Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
  • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
  • G05D1/0011—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement
  • G05D1/0044—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement by providing the operator with a computer generated representation of the environment of the vehicle, e.g. virtual reality, maps
• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
  • G08G5/0004—Transmission of traffic-related information to or from an aircraft
  • G08G5/0013—Transmission of traffic-related information to or from an aircraft with a ground station
• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
  • G08G5/0017—Arrangements for implementing traffic-related aircraft activities, e.g. arrangements for generating, displaying, acquiring or managing traffic information
  • G08G5/0026—Arrangements for implementing traffic-related aircraft activities, e.g. arrangements for generating, displaying, acquiring or managing traffic information located on the ground
• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
  • G08G5/003—Flight plan management
  • G08G5/0039—Modification of a flight plan
• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
  • G08G5/0043—Traffic management of multiple aircrafts from the ground
• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
  • G08G5/0047—Navigation or guidance aids for a single aircraft
  • G08G5/0069—Navigation or guidance aids for a single aircraft specially adapted for an unmanned aircraft
• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
  • G08G5/0073—Surveillance aids
  • G08G5/0091—Surveillance aids for monitoring atmospheric conditions
• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
  • G08G5/04—Anti-collision systems
  • G08G5/045—Navigation or guidance aids, e.g. determination of anti-collision manoeuvers

Definitions

• UAV Unmanned Aerial Vehicle
• the Unmanned Aircraft System Traffic Management is an initiative sponsored by the Federal Aviation Administration (FAA) to enable multiple beyond visual line-of-sight drone operations at low altitudes (under 400 feet above ground level (AGL)) in airspace where FAA air traffic services are not provided.
• FAM Federal Aviation Administration
• AGL ground level
• a framework that extends beyond the 400 feet AGL limit is needed.
• unmanned aircraft that would be used by package delivery services and air taxis may need to travel above at altitudes above 400 feet.
• Such a framework requires technology that will allow the FAA to safely regulate unmanned aircraft.
• Embodiments according to the present invention are directed to methods, apparatuses, and computer program products for aggregating data for unmanned aerial vehicle (UAV) navigation. These embodiments include receiving data corresponding to geographic locations from a plurality of data servers, determining a data type for each of the data received from the plurality of data servers, generating, from the received data, a plurality of aggregate data layers each corresponding to one data type, generating, in dependence upon telemetry data received from at least one UAV, a telemetry data layer from the telemetry data, and generating a map, wherein the map is overlaid with the telemetry data layer and the plurality aggregate data layers.
• UAV unmanned aerial vehicle
• receiving, by a server, data corresponding to geographic locations from a plurality of data servers includes receiving data of the same data type for the same geographic location from at least two different data servers, attributing a trust value to the each of the plurality of data servers, and scoring, in dependence upon the trust value of each data server, the data received from each of the at least two sources.
• generating a map includes partitioning the map into contiguous geographic cells, wherein data from each data layer having a geographic location with a geographic cell boundary is included in the geographic cell, determining, in dependence upon the data from each data layer, a navigability risk factor for each geographic cell in each data layer, and determining an aggregate risk factor for a geographic cell by combining the risk factors from each data layer.
• These embodiments may further include displaying, in a user interface of the control device, the map partitioned into geographic cells, wherein each geographic cell includes indicia of the navigability risk.
• Some embodiments may further include receiving, from a user, a request to display a region of the map overlaid with a specific subset of the plurality of data layers, and responsive to the request, displaying, in the user interface of the control device, the map overlaid with the specified subset of the plurality of data layers, wherein the map includes indicia of navigability risk.
• the telemetry data received from the one or more UAVs may include telemetry data from one or more UAVs in a UAV network coordinated by the server and receiving telemetry data from one or more participating UAVs outside of the UAV network.
• the telemetry data received from one or mote UAVs in the UAV network may be received as part of a periodic signal to the server.
• the telemetry data may include pitch, yaw, roll, and location coordinates of the UAV, and may further include sensor data from at least one of a visual sensor and an acoustic sensor.
• FIG. 1 is a diagram illustrating a particular implementation of a system for aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• UAV unmanned aerial vehicle
• FIG. 2 is a block diagram illustrating another particular implementation of a system for aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• UAV unmanned aerial vehicle
• FIG. 3 is a block diagram illustrating a particular implementation of blockchain-based operations used by the systems of FIGs. 1-2.
