The global UAS market size is predicted to grow to around £39.2 billion by 2030, up from £21.9bn last year. This means that Hundreds of thousands, if not millions, of autonomous drones will be operating in uncontrolled airspace below 400 feet altitude, performing a wide range of tasks, including package delivery, traffic monitoring, and emergency assistance.

In response to this, researchers at the Institute for Assured Autonomy, used artificial intelligence (AI) to model a system that could orchestrate this increase in traffic.

“We wanted to see if different approaches using AI could handle the expected scale of these operations in a safe manner, and it did,” said Lanier Watkins, a researcher at the Institute for Assured Autonomy to techxplore.

“Our simulated system leverages autonomy algorithms to enhance the safety and scalability of UAS operations below 400 feet altitude.”

The team evaluated the impact of autonomous algorithms in a simulated 3D airspace, drawing on previous research on collision avoidance algorithms. The findings revealed that inclusion of strategic deconfliction algorithms substantially improved safety and almost eliminated airspace accidents completely. 

To test real-world conditions, the researchers introduced ‘noisy sensors’ and a ‘fuzzy inference system’ which mimic unpredictable conditions and allows the drones to calculate risk on various factors such as proximity to obstacles and adherence to planned routes. 

Recommended reading

• UK Government Commits £2m Funding to Develop Anti-Drone Technologies
• U-Space System to Unlock Swiss Drone Airspace
• Drone Comes Within 10ft of Hitting Jet, Report Reveals

“Our study considered a variety of variables, including scenarios that involve ‘rogue drones’ that deviated from their planned routes. The results are very promising,” said Louis Whitcomb, another researcher at the Institute for Assured Autonomy with techxplore. 

Going forward the researchers plan to enhance simulations with more dynamic obstacles like weather. 

“This work helps researchers understand how autonomy algorithms that protect airspace can behave when faced with noise and uncertainty in 3D-simulated airspace and underscores the need to continuously monitor the results from these autonomous algorithms to ensure they have not reached potential failure states,” continued Watkins.