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.

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.

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.

An apparatus for generating an electric aircraft flight plan, where the apparatus includes a sensor and controller. The electric aircraft includes a sensor that is configured to detect a position of an electric aircraft, generate a position datum, and transmit the position to a flight controller. The electric aircraft also includes a database of recommended flights. The recommended flight plan is displayed on a display in the electric 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.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for an unmanned aerial system inspection system. One of the methods is performed by a UAV and includes obtaining, from a user device, flight operation information describing an inspection of a vertical structure to be performed, the flight operation information including locations of one or more safe locations for vertical inspection. A location of the UAV is determined to correspond to a first safe location for vertical inspection. A first inspection of the structure is performed is performed at the first safe location, the first inspection including activating cameras. A second safe location is traveled to, and a second inspection of the structure is performed. Information associated with the inspection is provided to the user device.

A position estimation method for a tracking target is implemented in an unmanned aerial vehicle. The position estimation method include: estimating a target position of the tracking target at the next time according to an initial position of the tracking target at the current moment; determining an estimated width and an estimated height of the tracking target in an image captured by a pan-tilt-zoom camera of the unmanned aerial vehicle according to the estimated target position; obtaining an actual width and an actual height of the tracking target in the image; determining a height difference between the estimated width and the estimated height and a width difference between the actual height and the actual width; and updating the target position of the tracking target at the next time according to the height difference and the width difference.