• FIG. 4 is a flowchart of an example method for aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• FIG. 5 A is an example weather data layer used in aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• FIG. 5B is an example weather data layer used in aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• FIG. 5C is an example weather data layer used in aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• FIG. 6 is a flowchart of another example method for aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• UAV unmanned aerial vehicle
• FIG. 7 A is an example air congestion data layer used in aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• UAV unmanned aerial vehicle
• FIG. 7B is an example air congestion data layer used in aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• UAV unmanned aerial vehicle
• FIG. 8 is a flowchart of another example method for aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• UAV unmanned aerial vehicle
• FIG. 9 is an example weather data layer used in aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• FIG. 10 is an example weather data layer used in aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present invention.
• FIG. 11 is an example weather data layer used in aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present invention.
• FIG. 12 is a flowchart of another example method for aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• UAV unmanned aerial vehicle
• FIG. 13 is a flowchart of another example method for aggregating data for unmanned aerial vehicle (UAV) navigation in accordance with the present disclosure.
• UAV unmanned aerial vehicle
• an ordinal term e.g., “first,” “second,” “third,” etc.
• an element such as a structure, a component, an operation, etc.
• an ordinal term does not by itself indicate any priority or order of the element with respect to another element, but rather merely distinguishes the element from another element having a same name (but for use of the ordinal term).
• the term “set” refers to a grouping of one or more elements, and the term “plurality” refers to multiple elements.
• determining may be used to describe how one or more operations are performed. It should be noted that such terms are not to be construed as limiting and other techniques may be utilized to perform similar operations. Additionally, as referred to herein, “generating,” “calculating,” “estimating,” “using,” “selecting,” “accessing,” and “determining” may be used interchangeably. For example, “generating,” “calculating,” “estimating,” or “determining” a parameter (or a signal) may refer to actively generating, estimating, calculating, or determining the parameter (or the signal) or may refer to using, selecting, or accessing the parameter (or signal) that is already generated, such as by another component or device.
• Coupled may include “communicatively coupled,” “electrically coupled,” or “physically coupled,” and may also (or alternatively) include any combinations thereof.
• Two devices (or components) may be coupled (e.g., communicatively coupled, electrically coupled, or physically coupled) directly or indirectly via one or more other devices, components, wires, buses, networks (e.g., a wired network, a wireless network, or a combination thereof), etc.
• Two devices (or components) that are electrically coupled may be included in the same device or in different devices and may be connected via electronics, one or more connectors, or inductive coupling, as illustrative, non-limiting examples.
• two devices may send and receive electrical signals (digital signals or analog signals) directly or indirectly, such as via one or more wires, buses, networks, etc.
• electrical signals digital signals or analog signals
• directly coupled may include two devices that are coupled (e.g., communicatively coupled, electrically coupled, or physically coupled) without intervening components.
• FIG. 1 sets forth a diagram of a system (100) configured for aggregating data for UAV navigation according to embodiments of the present disclosure.
• the system (100) of FIG. 1 includes an unmanned aerial vehicle (UAV) (102), a control device (120), a server (140), an air traffic data server (160), a weather data server (170), a regulatory data server (180), and a topographical data server (190).
• a UAV commonly known as a drone, is a type of powered aerial vehicle that does not carry a human operator and uses aerodynamic forces to provide vehicle lift.
• UAVs are a component of an unmanned aircraft system (UAS), which typically include at least a UAV, a control device, and a system of communications between the two.
• UAS unmanned aircraft system
• the flight of a UAV may operate with various levels of autonomy including under remote control by a human operator or autonomously by onboard or ground computers.
• An UAV may not include a human operator pilot, however, some UAVs, such passenger drones (drone taxi, flying taxi, or pilotless helicopter) carry human passengers.
• the UAV (102) is illustrated as one type of drone.
• any type of UAV may be used in accordance with embodiments of the present disclosure and unless otherwise noted, any reference to a UAV in this application is meant to encompass all types of UAVs. Readers of skill in the art will realize that the type of drone that is selected for a particular mission or excursion may depend on many factors, including but not limited to the type of payload that the UAV is required to carry, the distance that the UAV must travel to complete its assignment, and the types of terrain and obstacles that are anticipated during the assignment.
