Satellite Constellation Management for Autonomous Maneuver Planning
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Solution Overview
Problem
Manual management of satellite constellations is labor-intensive, costly, and inefficient, especially as the number of satellites increases, leading to potential safety and efficiency issues due to the inability to scale effectively and respond promptly to dynamic events.
Innovation Solution
A constellation management system (CMS) that acquires and utilizes various data sources for situational awareness, including space situation awareness, space weather, and telemetry data, to determine proposed plans for satellite operations, automate decision-making, and manage resources such as battery and propellant usage, while incorporating machine learning for predictive capabilities and reducing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual management methods are used for satellite constellations, then operational control can be maintained, but labor intensity and costs increase significantly as the number of satellites scales up
Solution Approach 1:
The system enables satellites to autonomously perform operations by automatically generating plans for maneuvering, maintaining formations, and avoiding collisions without human intervention. The constellation management system uses machine learning models to predict satellite behavior and generate control commands, allowing the system to serve itself rather than requiring manual management of each satellite.
Solution Approach 2:
The management system is divided into modular components including individual satellite controllers, constellation-level coordination systems, and machine learning prediction modules. Each satellite operates with its own control logic while participating in the larger constellation management architecture, allowing the system to scale by adding individual units rather than redesigning the entire system.
2Productivity
If the number of satellites in the constellation increases, then service coverage and redundancy improve, but manual management becomes infeasible and response time to dynamic events deteriorates
Solution Approach 1:
The system continuously monitors satellite positions, velocities, and operational status, feeding this data back to the management system which automatically adjusts control commands in real-time. Machine learning models predict future satellite states and generate proactive control actions, enabling the system to respond to dynamic events such as collisions or formation disruptions without manual intervention.
Solution Approach 2:
The management system dynamically adjusts operational parameters such as orbital elements, formation geometry, and maneuver timing based on real-time constellation state and predicted events. By automatically optimizing these parameters, the system maintains safe and efficient operations across large constellations without requiring proportional increases in human management resources.
3Productivity
If automated management systems are implemented, then response time and efficiency improve, but system complexity and initial costs increase
Solution Approach 1:
The constellation management system performs multiple functions including orbit determination, formation maintenance, collision avoidance, and anomaly detection within a single integrated architecture. The machine learning models are trained to handle various operational scenarios and generate appropriate control commands for different satellite types and mission requirements, reducing the need for separate specialized systems.
Data Source
AI summary
A constellation of many satellites provide communication between devices such as user terminals (UTs) and ground stations that are connected to other networks, such as the Internet. A constellation management system (CMS) facilitates management and operation of the satellites in the constellation and facilitates information exchange with other authorized systems to provide for situationally aware operation. The CMS may ingest data such as satellite telemetry, space weather data, object ephemeris data about other orbital objects, and so forth. The CMS uses the ingested data to automatically operate satellites to perform routine activities such as station keeping maneuvers, maintenance activities, interference mitigation, and so forth. Confirmation from a human operator may be obtained before performing some activities. Activities may be planned and coordinated to minimize resource consumption for the individual satellite as well as the constellation. Output, such as ephemeris data, may be provided to other parties as well.