Drone integration efforts lead to industry first
Virginia Tech and Mosaic ATM teamed up for a research project that resulted in the drone industry’s first demonstration and validation of a Remote Identification-based detect and avoid system for drone vs. drone encounters.
Virginia Tech researchers helped reach a new milestone in preventing drone from colliding with other drones.
Led by partner Mosaic ATM, a leading aviation research and development company, the researchers helped conduct the first ever demonstration and validation of a Remote Identification-based detect and avoid (DAA) system for drone versus drone encounters. The year-long project, which culminated in flight testing in late June and early July at Kentland Farms in Blacksburg, successfully showed the equipped drones are capable of autonomously sensing and avoiding other nearby drones.
“The industry has been gradually learning how to get drones to avoid regular aircraft, and now this is the first time we are working on having drones actively avoid each other,” said Robert Briggs, chief engineer for the Virginia Tech Mid-Atlantic Aviation Partnership (MAAP). “As drones become more commonplace and beyond visual line of sight flights become a regular practice, drone-to-drone detect and avoid can be a useful component for ensuring safe operations and this testing answers that call.”
Sponsored by the Federal Aviation Administration (FAA), this joint effort between MAAP, Mosaic ATM, and the Wireless Research Center of North Carolina marks a huge step forward in the initiative to integrate drones into the national airspace. It is also the latest achievement of MAAP’s work to advance FAA initiatives to safely integrate drones into the national airspace, which includes being a part of the agency’s drone-integration initiative, BEYOND, and helping develop regulations for flights over people.
This most recent project demonstrates and validates a prototype DAA system, which allows operators to detect and avoid other drones autonomously or manually. DAA systems were first used in traditional aviation and are now being incorporated into drone operations.
“DAA systems require knowing the real-time location of other nearby aircraft,” said Timothy Bagnall, principal analyst for Mosaic ATM. “Leveraging the FAA’s relatively new Remote Identification rule, which requires drones to continuously broadcast 3D position messages, we placed sensors on drones that allow them to detect other nearby drones and avoid colliding with them.”
Remote Identification receivers, whether mounted on a drone or stationed on the ground, can provide the real-time position information of nearby drone operations. Flight testing conducted by the team included scenarios of air only and ground only Remote Identification DAA. The prototype allows the pilot to execute avoidance maneuvers manually or autonomously and is compatible with both Wi-Fi and Bluetooth.
Researchers tested the DAA prototype system in conjunction with commercially available Remote Identification-based transmitters to assess automation performance, human factor considerations, and overall ability to maintain safe separation during live flight evaluations.
With the demonstration and validation accomplished, the project will officially conclude at the end of August. As the team works to complete simulations, data processing, and final reporting, it will focus on gathering data and evidence to consider the following questions:
- Is Remote Identification-based DAA feasible for operations in the national airspace?
- Could stronger Remote Identification broadcast power level requirements help expand beyond visual line of sight drone operations in the national airspace?
