ISRU.AI orchestrates autonomous robotics, intelligent planning, and off-world logistics to power a sustainable space economy. From lunar ice to asteroid volatiles, it transforms raw terrain into mission-critical supply chains.
ISRU.AI maps subsurface water, oxygen-rich minerals, and volatiles using Graph Convolutional Networks (GCNs) and multispectral imaging. These models learn terrain structure and material composition for efficient targeting.
Distributed reinforcement learning coordinates multi-robot teams for excavation, hauling, and construction—adaptable to regolith variability and terrain occlusion in Permanently Shadowed Regions (PSRs).
Neural networks dynamically optimize regolith reduction, water electrolysis, and Sabatier fuel production to maximize oxygen and methane yields—crucial for life support and propulsion.
Recurrent models like Long Short-Term Memory (LSTM) and attention networks manage rover fleets and resource flows—ensuring just-in-time delivery to hoppers, habitats, and orbiting depots.
Variational Autoencoders (VAEs) and Temporal Convolutional Networks (TCNs) detect system anomalies, predict failures, and trigger real-time behavior reconfiguration—ensuring mission continuity under extreme conditions.
Built on ROS 2 and edge-deployed on Jetson and Xeon hardware, ISRU.AI scales from autonomous rovers to asteroid capture missions. Mesh networks and cloud retraining make it adaptable across cislunar, Martian, and microgravity environments.
ISRU.AI is not just a toolkit—it’s the neural operating system of off-world industry. Built on edge computing and ROS 2, it unifies terrain intelligence, robotic autonomy, and interplanetary logistics into a self-improving ecosystem for space resource utilization.