Artificial Intelligence and Embedded Computing for Unmanned Vehicles
May 01, 2020 | BY: David Jedynak
Published in Military & Aerospace Electronics
Written by Jamie Whitney
The two most prevalent terms in military and civilian technology represented little more than science fiction a generation ago. But today, unmanned vehicles and artificial intelligence (AI) command center stage in any discussion of future military requirements for platforms, tactics, techniques, and procedures.
Unmanned vehicles, in the form of unmanned aerial vehicles (UAVs), arrived on the scene first, but how the military wants to use them and other platforms — unmanned ground vehicles (UGVs), unmanned surface vehicles (USVs), unmanned underwater vehicles (UUVs) and unmanned space vehicles (USVs) — in the future had to wait for at least rudimentary AI.
Each of those has its own unique operational environments that require specific AI capabilities to make autonomous underwater vehicles (AUVs) practical. Sandeep Neema, program manager in the U.S. Defense Advanced Research Project Agency (DARPA) Information Innovation Office (I2O) in Arlington, Va., says some of the most difficult unmanned technology challenges involve UUVs.
“While each evaluation environment is distinctive, undersea environments present a unique set of challenges,” Neema explains. “In these environments, things move much more slowly, missions can take longer due to harsh environmental conditions, and the limits of physics and navigation/sensing/communications issues exacerbate the challenges. Advanced autonomy could significantly aid operations in the underwater domain.”
Smaller, faster processors and enhanced onboard memory have expanded the capabilities of embedded computing greatly across the range of unmanned vehicles, but especially on smaller platforms like hand-launched UAVs, UUVs, and UGVs operating underground.
“There’s nothing magical to make AI deployable,” says David Jedynak, chief technology officer at the Curtiss-Wright Corp. Defense Solutions segment in Ashburn, Va. “It comes down to are there chips that can run what we need to run and fit on the platform? Are the parts available from industry and are we allowed to use them in the defense market? There are some chip makers in the broad tech industry that aren’t interested in the defense market and they just won’t talk to you. So we can’t just do anything we want with those chips. At the end of the day, it’s about the engineering support.
“The whole point of AI is the upper level DOD [U.S. Department of Defense] policy — the third offset strategy — which is why we are doing a lot of this. The DOD strategy is we are going to get machine learning and cyber-hardened equipment to the services, such as man-machine interfaces. That’s a huge driving policy force behind all this, getting AI to the battlefield to help the warfighter be more effective, using machine learning to provide greater capabilities beyond what the individual warfighter can do now.”