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.

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 air vehicle control system (1) and method for operation of one or more air vehicles, AVs, (2) flying along flight routes (FR) assigned to the air vehicles, AVs, (2) by said air control system (1) according to a calculated flight route plan, FRP, within a predefined airspace, wherein an air flight guarding control unit (3) integrated in the air vehicle, AV, (2) is adapted to intervene automatically with flight controls of the air vehicle, AV, (2) on the basis of a monitored flight status of the air vehicle, AV, (2) such that the air vehicle, AV, (2) is kept during a flight movement within three—dimensional confines or boundaries of the assigned flight route (FR) and collisions with other air vehicles, AVs, (2) or with other obstacles are avoided.

A vehicle control system includes at least one imaging device attached to a vehicle and that captures multiple images, and a control circuit that generates a composite image from the multiple images and displays the composite image on a display unit. The vehicle is operated according to a user operation on a portion of the display unit on which the composite image is being displayed.

A simulation testing architecture can be applied to an aircraft monitoring system for an aircraft that includes complex algorithms (such as machine learning algorithms) for sensing objects around the aircraft and controlling the aircraft to avoid such objects. A reference scenario is selected from a plurality of stored scenarios based on a desired set of aircraft safety standards. A stochastic process is applied to generate a large number of conditional variations within a simulated environment, varying weather, objects in the airspace, points of failure, and the like to provide a representative sample of possible aircraft missions and encounters within the selected reference scenario. Synthetic environmental inputs are fed into the aircraft monitoring system software, and the resultant actions of the software are logged. These logs can be used to generate metrics on an encounter-level basis, a scenario-level basis, or across a population of scenarios.

A monitoring system for an aircraft uses sensors configured to sense objects around the aircraft to generate a recommendation that is ultimately used to determine a possible route that the aircraft can follow to avoid colliding with a sensed object. A first algorithm generates guidance to avoid encounters with sensed airborne aircrafts. A second algorithm generates guidance to avoid encounters with sensed non-aircraft airborne obstacles and ground obstacles. The second algorithm sends inhibiting information to the first algorithm in a feedback loop based on the position of sensed non-aircraft objects. The first algorithm considers this inhibiting information when generating avoidance guidance regarding airborne aircrafts.

A method for validating a terrain database includes a step of simulating flights based on trajectory data for an aircraft, the flight simulations being speeded up, a step of determining terrain collision risks by means of a system for signalling terrain collision risks based on the speeded-up flight simulations, a step of determining the origins of terrain collision risks with a view to validating or not validating the terrain database.

An example autonomous or remotely piloted aircraft includes a virtual aperture radar system including a plurality of antennas relationally positioned on one or more surfaces of the aircraft such that individual beams from each of the plurality of antennas scan respective volumes around the aircraft and the respective volumes together substantially form an ellipsoidal field of regard around the aircraft, and a computing device having one or more processors configured to execute instructions stored in memory for performing functions of: combining the respective volumes together to form an image representative of the ellipsoidal field of regard around the aircraft, and identifying one or more objects within the image.

A method for automatically proposing an air conflict resolution, the method includes a step of receiving air conflict and air situation data at a time when an air conflict is detected, the method comprising the steps of: determining a conflict category associated with the detected air conflict based on the conflict data; determining a degree of relevance in proposing a resolution to the air conflict according to the category and the air situation; determining one or more types of resolution to be applied to resolve the detected air conflict according to the degree of relevance; determining set of alternative trajectories corresponding to the one or more types of resolution; determining a set of candidate trajectories from among the set of alternative trajectories, a candidate trajectory being an alternative trajectory which does not generate an air conflict; selecting, from among the candidate trajectories, a candidate trajectory which fulfills a selection criterion relating to the operational relevance of the candidate trajectories and to the acceptability of the candidate trajectories by the flight plan management system; returning the candidate trajectory, the candidate trajectory being saved and re-evaluated as long as it does not generate an air conflict or has not been accepted by an air traffic controller.

A crane system, a crane, and a mobile unit capable of easily coping with a case where electrical equipment such as an obstacle light fails are to be provided. A crane system includes a crane body and a mobile unit movable around the crane body. The mobile unit includes an obstacle light and functions as an obstacle light for the crane body.