Mission Re-Planning Automation After an Unplanned Evasive Maneuver

An unplanned maneuver disrupts the carefully scheduled mission timeline, idling high-value payloads (e.g., Earth imaging sensors, communication transponders). Manual replanning takes days, leaving revenue-generating assets inactive. A custom automation workflow uses an AI agent to ingest the new orbital state and immediately re-optimize the schedule, reassigning imaging targets, communication slots, and calibration activities to maximize data collection within the new orbital constraints. This directly recovers lost mission utility, protecting quarterly imaging quotas or service-level agreements.

Post-maneuver, mission planners, payload operators, and ground station engineers scramble to rebuild schedules across disconnected tools (e.g., STK, custom planners, Excel). This creates version conflicts and communication overhead. The automated workflow acts as a central orchestration layer, integrating with mission planning systems and using constraint-satisfaction algorithms to generate a conflict-free, optimized schedule. It automatically updates all downstream systems and notifies stakeholders, turning a multi-team, multi-day coordination effort into a validated plan delivered in under an hour.

A new orbit changes the visibility windows for ground stations, potentially stranding collected data on-board. Manual re-booking of antenna time is slow and may miss optimal windows. The automation workflow includes an agent dedicated to ground segment optimization. It queries station availability (via APIs like NORAD's SGP4 or commercial networks), books contacts based on data volume and priority, and sequences downlinks to clear buffer space, ensuring no collected data is lost due to missed opportunities. This maximizes the return on every orbital pass.

Inefficient manual replanning can lead to suboptimal operations that require additional, fuel-costly attitude adjustments or opportunistic maneuvers later. The AI-driven replanning workflow embeds fuel-awareness as a primary constraint. It models the delta-V costs of proposed payload slews and communication antenna pointing, selecting the sequence that minimizes propellant consumption while meeting mission objectives. This proactive conservation directly extends satellite operational life, protecting a multi-million dollar asset.

For constellation operators, one satellite's unplanned maneuver can disrupt coordinated coverage patterns or inter-satellite links. Manual recovery risks creating gaps or conflicts with sibling assets. The automated workflow scales to a fleet-aware context. The re-planning agent considers the relative phasing and coverage requirements of the entire constellation, adjusting not just the affected satellite's schedule but also proposing minor tweaks to neighbors to maintain global service levels. This preserves the collective value of the constellation infrastructure.

Unplanned maneuvers and their operational impacts must be documented for internal review, regulator reporting, and insurance claims. Manual logging is error-prone. The custom workflow automatically generates a comprehensive audit trail. It captures the trigger (conjunction alert data), the executed maneuver details, the pre- and post-maneuver schedules, the AI replanning rationale, and the final recovery plan. This immutable log provides defensible evidence for operational integrity, streamlining compliance and potentially supporting favorable insurance terms.

An AI agent ingests the latest telemetry and precise orbit determination (OD) data to validate the new orbital state against the predicted maneuver. It flags any execution anomalies (e.g., under-performance, attitude errors) and updates the satellite's digital twin. This automated health and position check replaces manual analysis, ensuring the re-planning engine operates on a verified, accurate baseline, which is critical for downstream scheduling accuracy.

A constraint-based optimization agent takes the new orbit and remaining mission objectives (e.g., imaging targets, communication slots, calibration tasks) to generate a revised, conflict-free schedule. It models visibility windows, solar illumination, onboard resource limits (power, data storage), and priority rules. This component directly recovers revenue-generating operations by re-booking high-value targets into newly available slots, often restoring 70-90% of lost mission time within one orbital period.

This agent recalculates ground station visibility passes based on the new orbit and coordinates with network operators (e.g., KSAT, AWS) to secure required contacts. It prioritizes contacts for urgent telemetry downlink and the upload of the new mission schedule. The workflow integrates via APIs with systems like Satellite Toolkit (STK) and ground segment management software to automate booking and conflict resolution, preventing communication blackouts that delay return to normal operations.

A supervisory agent enforces guardrails throughout the re-planning process. It continuously evaluates the revised mission plan against remaining propellant budgets, power margins, and thermal limits. If the proposed schedule risks depleting resources needed for future station-keeping or collision avoidance, it triggers a re-optimization or escalates to a human planner. This layer is essential for protecting the satellite's long-term operational life and ensuring AI-driven decisions align with business-level asset management goals.

The final component translates the approved mission schedule and ground station plan into a validated, time-tagged command sequence for upload. It integrates with flight dynamics systems and uses a LangGraph-style workflow to check for command conflicts, proper timing, and compliance with satellite command database rules. This automated generation and validation eliminate a major manual, error-prone step, compressing the timeline from plan to executable commands on the satellite.

The entire workflow is surfaced in an operator dashboard built on a real-time data stream. It presents the assessed state, the proposed mission and ground station schedules, resource projections, and the command sequence for review. Human operators have configurable approval gates (e.g., any schedule change >5% fuel impact) with clear accept/reject/modify controls. This interface provides the necessary oversight and audit trail, turning a reactive, chaotic process into a managed, efficient operational procedure.