One team in the upcoming Baja 1000 dirt-bike race will bring a special advantage: AI that prescribes when a rider should pit, long before the need becomes obvious.
The thousand-mile trek through California and Mexico is “the perfect” environment to test GDIT’s logistics-and-maintenance AI before it heads off to rough and disconnected battlefields, a company representative said.
GDIT is teaming with AWS on Project Celerity, an AI-enabled platform for managing energy. The Army’s Advanced Research Lab has been heavily investing in how to deploy small, tactical “microgrids”—essentially energy generation and storage systems for environments where power and connectivity are absent. Those microgrids aren’t intended to simply provide power for soldiers on base but also power batteries for a growing fleet of eclectic and robotic vehicles and weapons. So predicting when and how drones or ground robots might require new batteries is a part of the challenge.
Shannon Judd, the director of global defense partners at AWS, said in an email that the military applications for the project are many. These include helping “teams conducting patrols or surveillance in remote areas, special operations forces who need to make decisions quickly without guaranteed access to full communications, and disaster or humanitarian missions,” on top of managing power or other pieces of infrastructure.
Brandon Bean, GDIT’s vice president for artificial intelligence and machine learning, said, “This is a proxy for contested logistics… The Baja Desert provides us with adverse terrain topography and weather; it also provides us a dynamic [operational tempo] so we can’t pre-predict or plan anything.” GDIT did not say which team would be using its tools.
It shows an evolution of GDIT’s Defense Operations Grid-Mesh Accelerator, or DOGMA, a tool that fuses sensor data and sends it back to an operator under difficult conditions, such as enemy jamming, broken communications links, etc.
Since introducing DOGMA last August in the Pentagon’s T-REX drone warfare experiment, the company has developed three versions of it: one for fusing data, one for running autonomy, and one called WorldView, which Bean described as a “cognitive layer that provides a common operating picture.”
The race team will use electrically powered bikes, similar to the ones that special operations forces use in some missions. They’re quieter than motorbikes and their large batteries can also power sensors and communications gear.
“All the telemetry that’s coming from the rider and from the motorcycle” will go to AWS servers, Bean said. “Then we’re going to provide predictive analytics on when and where the rider needs to pit and where we need to replace the batteries.”
Other telemetry tools may eventually be added, like a rider-health tool designed for no-communication environments where standard fitness trackers don’t work. The company unveiled it along with DOGMA WorldView at the recent SOF Week event in Tampa, Florida.
“What we did was we built a round-loop workflow where we collected all this telemetry data off of these devices, [and] we’re able to work and pull this data into our DOGMA WorldView and be able to do pattern of life on these individuals,” Bean said. “So we could tell, based on the telemetry data on the phone, whether they’ve [encountered] elevated terrain or whether they stopped for periods of time. The next step of that is to actually tap into the microphone and the camera on the phone, so that we can identify if there’s hostile control [of the] device.”