• the UAV (102) includes a processor (104) coupled to a memory (106), a camera (112), positioning circuitry (114), and communication circuitry (116).
• the communication circuitry (116) includes a transmitter and a receiver or a combination thereof (e.g., a transceiver).
• the communication circuitry (116) (or the processor (104)) is configured to encrypt outgoing message(s) using a private key associated with the UAV (102) and to decrypt incoming message(s) using a public key of a device (e.g., the control device (120) or the server (140)) that sent the incoming message(s).
• a device e.g., the control device (120) or the server (140)
• communications between the UAV (102), the control device (120), and the server (140) are secure and trustworthy (e.g., authenticated).
• the camera ( 112) is configured to capture image(s), video, or both, and can be used as part of a computer vision system.
• the camera (112) may capture images or video and provide the video or images to a pilot of the UAV (102) to aid with navigation.
• the camera (112) may be configured to capture images or video to be used by the processor (104) during performance of one or mote operations, such as a landing operation, a takeoff operation, or object/collision avoidance, as non-limiting examples.
• a single camera (112) is shown in FIG. 1, in alternative implementations more and/or different sensors may be used (e.g., infrared, LIDAR, SONAR, etc.).
• the positioning circuitry (114) is configured to determine a position of the UAV (102) before, during, and/or after flight.
• the positioning circuitry ( 114) may include a global positioning system (GPS) interface or sensor that determines GPS coordinates of the UAV (102).
• GPS global positioning system
• the positioning circuitry' (114) may also include gyroscope(s), accelerometer(s), pressure sensor(s), other sensors, or a combination thereof, that may be used to determine the position of the UAV (102).
• the processor (104) is configured to execute instructions stored in and retrieved from the memory (106) to perform various operations.
• the instructions include operation instructions (108) that include instructions or code that cause the UAV (102) to perform flight control operations.
• the flight control operations may include any operations associated with causing the UAV to fly from an origin to a destination.
• the flight control operations may include operations to cause the UAV to fly along a designated route (e.g., based on route information (110), as further described herein), to perform operations based on control data received from one or more control devices, to take off, land, hover, change altitude, change pitch/yaw/roll angles, or any other flight-related operations.
• the UAV (102) may include one or more actuators, such as one or more flight control actuators, one or more thrust actuators, etc., and execution of the operation instructions (108) may cause the processor (104) to control the one or more actuators to perform the flight control operations.
• the one or more actuators may include one or more electrical actuators, one or more magnetic actuators, one or more hydraulic actuators, one or more pneumatic actuators, one or more other actuators, or a combination thereof.
• the memory (106) also includes route information (110) that indicates a flight path for the UAV (102) to follow.
• route information (110) may specify a starting point (e.g., an origin) and an ending point (e.g., a destination) for the UAV (102).
• the route information may also indicate a plurality of waypoints, zones, areas, regions between the starting point and the ending point.
• the route information (110) may also indicate a corresponding set of control devices for various points, zones, regions, areas of the flight path.
• the indicated sets of control devices may be associated with a pilot (and optionally one or more backup pilots) assigned to have control over the UAV (102) while the UAV (102) is in each zone.
• the route information (110) may also indicate time periods during which the UAV is scheduled to be in each of the zones (and thus time periods assigned to each pilot or set of pilots).
• the control device (120) includes a processor (122) coupled to a memory (124), a display device (132), and communication circuitry (134).
• the display device (132) may be a liquid crystal display (LCD) screen, a touch screen, another type of display device, or a combination thereof.
• the communication circuitry (134) includes a transmitter and a receiver or a combination thereof (e.g., a transceiver).
• the communication circuitry (134) (or the processor (122)) is configured to encrypt outgoing message(s) using a private key associated with the control device (120) and to decrypt incoming message(s) using a public key of a device (e.g., the UAV (102) or the server (140)) that sent the incoming message(s).
• a device e.g., the UAV (102) or the server (140)
• communication between tire UAV ( 102), the control device (120), and the server (140) are secure and trustworthy (e.g., authenticated).
• the processor (122) is configured to execute instructions from the memory (124) to perform various operations.
