AI Space Trajectory Optimizer

The rapid growth of satellites and debris objects in Low Earth Orbit (LEO) has made safe and responsive trajectory planning a critical requirement for modern space missions. Past collision events, such as the 2009 Iridium–Cosmos incident, have demonstrated how undetected trajectory conflicts can generate long-lasting debris and increase future mission risks. This project presents the AI Space Trajectory Optimizer, a hybrid trajectory planning system that combines a physics-based orbital mechanics engine with a lightweight machine learning warm-start predictor to generate collision-aware and fuel-conscious orbital transfer and insertion paths. The system accepts mission parameters and orbital elements as inputs and evaluates trajectory feasibility using numerical propagation, analytical collision checks, and delta-V estimation based on Tsiolkovsky’s rocket equation. A trained AI model proposes an initial maneuver estimate, which is then refined and verified using the physics module to ensure constraint satisfaction and safety margins. Simulation tests with varied mission inputs and synthetic debris conditions demonstrated that the hybrid pipeline consistently produces valid maneuver plans while maintaining the transparency and auditability of calculations. Unlike purely iterative optimization approaches, this hybrid AI–physics method is designed to improve the planning responsiveness while preserving deterministic verification. The implementation includes a REST API backend, persistent mission logging, and a Three.js 3D visualization interface for the trajectory and debris inspection. The current limitations include dependence on simulated debris fields and simplified propulsion assumptions; however, the framework is extensible to real catalog data and higher-fidelity force models. This project demonstrates a practical, demonstrator-grade approach toward faster and safer autonomous trajectory planning for future space operations.