37results about "Aircraft ground control" patented technology

A system and a method include a user interface having a display. A control unit is configured to monitor a position of an aircraft in relation to one or more other structures at an airport, associate an aircraft model with the position of the aircraft, associate one or more structure models with the one or more other structures, and show the aircraft model and the one or more structure models on the display.

An aircraft-based strategic and tactical interactive guidance system for ground operations includes position sensors for determining position information of an aircraft engaged in taxi operations (e.g., gate to takeoff, touchdown to gate) at an airport. The guidance system stores airport database information about runways, taxiways, hazards, gates, and other airport features. The guidance system analyzes and parses verbal taxi instructions from the airport control facility, into a navigation path. The guidance system fuses the navigation path with the airport database information and the aircraft's position information to present, via an interactive display surface, a strategic display, e.g., providing an overhead view of the current aircraft position, its assigned gate or destination, and the navigation path incorporating the prescribed runways and / or taxiways via which the aircraft will reach said destination. The pilot may modify the displayed navigation path via the interactive display surface.

The present invention relates to a technology for predicting, on the basis of an artificial intelligence model, a flight path along which a flight vehicle such as a UAM will fly. According to an aspect of the present invention, a method performed by a path prediction model training device comprises the steps of: obtaining multiple pieces of training data including performance information and fuel information of at least one flight vehicle, and weather information and route deviation information based on the location of the at least one flight vehicle; preprocessing the multiple pieces of training data; and training a path prediction model to predict the path of the flight vehicle, on the basis of the multiple processed pieces of training data.

Tracking aircraft in and near a ramp area is described herein. One method includes receiving camera image data of an aircraft while the aircraft is approaching or in the ramp area, receiving LIDAR / Radar sensor data of an aircraft while the aircraft is approaching or in the ramp area, merging the camera image data and the LIDAR / Radar sensor data into a merged data set, and wherein the merged data set includes at least one of: data for determining the position and orientation of the aircraft relative to the position and orientation of the ramp area, data for determining speed of the aircraft, data for determining direction of the aircraft, data for determining proximity of the aircraft to a particular object within the ramp area, and data for forming a three dimensional virtual model of at least a portion of the aircraft from the merged data.

Systems, apparatuses, methods, and computer program products are provided herein. For example, a method is described herein includes identifying aviation operations program data representative of one or more aviation operations programs. In some embodiments, the method includes configuring the one or more aviation operations programs in accordance with at least one of a plurality of aviation implementation states. In some embodiments, the method includes providing aviation mission data to an external aviation mission device. In some embodiments, the method includes receiving, from the external aviation mission device, first program implementation data in response to an aviation mission activation event. In some embodiments, the method includes initiating performance of one or more aviation mission actions based on the first program implementation data.

A method of perceiving an airport scene is disclosed. The method comprises receiving an image of at least part of an airport and processing (304) said image using a first machine learning algorithm to identify at least one segment (402) in said image and determine an initial estimate of a category of airport feature present in said segment. The method also comprises determining (310) one or more real-world coordinates or dimensions associated with the segment, applying (312) one or more predetermined logical tests to the initial estimate of the category of airport feature present in the segment and the real-world coordinates or dimensions of the segment to determine a reviewed estimate of the category of airport feature present in the segment, and outputting (320) said reviewed estimate.

The objective of the present invention is to determine a preferred flight route for a multi-purpose flying object. A flight route determining system S comprising a battery-driven drone 300 and a server 100 for managing the drone 300 is provided with: a selecting means 111 for selecting a target parking place 400 from among a plurality of parking places 400 corresponding to a plurality of destinations; a first setting means and a second setting means 112, 113 for setting a direct route for causing a drone 300a parked in the target parking place 400 to fly between a first destination and a second destination without passing through any parking places 400, and a detour route for causing the drone 300a to fly between the first destination and the second destination via a parking place 400; a first calculating means 114 for calculating a flyable distance on the basis of a load weight and a remaining battery capacity; and a first determining means 116 for determining, on the basis of the flyable distance, one of the direct route and the detour route as a flight route for the drone 300a.

A system for displaying a taxi clearance including a display is configured to display the position of an aircraft during airport taxiing with a first sensor for detecting the location of the aircraft during airport taxiing. The display indicates the position of hold stops and taxi paths at the airport. An input provides taxi instructions into the system. The input may be manually by the pilot or via a transmission from the air traffic control. The taxi instructions are represented on the display as a taxi path on the taxiways with a first hold stop identified on the route. The display indicates the aircraft's proximity to said hold stop.

