End-of-Life Deorbit Window Identification and Planning Automation

For satellite operators, manually planning a compliant end-of-life deorbit is a high-stakes, one-off task prone to oversight and inefficiency. This custom workflow automates the entire process, from ingesting orbital state vectors and regulatory rules to simulating fuel-optimal maneuvers and reserving ground support. The business value is clear: it eliminates manual analysis cycles, guarantees adherence to the 25-year rule or other mandates, and maximizes the residual value of remaining propellant, directly protecting against regulatory fines and preserving asset utility.

Implementation requires integrating this orchestration layer—built with frameworks like LangGraph—with flight dynamics software (e.g., AGI's STK), propulsion models, and ground station APIs (e.g., AWS Ground Station). Critical controls include human approval gates before command release, simulation validation against a digital twin, and continuous monitoring of the deorbit timeline. This turns a complex, reactive planning burden into a scheduled, managed process that reduces operational risk and overhead as constellations scale.

This workflow automates a critical regulatory and operational bottleneck: ensuring satellites deorbit within the 25-year rule while preserving maximum asset value. It eliminates the manual, error-prone analysis of orbital mechanics, fuel budgets, and ground station calendars that currently consumes engineering teams for weeks. The operational upside comes from guaranteed regulatory adherence, optimized use of remaining propellant to extend revenue-generating life, and the conversion of a one-off crisis into a managed, auditable process integrated with flight dynamics and mission control systems.

Implementation requires integrating a planning agent with systems like AGI's STK or custom propagators for high-fidelity simulation, and ground station APIs like AWS Ground Station for scheduling. The architecture must include robust validation against operational constraints, approval gates for the final maneuver sequence, and continuous monitoring of the satellite's state relative to the plan. This ensures the deorbit process is not only automated but also defensible to regulators and stakeholders, with full audit trails of all decisions and simulations.

Phase 1 establishes the foundational data pipeline, ingesting orbital state vectors, fuel telemetry, and regulatory rules (e.g., the 25-year rule) into a central orchestrator like LangGraph. This initial layer automates the continuous identification of compliant deorbit windows years in advance, generating preliminary maneuver simulations. The focus is on creating a reliable, auditable forecast without triggering any physical actions, allowing operators to validate models and build trust in the automated logic before integration with command systems.

Phase 2 integrates the planning logic with ground station networks (e.g., KSAT, AWS Ground Station) and flight dynamics systems to reserve support and generate fuel-optimal maneuver sequences. This phase introduces human-in-the-loop approval gates and exception routing for non-nominal scenarios. The final phase focuses on execution, adding secure command validation against a digital twin and orchestrating the full deorbit campaign. This staged approach mitigates risk by proving each subsystem's reliability before allowing it to influence critical operations.

Automating deorbit window identification and maneuver planning directly addresses the operational bottleneck of manually modeling orbital decay, fuel reserves, and regulatory deadlines for dozens of satellites. The business value is twofold: it eliminates costly, repetitive astrodynamics analysis, and it systematically prevents regulatory violations (like the 25-year rule) that can incur fines and licensing jeopardy. Implementation requires integrating with flight dynamics systems (e.g., AGI STK, FreeFlyer), fuel telemetry streams, and space traffic management APIs to create a continuous assessment loop, turning a complex, one-off task into a scheduled, managed process.

Governance is enforced through automated validation against a digital twin and hard constraint library before any plan reaches the flight director for final approval. A phased rollout starts with a shadow mode, where the system generates plans in parallel with manual processes for validation, followed by a pilot on a single end-of-life asset. Full fleet deployment requires robust monitoring of plan accuracy, fuel forecast deviations, and integration health with the mission planning system (e.g., COSMOS) and ground station booking APIs. This staged approach de-risks the automation while building operator trust in its recommendations.