A computer-implemented method is provided for training a supervised machine learning engine able to predict characteristics of aircraft trajectories from parameters of an aircraft, and environment parameters of the aircraft trajectory. A system able to train the supervised machine learning engine, a system for using the engine, and a computer-implemented method for using the engine are provided. The methods and systems provided are particularly useful for air traffic flow management applications.

A multi-dimensional flight release efficiency evaluation method. The method comprises: obtaining air flow control production and operation data which mainly comprises airspace capacity information, flight scheduling basic information, flight four-dimensional trajectory information and the like through a business information comprehensive processing platform, identifying a flight object affected by flow control and a flight object restricted by flow control through processing the operation data, analyzing a flight release time-hopping degree, calculating flight release delay distribution, evaluating controlled flight release fairness, predicting a controlled flight release normal rate, comparing airspace flow capacity matching situations, establishing a multi-dimensional flight release efficiency evaluation index set, and visually displaying evaluation indexes in modes of list, histogram, line chart, radar chart and the like.

According to a technical aspect of the invention, there is provided a multiple unmanned aerial vehicles navigation optimization method is performed at a ground base station which operates in conjunction with unmanned aerial vehicles-base stations which are driven by a battery to move and cover a given trajectory point set, the multiple unmanned aerial vehicles navigation optimization method including: calculating an age-of-information metric by receiving an information update from the unmanned aerial vehicles-base stations through communication, when the ground base station is present within a transmission range of the unmanned aerial vehicles-base stations; setting conditions of a trajectory, energy efficiency, and age of information of each of the unmanned aerial vehicles-base stations; and executing Q-learning for finding a trajectory path policy of each of the unmanned aerial vehicles-base stations, so as to maximize total energy efficiency of an unmanned aerial vehicles-base station relay network to which the energy efficiency and the age of information are applied. According to the invention, the following effects are obtained. Age of information (AoI) that is a new matrix used to measure up-do-dateness of data is set, an edge computing environment for a remote cloud environment is provided by using the AoI, and a computing-oriented communications application can be executed by using the edge computing environment.

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 flight data aggregation system for a plurality of aircraft includes one or more portable electronic devices in electronic communication with one or more central computers. The one or more portable electronic devices each monitor flight data from a corresponding aircraft. The one or more portable electronic devices analyze the flight data in real-time to determine an insight event indicating an incident of significance is presently occurring upon the corresponding aircraft. Each central computer includes one or more processors and a memory coupled to the one or more processors. The central computers are caused to receive the flight data collected during the insight event from an individual portable electronic device. The central computers determine overall flight data patterns based on the flight data collected during the insight event received from the individual portable electronic device and historical data stored in the one or more databases.

Methods, systems, apparatuses, and computer program products for incentivizing UAV use are disclosed. In a particular embodiment, incentivizing UAV use includes UAV pilot recommendation by a computing system. In this embodiment, the computing system receives at least one parameter related to a prospective UAV mission and retrieves UAV flight records of a plurality of UAV pilots. According to this embodiment, the computing system recommends, based on the at least one parameter and the UAV flight records, at least one UAV pilot for the prospective UAV flight.

In various aspects, a first set of attributes associated with a target location are determined. The target location is a location that one or more items are to be delivered to by an unmanned aerial vehicle (UAV). The first set of attributes are compared with a second set of attributes. The second set of attributes indicate attributes of each UAV of a plurality of UAVs. Based on the comparing, a UAV, of the plurality of UAVs, is recommended to deliver the one or more items to the target location.

An Unmanned Aerial Vehicle (UAV) air traffic control method utilizing wireless networks includes communicating with a plurality of UAVs via a plurality of satellites associated with the wireless networks, wherein the plurality of UAVs each include hardware and antennas adapted to communicate to the plurality of satellites; maintaining data associated with flight of each of the plurality of UAVs based on the communicating; and processing the maintained data to perform a plurality of function associated with air traffic control of the plurality of UAVs.

A system includes a memory system configured to store a plurality of instructions to provide aircraft communication visualization and a processing system. The processing system configured to execute the instructions to result in retrieving a plurality of status data associated with aircraft communication at a target location from a database, determining a plurality of geographic location information for a plurality of aircraft parking locations at the target location, accessing a plurality of imagery data associated with a view at the target location, and generating an aircraft communication visualization map for the target location. The aircraft communication visualization map includes aircraft communication information overlaid upon an image based on the imagery data and the aircraft parking locations at the target location.