A system and method for aircraft ground guidance and control are characterized by: inputting to a server and managing the GPS coordinate values, representing fixed location information, of all airfield lights that are installed within an airfield and can be controlled and monitored; determining whether the real-time coordinates of a moving aircraft on radars cross the fixed coordinates of the airfield lights; and specifying the location of the aircraft if the coordinates of the aircraft cross the fixed coordinates of the airfield lights, and guiding and controlling the airfield lights in the path of the aircraft. Accordingly, provided are the benefits of managing the GPS coordinate values representing fixed location information of airfield lights input to a server, accurately identifying the location of a moving aircraft if the real-time coordinates of the moving aircraft on radars cross the fixed coordinates of the airfield lights, and guiding and controlling the aircraft and airfield lights.

The present invention relates to a navigation method and device (APP) for an aircraft (AC), and to an associated system (SYS), aircraft (AC), computer program (PROG) and data storage medium (MEM). The proposed method is characterised in that a navigation module (LOC_VIS) determines navigation data (OUT_LOC_VIS) for the aircraft (AC) and in that it comprises: during a landing phase (S50, S70) of the aircraft (AC), a step of determining navigation data (OUT_LOC_VIS) on the basis of data from an inertial measurement unit (IMU) and images acquired by the aircraft (AC); and during a taxiing phase (S90), a step of determining navigation data (OUT_LOC_VIS) on the basis of data from the inertial measurement unit (IMU) and an odometer (ODOM).

Methods and systems are provided for assisting operation of a vehicle using speech recognition and transcription to provide a conversation log graphical user interface (GUI) display. One method involves analyzing a transcription of an audio communication depicted on the conversation log GUI display to identify an operational subject associated with the audio communication, obtaining supplemental information pertaining to the operational subject from a data source, generating, on the conversation log GUI display, a selectable GUI element associated with the supplemental information, and in response to user selection of the selectable GUI element, generating, on the display device, a graphical representation based at least in part on the supplemental information. In some implementations, a separate supplemental information GUI display is presented in lieu of the conversation log GUI display in response to selection of the selectable GUI element.

The present disclosure envisages in the field of navigation of aircrafts, their landing and take-off in low visibility situations. The system (100) comprises User device (200), Terminal (300), Aircraft Speed, Brake & Engine Control Module (500), a server (400) and pass-through points (600). Terminal (300) is configured to Aircraft Speed, Brake & Engine Control Module (500) which is configured to Aircraft Speed System. The server (400) through user device (200) with air traffic controller creates geofences (on the defined stretched of aircraft flight paths, runway zones / taxi bay lanes) and stores rules associated with each such geofences. Terminal (300) when it is found in a geofence gets instructions / messages from server (400) and communicate to Aircraft Speed, Brake & Engine Control Module (500). Aircraft Speed, Brake & Engine Control Module (500) connected to aircraft speed management system accordingly changes / regulates the speed of the aircraft which remains valid until Terminal (300) gets a new instruction / message from server (400) once Terminal (300) is at a new geofence. Precise location coordinates-based navigation map (based on multiple geofences fusion / intersection / overlap) and glidepath is created connecting the centerline of touchdown zone of runway to the terminal (300) of the aircraft when terminal (300) is found at desired descent zone geofence location coordinates. Pilot follows the glidepath i.e. precise navigation map and altitude (and after considering other usual parameters like speed, wind speed and direction) at various stages of landing and take-off without a need of visuals / lighting system which is not available in low visibility situations like fog, heavy rains, night-time etc. The present disclosure provides a solution for landing and take-off in low visibility situation without investing heavily on infrastructure and certification of operations levels of airports like CAT II and CAT III.

A system comprises a controller onboard a vehicle that provides for autonomous taxiing of the vehicle at an airport. Onboard optical sensors include look-forward and look-down optical sensors. An onboard bidirectional radio receives ATC clearance information. The system also includes onboard aiding sensors, and an airport moving map database. An ATC clearance processing module translates the ATC clearance information into processor readable information. An image processing module receives image data from the optical sensors, and provides object detection and image interpretation functions. A taxi route generation module receives translated ATC clearance information and map information to generate a travel route at the airport for the vehicle. An aiding sensor data processing module determines state information for the vehicle based on vehicle state data. A command generation module generates commands for driving the vehicle. A control module automatically drives the vehicle on an airport surface, and controls a brake and throttle.

Techniques for traversing in an environment that includes at least one obstacle, by a mobile autonomous system, to a destination in the environment, are presented. The techniques can include generating, prior to the mobile autonomous system commencing activity in the environment, a graph including a plurality of vertices representing positions in the environment and a plurality of edges between vertices representing feasible transitions by the mobile autonomous vehicle in the environment; annotating the graph with at least one edge connecting a representation of a present position of the mobile autonomous system to a vertex of the graph; determining, based on the graph, a path from the present position of the mobile autonomous system in the environment to the destination; and traversing the environment to the destination, by the mobile autonomous system, based on the path.

