Autonomous Trajectory Optimization

This application area focuses on automatically designing and executing optimal spacecraft trajectories and maneuvers—across single vehicles and swarms—under tight constraints on fuel, safety, and computation. It covers tasks like multi-phase interplanetary transfers, low‑Earth orbit transfers, constellation deployment, formation flying, collision avoidance, and close‑proximity operations such as inspection. Instead of relying on manual, expert‑driven analysis and slow numerical solvers, trajectory and control solutions are generated or refined automatically, often in (near) real time and at large operational scales. AI and advanced optimization are used to approximate complex dynamics, search huge maneuver spaces, and coordinate multiple spacecraft under uncertainty and communication limits. Techniques such as reinforcement learning, neural surrogates, and distributed model predictive control drastically cut computation time while maintaining or improving fuel efficiency and safety. This enables more agile mission design, real‑time onboard decision‑making, and economically viable operation of large satellite constellations and inspection vehicles.