Physics-Informed
Perception Stacks.
We replace brute-force digital computation with analog elegance. By utilizing Compressed Sensing at the hardware layer, we reduce data throughput by 90% while increasing signal fidelity in noise.
Analog Front-End
The Physical Layer
Standard autonomous systems digitize noise, then try to filter it out using heavy compute. We do the opposite.
Our custom RF-SoC (Radio Frequency System on Chip) performs analog pre-processing before the signal ever hits the ADC (Analog-to-Digital Converter). By implementing Compressive Sensing strategies at the antenna level, we discard 90% of the spectrum noise physically, never wasting a single clock cycle processing interference.
- Nyquist-Rate Bypass
- Wideband Staring
VLA Inference
The Compute Layer
We don't separate "Vision" and "Action." Our Vision-Language-Action (VLA) models are end-to-end.
Instead of generating a text description of the scene ("I see a wall"), our model generates a direct motor torque command ("Turn rotors 15 degrees"). This collapses the latency stack from ~200ms (standard cloud AI) to <15ms (Edge Reflex), essential for navigating unstable, GPS-denied environments.
> CONSTRAINT: Maxwell_Eq_Boundary
> OUTPUT: [Motor_Vector_X, Motor_Vector_Y]
Kinetic Control
The Actuation Layer
Physics is the ultimate validator. Our control loops are Physics-Informed (PINNs).
Unlike standard Reinforcement Learning which "hallucinates" physics, our agents are hard-coded with rigid-body dynamics constraints. An agent will never attempt a maneuver that violates the laws of momentum or gravity, ensuring mechanical longevity and mission safety even in failures.
Access the Technical Briefing
A comprehensive overview of our core architecture, validation data, and development roadmap is available for investors, strategic partners, and national innovation agencies.