Machine Learning & Neural Networks
Route planning and traffic management for autonomous aircraft are very complex problems that are most effectively solved by splitting into smaller sub-problems. Our approach uses a blend of ensemble learning and modular neural networks.
Ensemble Learning (EL)
We use a technique that combines the output from multiple models to improve the overall performance, robustness, and accuracy of aircraft trajectory and other predictions. This approach leverages the strengths of ensemble learning to achieve better predictive performance than could be obtained from any single model alone.
Modular Neural Networks
MNNs are at the core of optimizing Metal Raptor's flight guidance for autonomous aircraft, focusing on several critical aspects: trajectory prediction, traffic management, routing, and adaptive responses to weather or emergencies. Each module is specialized, ensuring a comprehensive approach to flight guidance. The trajectory prediction module analyzes historical and real-time flight data to forecast future positions accurately. Our traffic management module monitors current airspace conditions to prevent collisions and optimize flight paths. By integrating these and other modules, the Modular Neural Network provides a holistic and adaptable system for guiding autonomous aircraft through complex and ever-changing airspace environments.
Connecting EL to the MNNs
By leveraging ensemble learning's ability to improve prediction accuracy through the aggregation of multiple models - we enhance the predictive performance of our modular neural networks which are structured to handle complex, subdivided tasks. This approach capitalizes on the modular architecture's ability to tackle specific sub-problems and harnesses the ensemble methods' power to significantly reduce prediction error, leading to safer and more efficient autonomous aircraft operations.
AI & System Safety
To insure our machine learning methods optimize safety and never have control of the system, flight data is captured and analyzed by AI in a separate environment from flight operations. Recommendations for adjustments are run through multiple simulations to insure safety prior to implementation in the live system. We control the code, not the other way around.
