GNC SYSTEM ARCHITECTURE FOR PARAFOIL FLIGHT WIND KNOWLEDGE FUSION EXPRO+

Objective: To develop a robust, adaptive guidance framework for parafoil-based recovery systems based on the fusion of wind data from both ground-based measurements and onboard sensors.Description: Parafoil-based recovery systems offer a lightweight, low-cost, and reusable solution for space re-entry and payload delivery. They require advanced GNC strategies to achieve precise and reliable landings in highly dynamic atmospheric conditions. Their aerodynamic controllability and passive deployment make them suitable for various missions. However, achieving precise landings in real-world conditions remains challenging due to atmospheric variability, especially wind, which introduces uncertainty in trajectory prediction and landing dispersion. Accurate and timely wind knowledge is therefore not just a helpful input, but a critical enabler of parafoil performance and reliability. Current GNC systems often treat wind as a disturbance, relying on conservative margins or reactive filtering. This limits system performance, increases landing footprints, and misses opportunities for optimization. Crucially, there is a gap in the systematic use of wind dataforecasted, sensed, or inferredin guidance and navigation design. This activity addresses that gap with a GNC architecture that treats wind knowledge asa core element, integrated by design into both preflight planning and onboard adaptation. By continuously adjusting to atmospheric variations, this approach aims to enhance landing accuracy and improve system robustness to weather conditions. This activity explores wind-aware GNC through two synergistic layers: (1) ground-based wind profiling and forecasting for mission planning, and (2) onboard sensing and virtual estimation to refine wind knowledge in-flight. By fusing this data into navigation filters and adaptive guidance laws, the system dynamically adjusts trajectories, reduces dispersion, and exploits beneficial wind structures. This leads to more accurate and resilient landings. The output will be a modular GNC software prototype, validated in Monte Carlo simulations, and designed for future TRL scaling. This activity encompasses the following tasks: - Define GNC architecture, wind data levels, simulation environment, and test interfaces. - Develop wind estimation (onboard sensors, virtual sensing), integrate with navigation filters. - Design adaptive guidance using wind input, implement real-time trajectory updates. - Integrate full GNC stack, simulate full missions, and evaluate robustness to wind variability in a representative simulation environment.