Embodiments provide a flight supervision platform including a server computer and an animated graphical user interface for simultaneously monitoring multiple autonomous aircraft. More specifically, techniques disclosed herein provide a flight supervision platform that allows for one-to-many supervision where a remote supervisor (e.g., human or artificial intelligence) may continuously monitor and / or interact with a plurality of autonomous aircraft. For example, the autonomous aircraft may include one or more autonomous electric vertical takeoff landing (eVTOL) aircraft. The flight supervision platform may display information and receive input via the GUI and transmit commands based on the received input to the autonomous aircraft.

A computing system for combined aviation and ground mapping for aircraft navigation includes memory storing aviation map data, road map data, and instructions of an air-ground navigation program, and processing circuitry configured to implement the air-ground navigation program. The processing circuitry receives aircraft location data and destination data for a user of the aircraft. The destination data is cross-referenced with the road map data to validate a destination of the user, and a landing site proximate the destination is determined via the aviation map data. An air route segment from the location of the aircraft to the landing site and a ground route segment from the landing site to the destination are generated via respective algorithms. The air and ground route segments are combined to produce a combined air-ground navigation route, and a visual representation of the combined air-ground navigation route is output to a display device.

The present disclosure envisages in the field of navigation of aircrafts, their landing and take-off in low visibility situations. The system (100) comprises User device (200), Terminal (300), Aircraft Speed, Brake & Engine Control Module (500), a server (400) and pass-through points (600). Terminal (300) is configured to Aircraft Speed, Brake & Engine Control Module (500) which is configured to Aircraft Speed System. The server (400) through user device (200) with air traffic controller creates geofences (on the defined stretched of aircraft flight paths, runway zones / taxi bay lanes) and stores rules associated with each such geofences. Terminal (300) when it is found in a geofence gets instructions / messages from server (400) and communicate to Aircraft Speed, Brake & Engine Control Module (500). Aircraft Speed, Brake & Engine Control Module (500) connected to aircraft speed management system accordingly changes / regulates the speed of the aircraft which remains valid until Terminal (300) gets a new instruction / message from server (400) once Terminal (300) is at a new geofence. Precise location coordinates-based navigation map (based on multiple geofences fusion / intersection / overlap) and glidepath is created connecting the centerline of touchdown zone of runway to the terminal (300) of the aircraft when terminal (300) is found at desired descent zone geofence location coordinates. Pilot follows the glidepath i.e. precise navigation map and altitude (and after considering other usual parameters like speed, wind speed and direction) at various stages of landing and take-off without a need of visuals / lighting system which is not available in low visibility situations like fog, heavy rains, night-time etc. The present disclosure provides a solution for landing and take-off in low visibility situation without investing heavily on infrastructure and certification of operations levels of airports like CAT II and CAT III.

Tracking aircraft in and near a ramp area is described herein. One system for tracking an aircraft, comprising: a sensor array, positioned at or near a ramp area at an airport, having a camera image sensor device and a light detection and ranging (LIDAR) sensor device that are both located at a single sensor array location with respect to the ramp area; the camera image sensor device configured to capture camera image data illustrating the aircraft while the aircraft is approaching or in the ramp area; the LIDAR sensor device configured to capture LIDAR sensor device data illustrating the aircraft while the aircraft is approaching or in the ramp area.

A system and a method include an aircraft including one or more lighting devices. A control unit is in communication with the one or more lighting devices. The control unit is configured to operate the one or more lighting devices to provide illuminated messages regarding traffic in relation to one or more ground paths at an airport on one or more surfaces.

A prediction system with a unit for generating a current value of a parameter relating to the movement of an aircraft, a selection unit for a crew member of the aircraft to select an exit of the runway used for landing, an avionics computer for computing a value of the time of occupancy of the runway and a unit for transmitting this value to a user system. The avionics computer using an initial ground speed and an exit type and an exit distance of the exit selected, and computes the value of the time of occupancy of the runway, with the data and by using a prediction model comprising a neural network trained on the basis of landing data originating from previous landings.

A method to autonomously taxi an aircraft is disclosed. The method comprises receiving, by a processing means, a route intended for the aircraft 310. Obtaining, by the processing means, a route line based on the route 320. Determining, by the processing means, at least one route feature along the route line 330. Determining, by the processing means, a deviation of a nosewheel of the aircraft from the route line based on a current position of the aircraft provided by the aircraft global navigation satellite system (GNSS) 340. Outputting, by the processing means, an adjustment of an angle of the nosewheel to correct the deviation from the route based on the deviation of the nosewheel 350. Outputting, by the processing means, an adjustment of the speed of the aircraft based on a current position on the route line and a proximity to the at least one determined route feature 360.

