This is like an automatic drone pilot for spacecraft that can fly around another spacecraft to inspect it, while using as little fuel as possible. It combines a rule-based "if this then that" pilot (fuzzy control) with an evolutionary optimizer (genetic algorithm) that keeps tweaking those rules until the flight path is both safe and very fuel‑efficient.
Autonomously inspecting spacecraft or satellites in orbit is hard because it must be safe (no collisions), precise, and extremely fuel‑efficient (delta‑v is precious). This approach optimizes inspection trajectories and control policies so that an autonomous inspector can maneuver around a target spacecraft with minimal fuel use and without continuous ground control intervention.
Specialized control algorithm design (genetic fuzzy control) for minimal‑delta‑v proximity operations, tuned to orbital dynamics; potential defensibility via domain expertise, simulation environments, and flight‑validated datasets for on‑orbit inspection scenarios.
Classical-ML (Scikit/XGBoost)
Unknown
High (Custom Models/Infra)
On‑board compute and real‑time reliability for running genetic optimization and fuzzy control under strict spacecraft hardware and safety constraints.
Early Adopters
Focus on minimizing delta‑v for autonomous inspection in close proximity using a hybrid of genetic algorithms and fuzzy control, rather than more conventional fixed‑rule or purely optimal control methods; tailored to spacecraft inspection rather than generic guidance, navigation, and control.