• the instructions also include control instructions (130) that include instructions or code that cause the control device (120) to generate control data to transmit to the UAV (102) to enable the control device (120) to control one or more operations of the UAV (102) during a particular time period, as further described herein.
• the server (140) includes a processor (142) coupled to a memory (146), and communication circuitry (144).
• the communication circuitry (144) includes a transmitter and a receiver or a combination thereof (e.g., a transceiver).
• the communication circuitry' (144) (or the processor (142)) is configured to encrypt outgoing message(s) using a private key associated with the server (140) and to decrypt incoming message(s) using a public key of a device (e.g., the UAV (102) or the control device (120)) that sent the incoming message(s).
• a device e.g., the UAV (102) or the control device (120)
• communication between the UAV (102), the control device (120), and the server (140) are secure and trustworthy (e.g., authenticated).
• the processor (142) is configured to execute instructions from the memory (146) to perform various operations.
• the instructions include aggregation instructions (148) that include instructions or code that cause the processor (142) to aggregate data for UAV navigation from a plurality of data sources (160, 170, 180, 190).
• the aggregation instructions (148) cause the processor (142) to receive data from disparate data servers, aggregate the data into data layers according to data types, aggregate data from a one or more UAVs into a UAV data layer, and generate a map selectively overlaid with one or more of the data layers.
• the aggregation instructions (148) are configured to receive data corresponding to geographic locations from a plurality of data servers, determine a data type for each of the data received from the plurality of data servers, generate, from the received data, a plurality of aggregate data layers each corresponding to one datatype, generate, in dependence upon telemetry data received from at least one UAV, a telemetry data layer from the telemetry data, and generate a map, wherein the map is overlaid with the telemetry data layer and the plurality aggregate data layers.
• the instructions may also include control instructions (150) that include instructions or code that cause the server (140) to generate control data to transmit to the UAV (102) to enable the server (140) to control one or more operations of the UAV (102) during a particular time period, as further described herein.
• the UAV (102), the control device (120), and server (140) are communicatively coupled via a network (118).
• the network (118) may include a satellite network or another type of network that enables wireless communication between the UAV (102), the control device (120), and the server (140).
• the control device ( 120), the server (140) communicate with the UAV ( 102) via separate networks (e.g., separate short range networks.
• minimal (or no) manual control of the UAV (102) may be performed, and the UAV (102) may travel from the origin to the destination without incident.
• one or more pilots may control the UAV (102) during a time period, such as to perform object avoidance or to compensate for an improper UAV operation.
• tire UAV (102) may be temporarily stopped, such as during an emergency condition, for recharging, for refueling, to avoid adverse weather conditions, responsive to one or more status indicators from the UAV (102), etc.
• the route information (110) may be updated (e.g., via a subsequent blockchain entry, as further described herein) by the UAV (102) or by the server (140)).
• the updated route information may include updated waypoints, updated time periods, and updated pilot assignments.
• the route information is exchanged using a blockchain data structure.
• the blockchain data structure is shared in a distributed manner across a plurality of devices of the system (100), such as the UAV (102), the control device (120), the server (140), and any other control devices or UAVs in the system (100).
• each of the devices of the system (100) stores an instance of the blockchain data structure in a local memory of the respective device.
• each of the devices of the system (100) stores a portion of the shared blockchain data structure and each portion is replicated across multiple of the devices of the system ( 100) in a manner that maintains security of the shared blockchain data structure as a public (i.e., available to other devices) and incorruptible (or tamper evident) ledger.
• the blockchain data structure may include, among other things, route information associated with the UAV (102).
• the route information (110) may be used to generate blocks of the blockchain data structure.
• a sample blockchain data structure (300) is illustrated in FIG. 3. Each block of the blockchain data structure (300) includes block data and other data, such as availability data or route data.
• the block data of each block includes information that identifies the block (e.g., a block ID) and enables the devices of the system (100) to confirm the integrity of the blockchain data structure (300).
• the block data also includes a timestamp and a previous block hash.
• the timestamp indicates a time that the block was created.
• the block ID may include or correspond to a result of a hash function (e.g., a SHA256 hash function, a RIPEMD hash function, etc.) based on the other information (e.g., the availability data or the route data) in the block and the previous block hash (e.g., the block ID of the previous block). For example, in FIG.
