Unmanned Aerial Vehicles also known as UAVs or Drones, either autonomous or remotely piloted, are classified as drones by the US Federal Aviation Administration (FAA) as weighing under 212 pounds. The system described herein details Autonomous Flight Vehicles (AFV) which weigh over 212 pounds but less than 1,320 pounds which may require either a new classification or a classification such as Sport Light Aircraft, but without the requirement of a pilot due to the safe autonomous flight system such as the Safe Temporal Vector Integration Engine or STeVIE. Safe Autonomous Light Aircraft (SALA) are useful as drone carriers, large scale air package or cargo transport, and even human transport depending on the total lift capability of the platform.

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.

The invention relates to an electronic device generating a flight plan for an aircraft, comprising: a display module configured for displaying two flight plans simultaneously and for each flight plan, for viewing, the common elements and distinct elements with regard to the other flight plan. an acquisition module configured for acquiring a copy command. a copying module configured for copying at least one of the distinct elements from one of the two flight plans to the other flight plan based on the copy command; and a processing module configured for generating a new flight plan based on the target flight plan and the distinct element or elements which were copied.

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 system and method for an ownship aircraft auto transition from an assigned spacing application to a visual separation application provides the ability to intuitively pre-configure for and execute an automatic transition from an assigned spacing traffic application managing an assigned interval spacing to a traffic application managing visual separation from an assigned target aircraft. This feature enables integration between separate traffic applications, creating new capabilities while reducing the workload on the pilot during a particularly busy phase of flight.

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.

Disclosed is route planning using a worst-case risk analysis and, if needed, a best-case risk analysis of GNSS coverage. The worst-case risk analysis identifies cuboids or 2d regions through which a vehicle can be routed with assurance that adequate GNSS coverage will be available regardless of the time of day that the vehicle travels. The best-case risk analysis identifies cuboids or 2d regions through which there is adequate coverage at some times during the day. In case path finding using the worst-case risk analysis fails, a best-case risk analysis can be requested and used to find alternate potential path(s). Time dependent forecast data that covers regions along the alternate potential path(s) can be requested and used to route vehicles, including autonomous drones, from starting points to destinations. This includes generation, distribution and use of risk analysis data, implemented as methods, systems and articles of manufacture.

The present invention is directed to methods and systems for requesting information from a mobile device with a fencing agent. The fencing agent determines a position with a DNS resolver, queries geofences with an IP address, receives an anchor point with an IP address from the DNS resolver. The device with the fencing agent is able to receive multiple anchor points within multiple geofences within an ROI and translate fence points into fence geometries. Geofence information is stored and registered in a database of geofences, and each geofence is associated with a plurality of geographic designators, wherein each of the plurality of geographic designators is associated with an IP address.

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.

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.