A method for landing an aircraft, which takes off and lands vertically, at a predetermined landing site defined by a circular marking that can be captured optically and has a circular outer contour utilizes a landing system. A camera arranged on the aircraft and directed to the landing site is used to capture images electronically, each of which represents a reproduction of the marking detected at least in sections. Each camera image is evaluated in a control device. The control device is used to fit a geometric object with at least one straight line, which has a predetermined line slope in relation to the camera image, into the reproduction in such a way that the line constitutes a tangent through a contact point to the marking detected at least in sections. The control device steers the aircraft in the direction of the contact point determined in this way.

There is provided a method performed by a configuration unit, the configuration unit being communicatively coupled to a communication network and an Uncrewed Aerial System, UAS, the UAS including an Uncrewed Aerial Vehicle, UAV, and a UAV controller, UAV-C, the method comprising: receiving, for each of a plurality of UAV service suppliers, USS, information specifying a service area of that USS; obtaining UAS specific information, wherein the UAS specific information comprises a UAV registration area, a USS service requirement, or a combination thereof; and determining a mapping of the UAS specific information to one or more of the plurality of USSs based on the received USS information and the obtained UAS specific information.

A system for urban air mobility monitors flight separation for compliance with a safe separation distance. A reference formation airspace is established for a reference air taxi based on minimum longitudinal, lateral and vertical parameters. When penetration of the reference formation airspace is detected, a penetration airspace is established. A centroid of the penetration airspace is determined and a target separation to the centroid is supplied to the air taxi to reestablish safe separation. The extent of separation is also safely contained by the presence of virtual air taxis whose positions on the periphery of the penetrated airspace serve to limit potential penetration of surrounding air taxi air spaces.

A method and system for optimizing drone delivery efficiency. The method includes determining an optimal intermediate location for a UAV based on historical payload delivery data related to a payload carried by the UAV, wherein the distance between the optimal intermediate location and each of a group of potential recipient devices is less than a predetermined threshold; causing the UAV to navigate to the optimal intermediate location; sending, to each potential recipient device having a probability of requesting the payload carried by the UAV which exceeds a predetermined threshold, a notification indicating the payload carried by the unmanned aerial vehicle; receiving, from a first potential recipient device of the potential recipient devices, a request to deliver the payload; and causing the UAV to navigate from the optimal intermediate location to a location of the first potential recipient device when the request to deliver the payload is received.

An apparatus and a method of a wireless communication system, and a computer-readable storage medium are disclosed. The apparatus comprises a processing circuit. The processing circuit is configured to configure, directly or indirectly on the basis of one or more height thresholds for user equipment and a current height of the user equipment, operation of the user equipment. According to at least one aspect of the embodiments of the disclosure, configuring, on the basis of a height threshold, operation of a user equipment optimizes communication performance in an unmanned aerial vehicle communication scenario.

Devices and computer-implemented methods for analyzing aircraft trajectories, the method includes the steps of receiving data associated with a plurality of aircraft trajectories; breaking the trajectories down into a plurality of vectors, a vector comprising one or more sequences of enumerators; aligning multiple vectorized trajectories by shifting sequences of enumerators by one or more positions; and detecting one or more anomalies in one or more trajectories by unsupervised classification (e.g. DBSCAN). Developments describe the supervised determination of trajectory anomaly detection models, the use of density-based algorithms, the use of one or more neural networks and/or decision trees, one or more display steps, notably displaying root causes (explainable or understandable artificial intelligence), the processing of avionics data flows, etc. System (e.g. computing) and software aspects are described.

This application discloses a beacon for guiding landing of an unmanned aerial vehicle. The beacon includes at least three levels of patterns: one first-level pattern and at least one second-level pattern, where the at least one second-level pattern is superposed above the first-level pattern, and an area of the second-level pattern is less than that of the first-level pattern.

A system pairs a 5G-connected drone to a 5G-enabled user-controlled device. The system sets a predicted route for the 5G-connected drone to navigate. A portion of this route is indoors and matches an actual route of the user-controlled device. The system then causes the 5G-connected drone to commence navigating the predicted route. The system receives environmental data from the area surrounding the 5G-connected drone on the predicted route measured by a set of onboard sensors of the 5G-connected drone. Using the received environmental data and a deviation of the predicted route from the actual route traversed by the user-controlled device, the system determines that an alternate route for the 5G-connected drone exists and sets the alternate route. The system generates a notification based on the environmental data indicating hazards on the alternate route and transmits the notification over a 5G network to the user-controlled device.

Systems and methods provide a services architecture. An air mobility platform locally stores a native application at. The air mobility platform receives a first request for a first service and data identifying the first user as a priority user type or a non-priority user type. A second request for a second service and data related to the second user identifies the user type is received. The air mobility platform calculates a priority for the first request. If the priority for the first request exceeds the priority for the second request, a third party application is accessed for the first request. The air mobility platform submits the data related to the first request from the third party application to at least one of the first unmanned aerial vehicle or the first user.

The present disclosure provides a control system for controlling unmanned autonomous systems (UAS). The control system comprises of an application user system 102 to operate the UAS, an operating system 103, a virtual road system (VRS) 109 and a virtual packet 501. The virtual packet 501 created as a boundary around the UAS defined by application user system 102 or VRS 109. The operating system 103 includes a machine learning processing unit (MLPU) 104 configured for positioning the UAS, detecting collision within path of the virtual packet 901. The VRS 109 configured to generate a virtual roadway 902 using architecture for routing the UAS. The routing and controlling of UAS by the VRS 109 is based on request received from the MLPU 104, application zone packet parameters and actual position co-ordinates received from the MLPU 104.