Mission Scenario Design
Build and modify scenarios quickly, from early concept studies to more structured mission campaigns.
Overview
SpaceAGORA is a Julia spacecraft simulation framework for mission scenarios that combine orbital propagation, atmospheric-flight modeling, spacecraft dynamics, environment models, and guidance and control in one environment.
In the lab, it serves as the digital proving ground where mission concepts, control logic, and autonomy are exercised against realistic dynamics before flight. It also makes experimentation easier: new scenarios, GNC logic, scheduling logic, and new forces or torques can be added without rebuilding the simulation stack from scratch.
Get Started
Documentation, example scenarios, and citation information are available through the project site and repository.
Why SpaceAGORA Exists
Adaptive missions are hard to evaluate when dynamics, control, autonomy, and uncertainty are spread across separate tools. SpaceAGORA exists so those pieces can be studied together, making it possible to compare mission behavior, test new ideas, and understand how assumptions affect performance before flight.
What It Enables
GNC and Autonomy Integration
Evaluate guidance, navigation, control, autonomy, and scheduling logic inside the same simulation loop.
Environment-Aware Studies
Study orbital, atmospheric, and perturbation-driven effects when environmental structure matters to mission behavior.
Single- and Multi-Spacecraft Analysis
Support individual spacecraft, rendezvous operations, and constellation-scale studies in one framework.
Fidelity and Validation
Flexible Fidelity
SpaceAGORA supports lightweight local runs for rapid development and richer environment models when mission studies require them.
That makes it possible to move from quick scenario design to more detailed analysis without changing the broader workflow.
Validation and Comparative Studies
Telemetry-informed verification and reproducible comparisons help the lab study how mission outcomes change across models, assumptions, and execution choices.
The framework also supports benchmark-style studies for runtime, solver, and execution tradeoffs.