A system and method for optimizing mission fulfillment via unmanned aircraft systems (UAS) within a mission space generates or receives atmospheric models forecasting weather and wind through the mission space, the atmospheric models having an uncertainty factor. Until the projected flight time, the controller may iterate through one or more simulations of a projected flight plan through the mission space, determining the probability of successful fulfillment of mission objectives based on the most current atmospheric models (including the ability of the UAS to navigate the flight plan within authorized airspace constraints). Based on conditions and behaviors observed during a simulated flight plan, the controller may revise flight plans, flight times, or atmospheric models for subsequent simulations. Based on multiple probabilities of fulfillment across multiple simulations, the controller selects an optimal flight plan and/or flight time for fulfillment of the assigned set of mission objectives.

A method for determining a trajectory of an aircraft intended to fly over a field of operation with a view to performing an action on a target at a given time is provided. The method comprises a step of computing a set of sections between a starting point, intermediate points and the target. A first type of section has a rectilinear overall shape so as to limit the time spent by the aircraft in non-secure areas. A second type of section has a sinusoidal shape so as to allow a time reserve to adjust a position of the aircraft over the target at said given time with a view to performing the action.

A system for autonomous flight collision avoidance in ana electric aircraft, where the system includes an electric aircraft. The electric aircraft includes a at least a sensor coupled to the electric aircraft, where the at least a sensor coupled to the aircraft is configured to detect an obstacle in the electric aircraft's flight path and transmit the obstacle to a flight controller. The electric aircraft also includes a flight controller where the flight controller is configured to receive the obstacle from the at least a sensor coupled to the electric aircraft, determine an adjusted flight path as a function of the obstacle, and transmit the adjusted flight path to a pilot display. The system further includes a pilot display, where the pilot display is configured to receive the adjusted flight path form the flight controller and display the adjusted flight path to a user.

A method for determining a flight path of an unmanned aerial vehicle (UAV) includes: acquiring an initial flight path configured by a management platform; determining, based on the initial flight path, a first group of accessible base stations of the UAV on the initial flight path capable to be accessed when the UAV flies based on the initial flight path; if the first group cannot provide continuous cellular network services for the UAV, acquiring a second group of accessible base stations capable of providing continuous cellular network services for the UAV; and determining the flight path corresponding to the second group as a target flight path. As such, the initial flight path of the UAV can be reasonably adjusted upon that the core network device cannot provide satisfactory network services for the UAV flying according to the initial flight path, to enable the cellular network to provide satisfactory network services for the UAV.

Various embodiments of a system and method for a quantitative approach and departure risk assessment are described. In one example, the system includes program instructions executable in the computing device that, when executed by the computing device, cause the computing device to: obtain a nominal flight path of an aircraft, calculate a potential crash area for a section of the nominal flight path based on a failure mode, calculate risk values based on a population data of a geographical area traveled corresponding to the nominal flight path, and display the calculated risk values plotted on a map of at least a section of the geographical area traveled corresponding to the nominal flight path. Other examples include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

A weather depiction system for an aircraft is disclosed. A radar is configured to scan a surrounding environment of the aircraft and provide weather data. An aircraft computing device is configured to: detect weather patterns using the weather data, receive a flight trajectory of the aircraft from a flight management system (FMS), compare the flight trajectory to an altitude of each of the weather patterns, identify the weather pattern as relevant or non-relevant based on the comparison, and present symbols corresponding to the relevant weather patterns on the weather display and exclude symbols corresponding to the non-relevant weather patterns on the weather display.

Described are systems and methods that utilize nodes distributed at different geographic locations to detect and track the approximate position, trajectory, and/or predicted path of aerial vehicles operating below a defined altitude (e.g., 500 feet). As nodes detect an aerial vehicle, the node determines a bearing toward the aerial vehicle and provides the bearing to an air-traffic system. The air-traffic system processes bearings received from each node and determines one or more of an approximate position, trajectory, and/or predicted path of the detected aerial vehicle. The approximate position, trajectory, and/or predicted path may be provided to one or more subscribing clients and/or used to alter paths of one or more aerial vehicles.

A method is provided for predicting a reroute for a planned flight of an aircraft. The method includes building a machine learning model to predict a reroute on a future date of a planned flight of an aircraft. The machine learning model is built in a batch process that includes accessing reroute data and weather data, and performing a data wrangling of the reroute data and the weather data to produce a collection of data keyed by date. Candidate machine learning models are built using a training set produced from the collection of data. The candidate machine learning models are evaluated, and the machine learning model is selected from the candidate machine learning models based on the evaluation. And the machine learning model is output for deployment to classify the future date as having a reroute advisory issued, and predict a reroute on the future date.

A system and apparatus for assisting in determining the best course of action at any point inflight for an emergency. The system may monitor a plurality of parameters including atmospheric conditions along the flight path, ground conditions and terrain, conditions aboard the aircraft, and pilot/crew data. Based on these parameters, the system may provide continually updated information about the best available landing sites or recommend solutions to aircraft configuration errors. In case of emergency, the system may provide a pilot with a procedure for execution for landing the aircraft.

Vertical take-off and landing (VTOL) aircraft can provide opportunities to incorporate aerial transportation into transportation networks for cities and metropolitan areas. However, VTOL aircraft may be noisy. To accommodate this, the aircraft may utilize onboard sensors, offboard sensing, network, and predictive temporal data for noise signature mitigation. By building a composite understanding of real data offboard the aircraft, the aircraft can make adjustments to the way it is flying and verify this against a predicted noise signature (via computational methods) to reduce environmental impact. This might be realized via a change in translative speed, propeller speed, or choices in propulsor usage (e.g., a quiet propulsor vs. a high thrust, noisier propulsor). These noise mitigation actions may also be decided at the network level rather than the vehicle level to balance concerns across a city and relieve computing constraints on the aircraft.