-world settings at the Table Mountain Test Facility in Longmont, Colorado.
In tests, the controller enabled a small autonomous fixed-wing aircraft to adjust to environmental disturbances and modulate its airspeed to traverse a demanding path faster and more accurately than a commercial guidance controller.
With nonlinear numerical model predictive control, the aircraft was, in effect, “able to see into its future,” according to Hirst. “The aircraft can answer the question, what do I need to do in the next five seconds to maintain this path?”
Advances such as the new controller will enable fixed-wing aircraft to be used for more demanding missions, including mountainous and hazardous terrain and precision sampling of storms and urban air mobility, the researchers said.
Small uncrewed aerial vehicles are growing in use, according to NASA. In 2021, more than 873,000 uncrewed aircraft systems, called UAS, but commonly referred to as drones, were registered to fly in the United States, the agency said.
Fixed-wing aircraft are most popular in the agricultural and oil and gas industries since they can cover large areas at high speed. Fixed-wing drones also have been used to monitor animal populations and detect and deter poachers.
The new capability is described in the paper “Non-linear Model Predictive Control for Agile Guidance of Fixed-Wing sUAS.” The team behind the paper include Hirst, Christopher Reale at Draper, Eric Frew at CU Boulder and John Bird at the University of Texas at El Paso.
This research was funded by the Draper Scholar Program. The program gives graduate students the opportunity to conduct their thesis research under the supervision of both a faculty adviser and a member of Draper’s technical staff in an area of mutual interest. Draper Scholars’ graduate degree tuition and stipends are funded by Draper.
Since 1973, the Draper Scholar Program, formerly known as the Draper Fellow Program, has supported more than 1,000 graduate students pursuing advanced degrees in engineering and the sciences. Draper Scholars are from both civilian and military backgrounds and Draper Scholar alumni excel worldwide in the technical, corporate, government, academic and entrepreneurship sectors.