Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning with Refueling and Adaptive Collision Avoidance

Researchers developed a reinforcement learning AI to plan missions for small satellites tasked with removing multiple pieces of space debris. The system dynamically optimizes flight paths, fuel use, and refueling stops while avoiding collisions in real-time. Tested with real debris data, it proved safer and more efficient than traditional methods. This provides a scalable solution for cleaning Earth's crowded orbits and planning other complex autonomous space missions.