Project Overview
A unified AI platform fusing predictive analytics, twin simulation, and onboard autonomy to allow satellites to self-diagnose and heal without ground intervention.
The Problem
During deep space missions or comms blackouts, satellites are highly vulnerable. If a critical failure occurs while out of contact with ground stations, the satellite cannot be saved. Current systems rely entirely on human-in-the-loop decision making.
Our Solution
We deployed an onboard autonomous agent powered by an embedded Large Language Model (LLM) and expert rule-based systems. It continuously analyzes system health, cross-references with local digital twin simulations, and executes emergency protocols instantly without waiting for ground approval.
System Architecture
A highly compressed, quantized AI model running on Rad-Hard (Radiation-Hardened) edge processors. The system interfaces directly with the satellite's core flight software using a robust, fail-safe C API.
Impact & Results
Successfully demonstrated in low-earth orbit, the autonomous system detected a power-surge anomaly during a planned comms blackout and executed an emergency safe-mode shunt in 0.4 seconds, preventing permanent battery damage.