Study finds autonomy software needed in future drone traffic management system
By DRONELIFE Features Editor Jim Magill
As drone use scales up in the future, creating an increasingly crowded airspace at altitudes below 400 feet, a recent study by researchers at Johns Hopkins University suggests that increasing the level of autonomous operations help might create a safer air traffic management system.
The study, published in the Institute of Electrical and Electronics Engineers Computer Magazine, finds that “the best option for achieving airspace safety due to the predicted levels of congestion is likely by replacing the human-in-the-loop operations with autonomy.”
Experts predict that by 2035 there will be 65,000 UAS takeoffs and landings per hour. Currently, the busiest U.S. airports can only handle 300 commercial aircraft operations per hour, which means that a new traffic management system must be devised to accommodate the explosive growth in drone traffic.
The FAA has proposed a concept of operations for drone traffic management, but this concept relies a great deal on human control of drones.
“It’s not feasible for these processes to scale to support 65,000 operations per hour. So, we’re going to have to rely on autonomous operations,” Lanier Watkins, one of the lead authors of the study, said in an interview.
Watkins, a senior cyber research scientist at the Johns Hopkins University Applied Physics Laboratory (APL) and chair of the university’s EP Computer Science and EP Cybersecurity programs, said the research team performed a series of experiments to determine how autonomy algorithms can contribute to safety in congested airspace operations.
Among other lines of inquiry, the team investigated how autonomy algorithms react in “noisy” conditions that reflect real-world conditions in a busy airspace and whether the airspace safety promoted by the autonomy algorithms would be negated by the behavior of “rogue” drones operating in that airspace.
The researchers also performed experiments to identify what kinds of airspace risk the use of the algorithms could impose.
“The role for ensuring autonomy is to ensure that these autonomous algorithms work properly, that they don’t come across failure states and start making incorrect decisions, and then small air collisions start occurring,” Watkins said.
“It’s like a double-check on the algorithms, like looking over the algorithm’s shoulder, trying to make sure that they don’t make the airspace risky,” he said.
In their study, the team examined the feasibility of creating a UAS traffic management (UTM) system that relies heavily on the semi-autonomous operations of drones to safely transit the airspace and avoid mid-air collisions.
“We look at this from an end-to-end perspective, where UAS operators want to interact with the UTM system to be able to safely fly their UAS to deliver products to their customers, and the UTM system manages the airspace and monitors the UAS for conformance to the planned deconflicted flight paths the system provides UAS operators,” the study states.
In addition, each drone operating in the system avoids collisions with moving obstacles using its own collision avoidance software.
The study highlighted the cooperative relationship in the current air traffic management system between the drone operator the UAS Service Suppliers (USS), which comprise a select group of companies approved by the FAA to provide Low Altitude Authorization and Notification Capability (LAANC) services.
“During the UTM flight phase, the remote pilot in control and the USS both are sent data from the UAS, such as remote ID messages and flight telemetry data. This allows the UAS service supplier to perform conformance monitoring by comparing the UAS’s live telemetry data against its planned flight path and confirming it is within bounds,” the report states
In their study, the researchers added 3D analysis, “noisy” sensors, and collision avoidance algorithm assurance via airspace risk assessment to the existing system.
They also performed a Monte Carlo simulation, looking at hundreds of thousands of different scenarios to predict the probability of different outcomes in cases where there is a potential for multiple random variables.
This simulation provided three layers of separation management — flight planning, scheduling and collision avoidance — along with various safety and efficiency metrics such as small near midair collisions and real-time risk assessment.
“We found that in the scenarios that were looked at, these algorithms worked marvelously,” Watkins said.
The study found that both strategic deconfliction and conflict avoidance algorithms “contribute to airspace safety by lowering collisions and negating the effects of rogue UAS.” The team’s work was based in part on earlier studies that found that one side effect of the use of autonomous systems was delays in mission completion time.
As part of its research, the team built a “fuzzy inference system” that uses so-called fuzzy set theory to map inputs to outputs. “Given a certain input, only certain outputs are acceptable,” Watkins said.
The study’s authors acknowledge that autonomy is not “a silver bullet,” and that some autonomy algorithms might produce unknown failure states that may would make them unfit for use in an air traffic control system.
The university’s APL has been working with the FAA on similar projects for several decades Watkins said. “So, a lot of these findings have already been shared with the FAA in various ways.”
Although the FAA’s concept of operations (ConOps) for a drone traffic management system does not favor any specific implementation, “it does speak to the philosophical architecture necessary to provide the services for airspace management,” the study states.
“In reality, future airspace services will be implemented by a mix of government, industry and standards development organizations.”
Read more:
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- AIR ONE Personal eVTOL: Operating with High Lander UTM and Mission Control
Jim Magill is a Houston-based writer with almost a quarter-century of experience covering technical and economic developments in the oil and gas industry. After retiring in December 2019 as a senior editor with S&P Global Platts, Jim began writing about emerging technologies, such as artificial intelligence, robots and drones, and the ways in which they’re contributing to our society. In addition to DroneLife, Jim is a contributor to Forbes.com and his work has appeared in the Houston Chronicle, U.S. News & World Report, and Unmanned Systems, a publication of the Association for Unmanned Vehicle Systems International.
Miriam McNabb is the Editor-in-Chief of DRONELIFE and CEO of JobForDrones, a professional drone services marketplace, and a fascinated observer of the emerging drone industry and the regulatory environment for drones. Miriam has penned over 3,000 articles focused on the commercial drone space and is an international speaker and recognized figure in the industry. Miriam has a degree from the University of Chicago and over 20 years of experience in high tech sales and marketing for new technologies.
For drone industry consulting or writing, Email Miriam.
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