• the blockchain data structure (300) includes an initial block (Bk_0) (302) and several subsequent blocks, including a block Bk_1 (304), a block Bk_2 (306), and a block Bk_n (308).
• the initial block Bk_0 (302) includes an initial set of availability data or route data, a timestamp, and a hash value (e.g., a block ID) based on the initial set of availability data or route data.
• the block Bk_1 (304) also includes a hash value based on the other data of tire block Bk_1 (304) and the previous hash value from the initial block Bk_0 (302).
• the block Bk_2 (306) other data and a hash value based on the other data of the block Bk_2 (306) and the previous hash value from the block Bk_1 (304).
• the block Bk_n (308) includes other data and a hash value based on the other data of the block Bk_n (308) and the hash value from the immediately prior block (e.g., a block Bk_n-1).
• This chained arrangement of hash values enables each block to be validated with respect to the entire blockchain; thus, tampering with or modifying values in any block of the blockchain is evident by calculating and verifying the hash value of the final block in the block chain. Accordingly, the blockchain acts as a tamper-evident public ledger of availability data and route data for the system (100).
• each block of the blockchain data structure (300) includes availability data or route data.
• the block Bk_1 (404) includes availability data that includes a user ID (e.g., an identifier of the mobile device, or the pilot, that generated the availability data), a zone (e.g., a zone at which the pilot will be available), and an availability time (e.g., a time period the pilot is available at the zone to pilot a UAV).
• the block Bk_n (408) includes route information that includes a UAV ID, a start point, an end point, and time periods, primary pilot assignments, and backup pilot assignments for each zone associated with the route.
• the server (140) includes software that is configured to receive telemetry information from an airborne UAV and track the UAV’s progress and status.
• the server (140) is also configured to transmit in-flight commands to the UAV. Operation of the control device and the server may be carried out by some combination of a human operator and autonomous software (e.g., artificial intelligence (AI) software that is able to perform some or all of the operational functions of a typical human operator pilot).
• AI artificial intelligence
• the aggregation instructions (148) cause the server (140) to receive numerous types of information relevant to the navigation of a UAV from numerous data servers, aggregate the data according to the type of data, and generate a map upon which data of the same type is presented as layer that is overlaid on the map.
• the server (140) may receive data over the network (119) from an air traffic data server (160), a weather data server (170), a regulatory data server (180), and a topographic data server (190). It will be recognized by those of skill in the art that other data servers useful in flight path planning of a UAV may also provide data to the server (140) over the network (101) or through direct communication with the server (140).
• the air traffic data server (160) may include a processor (162), memory (164), and communication circuitry (168).
• the memory (164) of the air traffic data server (160) may include operating instructions (166) that when executed by the processor (162) cause the processor to provide the air traffic data (167) about the flight paths of other aircraft in a region, including those of other UAVs.
• the air traffic data may also include real-time radar data indicating the positions of other aircraft, including other UAVs, in the immediate vicinity or in the flight path of a particular UAV.
• Air traffic data servers may be, for example, radar stations, airport air traffic control systems, the FAA, UAV control systems, and so on.
• the weather data server (170) may include a processor (172), memory (174), and communication circuitry (178).
• the memory (174) of the weather data server (170) may include operating instructions (176) that when executed by the processor (172) cause the processor to provide weather data (177) about atmospheric conditions along the UAV’s flight path, such as temperature, wind, precipitation, lightening, humidity, atmospheric pressure, and so on.
• Weather data servers may be, for example, the National Weather Service (NWS), the National Oceanic and Atmospheric Administration (NOAA), local meteorologists, radar stations, other aircraft, and so on.
• the regulatory data server (180) may include a processor (182), memory (184), and communication circuitry (188).
• the memory (184) of the weather data server (180) may include operating instructions (186) that when executed by the processor (182) cause the processor provide regulatory data (187) about laws and regulations governing a particular region of airspace, such as airspace restrictions, municipal and state laws and regulations, permanent and temporary no-fly zones, and so on.
• Regulatory data servers may include, for example, the FAA, state and local governments, the Department of Defense, and so on.
• the topographical data server (190) may include a processor (192), memory (194), and communication circuitry (198).
• the memory (194) of the topographical data server (190) may include operating instructions (196) that when executed by the processor (192) cause the processor to provide topographical data about terrain, places, structures, transportation, boundaries, hydrography, orthoimagery, land cover, elevation, and so on.
