A secure, edge-deployable, voice-enabled AI assistant for hands-free command and control of unmanned ground vehicles — natural-language interaction that keeps working in denied, degraded, intermittent, and limited (DDIL) conditions.
What the system does, why it matters, and where it fits.
A secure, edge-deployable, voice-enabled AI assistant for hands-free command and control (C2) of autonomous logistics systems — specifically unmanned ground vehicles (UGVs). Operators direct robotic assets through natural language instead of graphical interfaces and manual input devices, keeping hands and attention on the task during rapid cargo movement.
The system is built for Denied, Degraded, Intermittent, and Limited (DDIL) environments. Speech-to-text and intent recognition run entirely on the edge, with no dependence on cloud connectivity, and are tuned for the high-noise conditions found in expeditionary and spaceport operations. The command path is authenticated and low-latency, so voice stays usable inside critical decision loops, and it is designed to interoperate with existing autonomy middleware and UGV control architectures.
The demo above shows operator speech being transcribed and correctly interpreted under representative noise — including drone rotor wash and spaceport operations.
Operator speech is captured and processed locally — nothing leaves the device.
Speech-to-text tuned for high-noise expeditionary and spaceport audio turns voice into text.
An intent-recognition model maps natural language onto a concrete C2 action.
An authenticated command is issued to the UGV through existing middleware — fully offline.
The assistant is built to interpret operator intent in the acoustic environments where manual input breaks down.
Broadband rotor and propulsion noise from nearby unmanned systems.
Launch-pad and ground-support acoustic loads at austere spaceport sites.
Cab, engine, and convoy noise during active cargo movement.
General high-noise forward-deployed and contested environments.
We can walk through the on-device speech stack, the intent models, and the C2 integration under NDA. Send a note and we’ll share what we can.
