Corporate Partners
This project trains a machine learning model to generate real-time, safe obstacle-avoidance flight routes for unmanned aircraft, replacing a legacy routing system too slow for dynamic airspace conditions. The model is optimized for safety, fuel efficiency, and waypoint compliance, with a potential extension to coordinated multi-aircraft formation flight and fleet navigation integrity.
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To improve prediction accuracy for 13 aftermarket product categories offered during vehicle transactions by incorporating additional behavioral and situational factors into the existing model. Dealer feedback will also be used to identify decision patterns for better recommendation accuracy, dealer performance, and customer experience.
Building on terrain-aware RF propagation models, this project optimizes placement of ground-based sensors and jammers against enemy threat networks by evaluating large geographic areas to maximize signal advantage while meeting siting, coverage, and deployment requirements. It then extends coverage via aircraft flight routes that balance threat avoidance with jamming effectiveness under flight dynamics and airspace constraints.
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