A device for assisting the piloting of a rotorcraft in order to pilot a rotorcraft during an approach stage preceding a stage of landing on a rotorcraft landing area. Such a device includes in particular a camera for taking a plurality of images of the environment of the rotorcraft along a line of sight, looking at least along a forward direction Dx of the rotorcraft, and processor means for identifying in at least one image from among said plurality of images at least one looked-for landing area.

A method of calculation, by a flight management system termed FMS, of a trajectory flown by an aircraft comprises the steps, calculated by the FMS, of: for at least one transition of the trajectory arising from the flight plan: 1) determining an initial transition comprising at least one arc exhibiting a single initial turning radius, 2) determining an initial trajectory incorporating the initial transition, 3) determining for each parameter a plurality of predicted values of the parameter in the course of the initial transition, 4) determining a plurality of ordered subdivisions of the arc of the initial transition according to a predetermined criterion, 5) determining, for each subdivision, an associated turning radius, 6) determining an improved transition on the basis of the ordered subdivisions and of the successive associated turning radii, 7) determining an improved trajectory incorporating the improved transition, 8) displaying the improved trajectory to a pilot of the aircraft.

Embodiments include devices and methods for navigating an unmanned autonomous vehicle (UAV) based on a measured magnetic field vector and strength of a magnetic field emanated from a charging station. A processor of the UAV may navigate to the charging station using the magnetic field vector and strength. The processor may determine whether the UAV is substantially aligned with the charging station, and the processor may maneuver the UAV to approach the charging station using the magnetic field vector and strength in response to determining that the UAV is substantially aligned with the charging station. Maneuvering the UAV to approach the charging station using the magnetic field vector and strength may involve descending to a center of the charging station. The UAV may follow a specified route to and/or away from the charging station using the magnetic field vector and strength.

Autoland systems and processes for landing an aircraft without pilot intervention are described. In implementations, the autoland system includes a memory operable to store one or more modules and at least one processor coupled to the memory. The processor is operable to execute the one or more modules to identify a plurality of potential destinations for an aircraft. The processor can also calculate a merit for each potential destination identified, select a destination based upon the merit; receive terrain data and/or obstacle data, the including terrain characteristic(s) and/or obstacle characteristic(s); and create a route from a current position of the aircraft to an approach fix associated with the destination, the route accounting for the terrain characteristic(s) and/or obstacle characteristic(s). The processor can also cause the aircraft to traverse the route, and cause the aircraft to land at the destination without requiring pilot intervention.

Methods, systems, and devices for wireless communications are described. A wireless device may receive a broadcast remote identification (BRID) message from a unmanned aerial vehicle (UAV), where the BRID message may include an identity of the UAV. The wireless device may identify UAV information associated with the UAV based on the UAV ID. In some cases, the wireless device may be configured with information that enables the identification of the UAV information. In other cases, the wireless device may request the UAV information from a network entity, such as a UAV flight management system (UFMS), which provides the requested UAV information. In some examples, the UFMS may request the UAV information from an unmanned aerial system (UAS) service supplier (USS) based on the BRID information. Upon identifying the UAV information, the wireless device may broadcast the UAV information to manned aerial vehicles, thereby indicating a presence of the UAV.

Using power line rights of way for UAV routing provides a direct, uninterrupted, aerially clear path to the vast majority of lots and buildings from nearby substations and generating stations. Segmenting or separating the UAV traffic by airframe glide ratio improves safety for people on the ground and utilization of the limited airspace. Further segmenting UAV traffic by airframe speed and size allows greater traffic throughput.

According to the present invention there is provided a drone (1) comprising one or more propellers (2) and one or more actuators (3) for actuating said one or more propellers (2) to generating a thrust force which enables the drone (1) to fly; a controller (4) which is configured such that it can control the flight of the drone (1), wherein the controller (4) comprises a memory (6) having stored therein a plurality of predefined sets of positions which define a virtual rail which can be used to guide the flight of the drone (1) so that the drone can avoid collision with an subject; and wherein the controller further comprises a mathematical model (7) of the drone; wherein the controller (4) is configured to control the flight of the drone by performing at least the following steps, (a) approximating lag error based on the position of the drone (1) measured by a sensor (5) and the virtual rail, wherein the lag error is the distance between a point along the virtual rail which is closest to the drone (1) and an estimate of said point along the virtual rail which is closest to the drone (1); (b) approximating a contour error based on the position of the drone (1) as measured by a sensor (5) and the virtual rail, wherein the contour error is the distance between a point along the virtual rail which is closest to the drone (1) and the position of the drone (1); (c) defining a cost function which comprises at least said approximation of the lag error and said approximation of the contour error; (d) minimizing the defined cost function, while also respecting at least limitations of the drone which are defined in said mathematical model, to determine a plurality of control inputs over a predefined time period into the future, and (e) applying the first control input only to the one or more actuators (3). There is further provided a corresponding method for controlling the flight of a drone.

Aspects relate to a system for flight plan generation of an electric vertical takeoff and landing (eVTOL) aircraft. An exemplary system for flight plan generation includes a flight controller mounted on an eVTOL aircraft. The flight controller may be configured to receive a plurality of flight plan data and generate a flight plan for the aircraft as a function of the plurality of flight plan data.

Boundary information associated with a three-dimensional (3D) flying space is obtained, including a boundary of the 3D flying space. Location information associated with an aircraft is obtained, including a location of the aircraft. Information is presented based at least in part on the boundary information associated with the 3D flying space and the location information associated with the aircraft, including by presenting, in a display, the boundary of the 3D flying space and an avatar representing the aircraft at the location of the aircraft.

An aerial vehicle is provided. The aerial vehicle can include a plurality of sensors mounted thereon, an avionics system configured to operate at least a portion of the aerial vehicle, and a machine vision controller in operative communication with the avionics system and the plurality of sensors. The machine vision controller is configured to perform a method. The method includes obtaining sensor data from at least one sensor of the plurality of sensors, determining performance data from the avionic system or an additional sensor of the plurality of sensors, processing the sensor data based on the performance data to compensate for movement of the unmanned aerial vehicle, identifying at least one geographic indicator based on processing the sensor data, and determining a geographic location of the aerial vehicle based on the at least one geographic indicator.