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The MAchine learning ESTimations for uRban Operations (MAESTRO) system is a novel approach that couples commodity sensors with advanced algorithms to provide real-time onboard local wind and kinematics estimations to a vehicle's guidance and navigation system. Sensors and computations are integrated in a novel way to predict local winds and promote safe operations in dynamic urban regions where Global Positioning System/Global Navigation Satellite System (GPS/GNSS) and other network communications may be unavailable or are difficult to obtain when surrounded by tall buildings due to multi-path reflections and signal diffusion. The system can be implemented onboard an Unmanned Aerial Systems (UAS) and once airborne, the system does not require communication with an external data source or the GPS/GNSS. Estimations of the local winds (speed and direction) are created using inputs from onboard sensors that scan the local building environment. This information can then be used by the onboard guidance and navigation system to determine safe and energy-efficient trajectories for operations in urban and suburban settings. The technology is robust to dynamic environments, input noise, missing data, and other uncertainties, and has been demonstrated successfully in lab experiments and computer simulations.

NASA's Extensible Traffic Management (xTM) system allows for distributed management of the airspace where disparate entities collaborate to maintain a safe and accessible environment. This digital ecosystem relies on a common data generation and transfer framework enabled by well-defined data collection requirements, algorithms, protocols, and Application Programming Interfaces (APIs). The key components in this new paradigm are: Data Standardization: Defines the list of data attributes/variables that are required to inform and safely perform the intended missions and operations. Automated Real Time And/or Post-Flight Data Verification Process: Verifies system criteria, specifications, and data quality requirements using predefined, rule-based, or human-in-the-loop verification. Autonomous Evolving Real Time And/or Post-Flight Data Validation Process: Validates data integrity, quantity, and quality for audit, oversight, and optimization. The verification and validation process determines whether an operation’s performance, conformance, and compliance are within known variation. The technology can verify thousands of flight operations in near-real time or post flight in the span of a few minutes, depending on networking and computing capacity. In contrast, manual processing would have required hours, if not days, for a team of 2-3 experts to review an individual flight.

The novel approach for optimizing airspeed for both actual and predicted wind conditions in electric Vertical Takeoff and Landing (eVTOL) aircraft with Distributed Electric Propulsion (DEP) systems includes the process of creating a lookup table for wind‐optimal airspeed as a function of wind magnitude, considering the direction of the wind relative to the cruise segment, considering the cruise altitude for an aircraft type, and incorporating the wind-optimal airspeed lookup table in the performance database for real‐time access by the Flight Management Systems (FMS) to predict wind-optimal airspeed at waypoints of the flight plan. The target wind‐optimal airspeed is updated in real-time throughout the cruise portion of a flight. In a test of the wind-optimal airspeed targeting technique using a multi-rotor aircraft model, results obtained show benefits of flying at the wind‐optimal cruise airspeed compared to the best‐range airspeed. In headwind conditions, energy consumption was reduced by up to 7.5%, and flight duration was reduced by up to 28%. Under uncertain wind magnitudes, flying at wind-optimal airspeed offered lower variability and higher predictability in energy consumption than flying at best‐range airspeed.