• Topographic data may be embodied in, for example, digital elevation model data, digital line graphs, and digital raster graphics.
• Topographic data servers may include, for example, the United States Geological Survey or other geographic information systems (GISs).
• GISs geographic information systems
• the server (140) may aggregate data from the data servers (160, 170, 180, 190) using application program interfaces (APIs), syndicated feeds and extensible Markup Language (XML), natural language processing, JavaScript Object Notation (JSON) servers, or combinations thereof. Updated data may be pushed to the server (140) or may be pulled on-demand by the server (140).
• the FAA may be an important data server for both airspace data concerning flight paths and congestion as well as an important data server for regulatory data such as permanent and temporary airspace restrictions.
• the FAA provides the Aeronautical Data Delivery' Service (ADDS), the Aeronautical Product Release API (APRA), System Wide Information Management (SWIM), Special Use Airspace information, and Temporary Flight Restrictions (TFR) information, among other data.
• the National Weather Service (NWS) API allows access to forecasts, alerts, and observations, along with other weather data.
• NWS National Weather Service
• the USGS Seamless Server provides geospatial data layers regarding places, structures, transportation, boundaries, hydrography, orthoimagery, land cover, and elevation. Readers of skill in the art will appreciate that various governmental and non-governmental entities may act as data servers and provide access to that data using APIs, JSON, XML, and other data formats.
• the server (140) can communicate with a UAV (102) using a variety of methods.
• the UAV (102) may transmit and receive data using Cellular, 5G, SublGHz, SigFox, WiFi networks, or any other communication means that w'ould occur to one of skill in the art.
• the network (119) may comprise one or more Local Area Networks (LANs), Wide Area Networks (WANs), cellular networks, satellite networks, internets, intranets, or other networks and combinations thereof.
• the network (119) may comprise one or more wired connections, wireless connections, or combinations thereof.
• FIG. 1 The arrangement of servers and other devices making up the exemplary system illustrated in FIG. 1 are for explanation, not for limitation.
• Data processing systems useful according to various embodiments of the present invention may include additional servers, routers, other devices, and peer-to-peer architectures, not shown in FIG. 1, as will occur to those of skill in the art.
• Networks in such data processing systems may support many data communications protocols, including for example TCP (Transmission Control Protocol), IP (Internet Protocol), HTTP (HyperText Transfer Protocol), and others as will occur to those of skill in the art.
• Various embodiments of the present invention may be implemented on a variety of hardware platforms in addition to those illustrated in FIG. 1.
• the aggregation instructions (148) include computer program instructions for receiving data from disparate data servers, aggregating the data into data layers according to data types, aggregating data from a one or more UAVs into a UAV data layer, and generating a map selectively overlaid with one or more of the data layers.
• aggregation instructions (148) are configured to receive data corresponding to geographic locations from a plurality of data servers, determine a data type for each of the data received from the plurality of data servers, generate, from the received data, a plurality of aggregate data layers each corresponding to one datatype, generate, in dependence upon telemetry' data received from at least one UAV, a telemetry data layer from the telemetry' data, and generate a map, wherein the map is overlaid with the telemetry data layer and the plurality aggregate data layers.
• FIG. 2 sets forth a block diagram illustrating another implementation of a system (200) for aggregating data for UAV navigation.
• the system (200) of FIG. 2 shows an alternative configuration in which one or more additional UAVs (103) configured similarly to the UAV (102) is included in the UAV network (118).
• the one or more additional UAVs (103) may each be under the control of the control device (120) or another control device (not shown) connected to the UAV network (118).
• flight plan information for the UAVs (102, 103) may be provided to the server (140) by the control device (120)
• telemetry data and other inflight data may be provided by the UAVs (102, 103) to the server (140), such that server (140) is aware of the current location and intended flight paths of UAVs within the UAV network (118).
• the server (140) may provide the control device (120) with information relevant to the navigability of a particular flight path, UAV location information to prevent the collision, in-flight information received from the UAVs (102, 103) about weather conditions and obstructions, and the like.
• the UAVs (102, 103) and control device (120) may communicate with each other using a security protocol or virtual private network, such that communication among members of the UAV network can be trusted.
