NeuroNav uses a combination of neuromorphic computing and energy-efficient machine learning algorithms designed to operate autonomously in resource-constrained environments.

These systems mimic the human brain's efficiency, allowing real-time decision-making without needing heavy computational power.

The AI is trained to process data from various onboard sensors, executing complex navigational tasks and adapting to new environments using deep learning models optimized for low-power consumption.

This setup drastically reduces the need for constant communication with Earth, giving spacecraft the autonomy to adapt to changing conditions, anomalies, or discoveries during missions.

NeuroNav reduces mission risks by providing autonomous operational capabilities to spacecraft, thus minimizing reliance on delayed Earth-based instructions.

It offers an energy-efficient system that mimics neurophysiological processing to maintain high-level navigational computation without consuming excessive power or processing capacity.

Developing NeuroNav is technically feasible but requires significant investment in neuromorphic chip technology and AI algorithm development.

Competing with established space navigation solutions would be challenging, but regulatory approval is manageable with government partnerships.

The initial cost barriers are high but justified by the potential to extend mission capabilities and reduce operational risks.