AI Workflow for In-Situ Resource Utilization (ISRU) Planning

An autonomous ISRU workflow directly converts prospecting data into actionable resource plans, eliminating the multi-hour latency of ground-in-the-loop analysis. The operational imperative is economic: reducing the mass and cost of Earth-supplied water, oxygen, and fuel by orders of magnitude for lunar or Martian outposts. This requires a custom orchestration layer that ingests spectral and geometric sensor data, runs site-suitability models, and outputs validated excavation schedules to robotic systems, all within local power and compute constraints.

Implementation integrates perception agents (for spectral analysis), planning agents (for geometric site layout), and a LangGraph-based supervisor that manages the workflow state. Critical controls include confidence scoring for autonomous validation, with low-confidence plans routed to a human review dashboard built on platforms like ServiceNow or a custom mission control UI. The architecture must be deployed on radiation-hardened edge compute, with data flows back to a central mission data lake (e.g., on an orbiting relay) for performance auditing and model retraining.

An ISRU workflow automates the end-to-end production chain from regolith analysis to plant scheduling. It eliminates the multi-hour latency of ground-based planning, turning a sequential, Earth-dependent process into a continuous, onsite manufacturing operation. The business case is direct: reducing launched mass of consumables by tens of tons per mission, which translates into hundreds of millions in saved launch costs or enables longer-duration surface presence with the same budget. The architecture must integrate spectral sensors, geometric planners, and processing unit controllers into a resilient, goal-directed system.

Implementation integrates LangGraph for agent orchestration, with dedicated nodes for prospecting, excavation planning, and plant scheduling. The Prospecting Agent fuses LiDAR and spectrometer data, building a resource map. The Excavation Agent, using a geometric solver like Gurobi or OR-Tools, plans optimal dig sequences. The Scheduler Agent interfaces with the processing plant's SCADA-like controls. A human review gate validates major plans pre-execution, while the system operates autonomously within approved parameters. Observability is provided through a unified telemetry stream linking material yield to original prospecting predictions, closing the loop for continuous optimization.

An ISRU planning workflow automates the prospecting-to-production pipeline, eliminating the multi-hour latency of ground-in-the-loop analysis for each sensor reading. The operational upside is measured in mission extension: by autonomously identifying high-value excavation sites and scheduling processing plant operations, a robotic asset can produce critical consumables like water or oxygen faster, reducing the required landed mass of supplies from Earth. This directly translates to lower launch costs and enables longer-duration surface missions for sustained human presence.

Implementation follows a phased, risk-aware delivery. Phase 1 deploys the Regolith Analysis Agent to process hyperspectral data onboard, flagging anomalies for ground review. Phase 2 integrates the Geometric Planning Agent with a digital twin for simulated excavation planning. The final phase connects the scheduler to the Spacecraft Command and Data Handling system, enabling closed-loop command of rovers and processing plants. Governance is enforced via confidence scoring, mandatory human review for low-confidence plans, and comprehensive audit logs of all autonomous decisions for mission safety.

Operationalizing ISRU automation demands a control architecture that enforces mission rules while enabling autonomous adaptation. The workflow ingests spectral data from prospecting rovers, executes geometric planning for excavation, and schedules processing plant operations to produce water or oxygen. Savings come from drastically reducing the planning latency and ground control burden inherent in long-delay communications, directly increasing daily resource yield. Implementation integrates agents built on frameworks like LangGraph for orchestration, connecting to onboard navigation systems (e.g., ROS), spectral analyzers, and digital twin simulations for validation before physical execution.

Rollout is sequenced from digital twin simulation to limited physical pilot, governed by approval gates at each phase. Controls include confidence scoring on mineral identification, exception routing for ambiguous sensor readings, and mandatory human review for high-risk excavation plans. Observability is provided through integrated dashboards tracking resource yield, system health, and plan-versus-actual execution, feeding continuous improvement loops. This structured approach mitigates the risk of autonomous systems committing irreversible errors with scarce extraterrestrial assets, ensuring the economic case for reducing Earth-supplied consumables is realized safely.

An ISRU planning workflow automates the end-to-end prospecting, site selection, and processing schedule to produce mission-critical consumables like water and oxygen from local materials. It eliminates the latency and labor of ground-based geological analysis and manual excavation planning. The operational upside is direct: it reduces the mass, cost, and risk of Earth-supplied logistics, enabling longer-duration surface missions. The architecture integrates onboard spectral/LIBS analyzers, terrain mapping systems, and a central orchestrator that models resource yield against power, time, and rover mobility constraints.

Implementation requires deploying the orchestrator as a containerized agent suite on the rover's flight computer, integrating via ROS 2 or a similar middleware with sensor drivers and the navigation stack. Critical controls include a human review gate for final excavation approval and continuous validation of processing plant output against predicted yields. The workflow must be fault-tolerant, capable of re-planning if an excavation site is unreachable or if plant telemetry indicates a blockage. Observability is provided through detailed logs of all model inferences, optimization decisions, and resource state changes for ground audit.