A system and method for aircraft surface navigation is disclosed. The method may include receiving W-band data from a W-band sensor configured to detect objects including non-cooperative aircraft. The method may also include receiving Automatic Dependent Surveillance-Broadcast (ADS-B) data from an ADS-B receiver configured to receive ADS-B signals from cooperative aircraft. Additionally, the method may involve identifying navigation information based on the W-band data and the ADS-B data, the navigation information comprising cooperative traffic and non-cooperative traffic, and displaying the navigation information via a display.

To provide a method for preventing an alarm from being generated due to a stationary aircraft or the like on a runway or the like being erroneously detected as a foreign object, and for reducing a false alarm rate, and to provide a foreign object monitoring device using this method, and further to enable detection of foreign objects in the vicinity of the aircraft or the like.SOLUTION: A device has input / output means for an external aircraft monitoring system and an aircraft model corresponding to the shape of each aircraft or the like, and includes: identifying the position of the aircraft or the like using position information included in monitoring information received from the external aircraft monitoring system; selecting the aircraft model using the information of the aircraft model included in the monitoring information, and determining the orientation of the aircraft model using nose orientation information included in the monitoring information; and excluding, from the foreign object determination, a range corresponding to the selected aircraft model and the orientation of the determined aircraft model from the position of the aircraft or the like, based on the identified position of the aircraft or the like, thereby preventing erroneous detection as a foreign object, reducing the false alarm rate, and enabling detection of foreign objects in the vicinity of the aircraft or the like.SELECTED DRAWING: Figure 1

Exemplary disclosed embodiments include systems, methods, and devices for a ground maneuvering system for aircraft. The systems, methods, and devices may include a ground maneuvering system including at least one processor in communication with at least one sensor and at least one maneuvering vehicle. The at least one maneuvering vehicle includes one or more processors in communication with one or more sensors. The one or more processors can be configured to sense, using the one or more sensors, an area associated with a plurality of areas, to determine a location within area, or to maneuver the maneuvering vehicle to the location. The maneuvering vehicle may also include a movement system for maneuvering the maneuvering vehicle in or around the plurality of areas, and a platform configured for an aircraft.

A method comprising the steps of: providing (S1) a surroundings detection sensor on a tug, which is configured for manoeuvring a mobile object, continuously detecting (S2) a region of an outer surface of the mobile object with the surroundings detection sensor, continuously determining (S3) a position of a tug-bound system relative to the mobile object based on a predetermined geometric model of an outer surface of the mobile object and the detected region of the outer surface, wherein the steps of detecting (S2) and determining (S3) are performed when the mobile object is manoeuvred by the tug, and detecting (S4) objects in the surroundings of the mobile object with the surroundings detection sensor based on the step of continuously determining (S3) the position of the tug-bound system. Further a sensor system for detecting objects in the surroundings of the sensor system and for providing on a tug.

A monitoring system comprising multiple portable camera units may be formed by aircraft safety cones each housing a video camera for locally viewing under-wing turnaround activities of an aircraft at the stand. Each portable camera unit has a processor for relaying or processing data, images and the like captured by the unit. Data is sent wirelessly to a separate computer / or to other systems or personnel at the airport, or both.

A system may include a display (306) and a processor (404) communicatively coupled to the display. The processor may be configured to: output, to the display, a synthetic vision system (SVS) taxi mode exocentric view (202) of an aircraft when the aircraft is performing taxi operations and when the aircraft is on ground; receive a user input to switch the output of the SVS taxi mode exocentric view to an SVS flight mode egocentric view (102) from the aircraft; and switch the output of the SVS taxi mode exocentric view to output, to the display, the SVS flight mode egocentric view when the aircraft is performing taxi operations and when the aircraft is on ground based at least on the user input to switch from the SVS taxi mode exocentric view to the SVS flight mode egocentric view.

A method including: receiving data associated with a unique identity of a vehicle asset to identify the vehicle asset; receiving data associated with a unique identity of a smart wheel chock to identify the smart wheel chock; associating the identified vehicle asset with the identified smart wheel chock, such that a location of the smart wheel chock can represent a location of the vehicle asset; receiving location data associated with the location of the smart wheel chock, wherein the received location data is based, at least in part, on location data generated by one or more systems on the smart wheel chock; and identifying the vehicle asset location based on the received smart wheel chock location data such that the vehicle asset can be determined and relayed to a user device to locate the vehicle asset.