Fleet and Flight Operations Integration and Optimization in a Mixed-Advanced Air Mobility Environment

We propose an innovation for a data-driven Artificial Intelligence/Machine Learning (AI/ML) model for uncrewed aviation fleet and flight operations optimization (UA-FFOpt) that will be trained with real-world data from an active flight program in the State of Michigan and at a partner cattle ranch in Florida. UA-FFOpt will leverage our existing Intellectual Property (IP), AVIATE, and NCR. AVIATErsquo;s capabilities for optimized mission planning and NCRrsquo;s access to real-time weather and airspace constraints accelerate UA-FFOptrsquo;s path to operation. Our model will incorporate aircraft capabilities across the fleet, airspace operational lsquo;rules of the skyrsquo; for integration into airspace classes, and dynamic re-planning based on real-time events such as traffic encounters, varying weather conditions, and community and geographic constraints. UA-FFOpt will be developed to be transferrable to other fleet operations applications for other use cases, including larger uncrewed aircraft, destined for the AAM ecosystem. By its nature as an AI/ML-based approach, it will be extensible to incorporate additional ecosystem (e.g., Class B, C, and D airspace) and operational variables beyond our primary use case with additional data and training.nbsp;