Madan Thambisetty

Evidential Uncertainty-Aware Fault Diagnosis for Autonomous Spacecraft Health Management

Autonomous spacecraft must continue to make health-management decisions when communication with ground operators is delayed or temporarily unavailable. This creates a particular problem for fault diagnosis: a system must not only identify a known failure, but also recognize when...

Evidential Uncertainty-Aware Fault Diagnosis for Autonomous Spacecraft Health Management

Autonomous spacecraft must continue to make health-management decisions when communication with ground operators is delayed or temporarily unavailable. This creates a particular problem for fault diagnosis: a system must not only identify a known failure, but also recognize when...

AstraHeal: Robust Multi-Cycle Autonomous Fault Recovery Under Perturbed Spacecraft Conditions

Autonomous spacecraft operating in Low Earth Orbit (LEO) and deep space must execute closed-loop fault recovery without ground intervention during communication blackouts. While recent aerospace AI architectures demonstrate promising isolated planning and evidential diagnosis, pr...

AstraHeal: Robust Multi-Cycle Autonomous Fault Recovery Under Perturbed Spacecraft Conditions

Autonomous spacecraft operating in Low Earth Orbit (LEO) and deep space must execute closed-loop fault recovery without ground intervention during communication blackouts. While recent aerospace AI architectures demonstrate promising isolated planning and evidential diagnosis, pr...

AstraHeal: Deterministic Safety Gating for Uncertainty-Aware Autonomous Spacecraft Fault Recovery

Autonomous spacecraft operating in Low Earth Orbit (LEO) and deep-space regimes must resolve critical electrical and thermal subsystem anomalies during prolonged communication blackouts. While machine-learning planners and evidential diagnostic engines can generate high-utility r...

AstraHeal: Deterministic Safety Gating for Uncertainty-Aware Autonomous Spacecraft Fault Recovery

Autonomous spacecraft operating in Low Earth Orbit (LEO) and deep-space regimes must resolve critical electrical and thermal subsystem anomalies during prolonged communication blackouts. While machine-learning planners and evidential diagnostic engines can generate high-utility r...