Astranoun: Intelligent Space Communication & Sustainability Initiative

Designing adaptive, AI-supported protocols for resilient, low-latency communication across planetary distances, including fault-tolerant relays and autonomous message routing. This includes deployment of a solar system-spanning mesh network, featuring strategically placed relay nodes connecting Earth, the Moon, Mars, and deep-space assets.

 Developing AI-enhanced tracking and classification systems for space debris, with predictive analytics to assess collision threats and counteract weaponization scenarios. These systems are designed to ensure mission safety and long-term orbital sustainability.
Astranoun’s approach integrates real-time data from ground-based telescopes, radar systems, and orbital sensors to continuously map debris trajectories and behavioral patterns. Machine learning models trained on historical and simulated data help predict potential high-risk intersections and cascading collision scenarios.

Advancing spacecraft platforms capable of anchoring to asteroids and executing gradual orbital redirection for planetary defense and sustainable resource extraction. This includes modular anchoring technologies, low-thrust propulsion systems, and AI-assisted orbital management for relocating asteroids into economically viable orbits.

Developing innovative thermodynamic energy generation platforms that exploit space’s extreme temperature gradients and helium gas trace elements. These systems aim to power orbital infrastructure and deep-space missions sustainably, minimizing reliance on traditional fuels. Concepts include Stirling-inspired gas-expansion cycles, radiative cooling techniques, and helium collection for long-duration missions.

Prototyping decentralized systems for authenticating, encrypting, and timestamping telemetry data, protecting against spoofing and unauthorized command access.

Building multi-modal AI platforms to monitor LEO, MEO, and GEO in real time, enhancing threat detection, object mapping, and environmental forecasting.