Inspiration
Space is no longer the vast, empty frontier it once seemed to be. Today, thousands of operational satellites, aging spacecraft, debris fragments, and frequent launches are all sharing the same orbital corridors around Earth. What used to be open space is quickly becoming a crowded and dynamic environment. We kept asking ourselves why there was no simple way to see everything together in one place and understand what might go wrong before it actually does. We imagined a system that feels like a mission control dashboard but speaks in plain language. A platform where someone could ask practical questions such as what happens if a launch is delayed and receive a clear answer without needing to be a rocket scientist. That idea became the starting point for A³O.
What it does
A³O is a browser based mission control and planning application designed to bring clarity to orbital operations. It allows users to see satellites, space stations, and launchers in a single unified dashboard. You can search and track well known entities such as the International Space Station, the Sentinel-1A, or the Starlink constellation and understand how they fit into the broader orbital picture. The system generates structured mission plans that include schedules and clearly identified conflicts. These conflicts might involve overlapping launches from the same site or satellites operating within unsafe altitude bands. Each issue is explained in simple language so users understand not just that there is a problem, but why it matters. The platform also supports instant what if simulations. Users can test scenarios such as launch delays or satellite failures and immediately receive an updated plan along with a readable explanation. In addition, a 3D visualization mode displays Earth, the Moon, Mars, and orbital paths so users can visually explore the system and see conflicts highlighted directly in space. In short, A³O helps users plan, detect risks, test alternatives, and understand orbital activity in both analytical and visual formats.
How we built it
The backend of A³O is built using FastAPI in Python, exposing a clean and structured REST API that handles entity retrieval, plan generation, and scenario simulation. At its core is a lightweight constraint based planning engine that reads structured entity data from JSON files and evaluates operational rules such as launch overlaps and risky altitude zones. The system constructs schedules while simultaneously detecting conflicts, ensuring that planning and validation happen together. Pydantic is used to maintain data consistency and validation throughout the application. For explanation generation, the system can optionally use Ollama to translate technical outputs into natural language summaries. When that is not available, a built in fallback mechanism produces concise and understandable explanations so users never face raw system data without context. The frontend is a single page application developed using plain HTML, CSS, and JavaScript. The dashboard and analytics components are powered by Chart.js, while the 3D environment is rendered using Three.js. Importantly, the orbital visuals are calculated using the same altitude and inclination data used in the planning logic, ensuring consistency between what users see and what the system computes.
Challenges we ran into
One of the primary challenges was defining what truly qualifies as a conflict. Some cases, like two launches scheduled at the same site simultaneously, are straightforward. Others, such as satellites operating in high risk altitude bands or interacting with debris zones, required careful rule design to balance realism with computational efficiency. Another significant challenge was maintaining synchronization between the backend planner and the 3D visualization. We avoided duplicating logic in the frontend by ensuring that all planning decisions originate from a single source of truth in the API. Designing explanations that remain clear even without AI support also required thoughtful structure. We wanted the system to remain interpretable regardless of whether an AI model was active. Finally, presenting scheduling tools, analytics, explanations, and 3D visualization in one application without overwhelming users demanded careful user experience decisions, leading to the separation of Information and 3D modes.
Accomplishments that we're proud of
We are proud that A³O unifies planning, visualization, and analysis within a single environment. Users do not need to switch between separate systems to understand their missions. What if simulations feel immediate and responsive, enabling quick experimentation with alternative scenarios. The 3D visualization is not decorative but technically grounded, using the same orbital parameters that drive planning logic. Every generated plan is accompanied by a clear explanation, ensuring that the system remains transparent and accessible rather than opaque and overly technical.
What we learned
Throughout development, we learned how to design a compact constraint based planning engine that handles operational rules without becoming unnecessarily complex. We also learned that maintaining a single source of truth for data is critical when integrating backend logic with interactive visualization. Most importantly, we discovered that even highly technical orbital coordination systems can be made understandable when clarity and explanation are treated as core design principles rather than optional features.
What's next for Autonomous Orbital Operations Orchestrator (A³O )
ooking ahead, we plan to expand A³O with predictive path detection capabilities. The goal is to integrate machine learning models capable of forecasting the trajectories of satellites, debris, and near Earth objects. By continuously updating orbital parameters such as velocity and position, the system will simulate future scenarios with greater realism. This will allow operators not only to detect existing conflicts but to anticipate potential collisions and hazardous intersections before they occur. The long term vision is to evolve A³O from a planning and conflict detection platform into a predictive orbital safety and coordination system that mirrors real world dynamics as closely as possible.
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