Fleet-Wide Debris Risk Management for CubeSat Operators

Automating the daily ingestion of TLE/CDM data, false-positive filtering, and initial alert triage eliminates the manual analyst screening that consumes 1-2 FTE hours per satellite per day. This directly converts fixed labor cost into variable compute cost, freeing limited engineering staff for higher-value mission tasks.

A custom workflow integrates propellant budgeting with risk scoring, recommending maneuvers that balance collision probability against fuel consumption and mission impact. For CubeSats with finite, non-replenishable propellant, this AI-driven trade-off analysis can prevent premature fuel exhaustion, directly protecting ROI and extending revenue-generating operations.

Automated compliance monitoring against the 25-year disposal rule and pre-launch analysis requirements prevents six-figure regulatory fines and license delays. More critically, by systematically lowering the probability of a loss-of-mission collision, the workflow directly protects the capital investment in each satellite and reduces potential liability exposure.

Manual debris management does not scale. A custom agentic architecture—where specialized agents handle data fusion, scoring, and coordination—allows a single small team to manage risk for a growing fleet of 10, 50, or 100+ CubeSats. This turns debris management from a scaling bottleneck into a non-linear operational advantage.

The data and models from an automated risk workflow provide empirical evidence for realistic fuel budgeting and insurance costing on future satellite designs. This moves planning from conservative guesswork to data-driven forecasting, allowing more accurate cost models and freeing capital for additional capability or margin.

Automated logging of every data point, probability score, and decision rationale generates an immutable audit trail. This is critical for demonstrating due diligence to investors, insurers, and regulators (e.g., FCC, NOAA), transforming risk management from an opaque operational task into a transparent, accountable business process.

This agent ingests raw Conjunction Data Messages (CDMs) from commercial SSA APIs like LeoLabs or Space-Track, applies domain-specific filters (e.g., minimum miss distance, probability thresholds, covariance quality), and ranks alerts by credible risk. It eliminates 70-90% of false positives that would otherwise consume analyst time, presenting only actionable events for review.

A rules-based optimizer calculates delta-V options for evasive maneuvers, constrained by the CubeSat's limited propellant budget (often cold gas or electric propulsion), power availability, and mission timeline. It outputs a ranked list of maneuvers with trade-offs between collision probability reduction and propellant cost, enabling fast, economically sound decisions.

When a conjunction involves another operator's asset, this agent automates the initial, structured communication. It drafts templated emails via the Space Data Association's SOCRATES API or direct channels, parses responses for maneuver intent, and escalates complex negotiations. This compresses a traditionally multi-day, manual process into hours, reducing collision risk from coordination delays.

This component automates the generation of audit-ready reports for regulators (e.g., FCC, NOAA). It pulls technical conjunction data, applies the correct regulatory template using a RAG system, and uses an LLM agent to draft the narrative. The output routes through a human-in-the-loop approval gate before submission, slashing administrative overhead and ensuring compliance.

A centralized dashboard agent ingests telemetry and health status from all CubeSats in the fleet. It correlates this with active conjunction alerts to prioritize risks—e.g., flagging conjunctions for satellites with low battery or degraded thrusters. Alerts are routed via Slack, email, or SMS based on severity, creating a single operational picture for a lean team.

After an evasive maneuver, this workflow automatically validates success by comparing predicted vs. actual telemetry. It then triggers a mission re-planning agent to reschedule lost payload operations (e.g., imaging targets, communication windows) and update ground station contacts. This recovers mission value and restores normal operations without manual intervention.

Owns the final go/no-go decision for any evasive maneuver. The workflow must deliver a prioritized, summarized risk dashboard with clear trade-off analysis (fuel cost vs. mission impact vs. collision probability) to support rapid, informed decisions. Integrates with mission planning tools to reschedule payload operations post-maneuver.

Responsible for integrating the workflow's output (maneuver delta-V, burn time) with the satellite's command stack and ground station scheduler. The architecture must provide validated command sequences and automate the rescheduling of communication passes to avoid conflicts with high-risk periods or post-maneuver stabilization.

Primary integration point for conjunction data. The workflow architecture must include robust, fault-tolerant connectors to commercial feeds (e.g., LeoLabs, COMSPOC) and public sources (CelesTrak), with logic to normalize TLE/OD formats, validate data quality, and handle API outages to maintain a continuous risk assessment pipeline.

Critical for fuel-aware and health-aware decision making. The workflow must continuously ingest real-time telemetry (propellant levels, power state, ACS health) to constrain maneuver options. A feedback loop should correlate debris proximity events with subsequent subsystem anomalies to diagnose potential micro-impact damage.

Requires an automated audit trail and reporting. The workflow must generate immutable logs for every automated assessment and recommendation, and assemble regulatory-compliant conjunction reports for bodies like the FCC or NOAA using RAG for template retrieval and LLM agents for narrative drafting, all behind a human approval gate.

For conjunctions involving other assets, the workflow needs a secure, automated communication channel. This involves agentic drafting of templated coordination messages, encrypted sending via services like SOCRATES, parsing of responses, and escalation of complex 'right-of-way' negotiations to the operations lead.