Systems and methods for generating a volatility index for drone activity are provided. For example, a method includes determining volatility data for drone activity in a plurality of areas. The method further includes generating a plurality of volatility indices to represent the volatility data. The method further includes determining a priority for each area of the plurality of areas based on the plurality of volatility indices. The method further includes updating map data based on the determined priority.

A system for electric aircraft navigation includes a sensor configured to detect a navigation signal, a flight controller, wherein the flight controller is configured to receive the navigation signal, identify a navigation status as a function of the navigation signal, and determine an aircraft adjustment as a function of the navigation status, and a pilot display, wherein the pilot display is configured to display the aircraft adjustment to a user, and present an autonomous function configured to enact the aircraft adjustment automatically.

Methods, systems, and devices for automatically customizing operation of a robotic vehicle are described. The method may include identifying an operator, retrieving an operator profile and associated metadata for the operator from a database, where the metadata includes operator habit information, and configuring the robotic vehicle based on existing preference-based and performance-based settings, where the existing preference-based and performance-based settings are based on the metadata. The methods may include identifying operator habit information during operation of the robotic vehicle, deriving updated preference-based and performance-based settings for the operator based on the identified operator habit information, and providing, to the database, modifications to the metadata associated with the operator profile of the operator.

A system and method are disclosed for design of a suite of multispectral (MS) sensors and processing of enhanced data streams produced by the sensors for autonomous aircraft flight. The onboard suite of MS sensors is specifically configured to sense and use a MS variety of sensor-tuned objects, either strategically placed objects and/or surveyed and sensor significant existing objects to determine a position and verify position accuracy. The received MS sensor data enables an autonomous aircraft object identification and positioning system to correlate MS sensor data output with a-priori information stored onboard to determine and verify position and trajectory of the autonomous aircraft. Once position and trajectory are known, the object identification and positioning system commands the autonomous aircraft flight management system and autopilot control of the autonomous aircraft.

An approach is provided for calculating a payload survivability estimate and generating aerial routes based on the payload survivability estimate. The approach, for example, involves processing data, such as map data representing the geographic area to identify at least one map feature, at least one material corresponding with the at least one map feature, or a combination thereof. The payload survivability estimate can be based on real-time data, historical data, or a combination thereof. The approach also involves generating a map data layer of a geographic database based on the payload survivability estimate. The approach further involves providing the map data layer as an output.

A method for controlling movement of a drone is disclosed. A spatial vector between a flight-capable drone and a reference object is computed. The spatial vector defines a direction and a distance by which the drone is spaced from the reference object. Flightpath attributes based on the computed vector are determined. The flightpath attributes include one or more of a flight direction, a flight distance, and a flight speed. The flight direction is variable as a function of the direction of the spatial vector. The flight distance is variable as a function of the distance of the spatial vector. The flight speed is variable as a function of the distance of the spatial vector. In an automated operation, movement of the drone is controlled according to the determined flightpath attributes.

A vehicle control function, UCF, and a method for the UCF that receives from a traffic management function, USS/UTM, a first positioning request message comprising a vehicle system, UAV, identity and coordinates of interest, determines with the USS/UTM a positioning configuration for the coordinates of interest, sends to a Location Server a second positioning request message comprising the UAV identity, UAV positioning configuration, receives positioning information for the UAV from the Location Server, and sends to the USS/UTM a positioning report message comprising the UAV identity and the positioning information.

An unmanned aerial vehicle airport, an unmanned aerial vehicle system, a tour inspection system and an unmanned aerial vehicle cruise system. The unmanned aerial vehicle airport comprises a support base, a parking apron, a protective cover and a protective cover opening and closing driving device. The parking apron is installed on the top of the support base; the protective cover covers the top of the apron; the protective cover opening and closing driving device is installed between the support base and the protective cover, and the protective cover opening and closing driving device is configured to cause a bar linkage mechanism to drive the protective cover to switch between an open position and a closed position. The unmanned aerial vehicle airport is provided with the protective cover for the parking apron. If the protective cover is open, the unmanned aerial vehicle is parked on the parking apron, and takes off from the parking apron.

Problems to be Solved

To provide an unmanned aerial vehicle capable of achieving both of equipping a pruning structure capable of pruning trees and enhancing safety.

Solution

An unmanned aerial vehicle 2 according to the present invention includes a pruning structure 23 capable of pruning a tree; a housing structure 24 capable of housing the pruning structure 23 inside; and a state control section 212 capable of controlling the state of the pruning structure 23 between a housed state in which the pruning structure 23 is housed inside the housing structure 24 and an exposed state in which the pruning structure 23 is exposed to the outside of the housing structure 24, wherein the state control section 212 can control the state of the pruning structure 23 to the housed state on landing. The state control section 212 preferable to control the state of the pruning structure 23 to the exposed state when a distance from the unmanned aerial vehicle 2 to a target tree to be pruned equal to or shorter than the predetermined distance, and to control the state of the pruning structure 23 to the housed state when a distance from the unmanned aerial vehicle 2 to the target tree longer than the predetermined distance.

Systems and methods for service drone landing zone operations are disclosed herein. An example method includes determining location-specific information for a location, the location-specific information including at least images of the location from at least one of a vehicle or a mobile device at the location, determining a landing area for a drone at the location using the location-specific information. receiving localizing signals from at least one of the vehicle or the mobile device as the drone approaches the location, and causing the drone to land in the landing area using the localizing signals.