General Atomics says that it has successfully integrated and flight-tested Agile Condor, a podded, artificial intelligence-driven targeting computer, on its MQ-9 Reaper drone as part of a technology demonstration effort for the U.S. Air Force. The system is designed to automatically detect, categorize, and track potential items of interest. It could be an important stepping stone to giving various types of unmanned, as well as manned aircraft, the ability to autonomously identify potential targets, and determine which ones might be higher priority threats, among other capabilities.
The California-headquartered drone maker announced the Agile Condor tests on Sept. 3, 2020, but did not say when they had taken place. The Reaper with the pod attached conducted the flight testing from General Atomics Aeronautical Systems, Inc.'s (GS-ASI) Flight Test and Training Center in Grand Forks, North Dakota.
Computing at the edge has tremendous implications for future unmanned systems, GA-ASI President David R. Alexander said in a statement. GA-ASI is committed to expanding artificial intelligence capabilities on unmanned systems and the Agile Condor capability is proof positive that we can accurately and effectively shorten the observe, orient, decide and act cycle to achieve information superiority. GA-ASI is excited to continue working with AFRL [Air Force Research Laboratory] to advance artificial intelligence technologies that will lead to increased autonomous mission capabilities."
Defense contractor SRC, Inc. developed the Agile Condor system for the Air Force Research Laboratory (AFRL), delivering the first pod in 2016. It's not clear whether the Air Force conducted any flight testing of the system on other platforms before hiring General Atomics to integrate it onto the Reaper in 2019. The service had previously said that it expected to take the initial pod aloft in some fashion before the end of 2016.
"Sensors have rapidly increased in fidelity, and are now able to collect vast quantities of data, which must be analyzed promptly to provide mission critical information," an SRC white paper on Agile Condor from 2018 explains. "Stored data [physically on a drone] ... creates an unacceptable latency between data collection and analysis, as operators must wait for the RPA [remotely piloted aircraft] to return to base to review time sensitive data."
"In-mission data transfers, by contrast, can provide data more quickly, but this method requires more power and available bandwidth to send data," the white paper continues. "Bandwidth limits result in slower downloads of large data files, a clogged communications link and increased latency that could allow potential changes in intel between data collection and analysis. The quantities of data being collected are also so vast, that analysts are unable to fully review the data received to ensure actionable information is obtained."
This is all particularly true for drones equipped with wide-area persistent surveillance systems, such as the Air Force's Gorgon Stare system, which you can read about in more detail here, that grab immense amounts of imagery that can be overwhelming for sensor operators and intelligence analysts to scour through. Agile Condor is designed to parse through the sensor data a drone collects first, spotting and classifying objects of interest and then highlighting them for operators back at a control center or personnel receiving information at other remote locations for further analysis. Agile Condor would simply discard "empty" imagery and other data that shows nothing it deems useful, not even bothering to forward that on.
"This selective 'detect and notify' process frees up bandwidth and increases transfer speeds, while reducing latency between data collection and analysis," SRC's 2018 white paper says. "Real time pre-processing of data with the Agile Condor system also ensures that all data collected is reviewed quickly, increasing the speed and effectiveness with which operators are notified of actionable information."
See more here:
MQ-9 Reaper Flies With AI Pod That Sifts Through Huge Sums Of Data To Pick Out Targets - The Drive