• a UAV (105) that is outside of the UAV network (118) may participate by communicating with the server (140) over the communications network (119), and may also provide telemetry data and other in-flight data to the server (140).
• the server (140) may apprise the control devices (120) of the location of the out-of-network UAV (105).
• FIG. 4 sets forth a flow chart illustrating an exemplary method for aggregating data for unmanned aerial vehicle (UAV) navigation according to embodiments of the present invention that comprises receiving (410) data corresponding to geographic locations from a plurality of data servers.
• Receiving (410) data corresponding to geographic locations from a plurality of data servers may be carried out by the aggregation instructions (148) on the server (140) receiving real-time data from data servers such as the airspace data server (160), the weather data server (170), the regulatory' data server (180), and the topographic data server (190).
• the received data may correspond to a geographic place, a specific geographic coordinate, a geographic boundary, or other geospatial reference.
• the data is associated with an altitude in addition to longitudinal and latitudinal reference points.
• restricted airspace data from a regulatory data server (180) may have longitudinal/latitudinal perimeter as well as an altitude range to which the restriction applies.
• the method of FIG. 4 further comprises determining (420) a data type for each of the data received from the plurality of data servers. Determining (420) a data type for each of the data received from the plurality of data servers may be carried out by the aggregation instructions (148) defining a plurality of data types, analyzing the data to determine a classification for the type of data, and assigning at least one data type to received data.
• an airspace traffic data type may be assigned to data such as radar data indicating the presence of aircraft at or near a particular geographic location, flight path data indicating the anticipated course of an aircraft as it relates to a particular geographic location, reports of unidentified or “rogue” aircraft at a particular geographic location, and other data relating to airborne objects known or detected at a geographic location.
• Data classified by the air traffic data type may be received from an air traffic data server (160) such as, for example, the FAA, airport control towers, military bases, radar stations, and aircraft flight monitoring sources that will occur to those of skill in the art.
• a weather data type may be assigned to data pertaining to weather conditions such as wind speed, wind direction, air pressure, precipitation, lightening, and other types of weather that may impose a difficulty on UAV navigation.
• the weather data type may include relative measures of adverse weather conditions, such as “severe winds” or “heavy rain,” or raw weather data.
• Data classified by the weather data type may be received from a weather data server (170) such as, for example, the NWS, meteorological radar station, and other weather monitoring sources that will occur to those of skill in the art.
• a topographic data type may be assigned to data representing structures, terrain, and other land features that may be an obstruction at a particular altitude.
• a topographic data type may relate to data that is a relative measure of whether an area is rural or urban, sparsely populated or densely populated, flat or mountainous, undeveloped or containing numerous structures.
• a topographic data type may include, for example, digital elevation model data, digital line graphs, and digital raster graphics.
• a topographic data type may also relate to taw elevation data of land features and structures, such as maximum height data, footprints, three-dimensional data, and so on. Data classified by the topographic data type may be received from a topographic data server (190) such as, for example, the USGS.
• a regulatory' data type may be assigned to data pertaining to airspace regulations and restrictions, such as airspace classes, altitude regulations, restricted airspaces, temporary airspace restrictions, geo-fences, privacy regulations, and so on.
• airspace regulations and restrictions such as airspace classes, altitude regulations, restricted airspaces, temporary airspace restrictions, geo-fences, privacy regulations, and so on.
• a military base may have a restricted airspace that is a no- fly zone for non-military aircraft
• an airport may have an airspace that is restricted for specific types of UAVs
• municipalities may have regulations against UAVs at certain altitudes
• an temporary airspace restrictions may be placed in an area hosting a special event, e.g., a diplomatic summit or major sporting event, or a geo-fence may protect an area of private property.
• Data classified by the regulatory data type may be received from a regulatory data server (180) such as, for example, the FAA, the Department of Defense, municipal governments, and so on.
• the method of FIG. 4 further comprises generating (430), from the received data, a plurality of aggregate data layers each corresponding to one data type.
• Generating (430), from the received data, a plurality of aggregate data layers each corresponding to one data type may be carried out by the aggregation instructions (148) extracting all data of the same data type from the data received from the plurality of data servers, combining the extracted data into a single dataset, and plotting the dataset according to geographic location.
• Combining the extracted data into a single dataset may include amplifying data points on which two or more data servers agree.
• FIG. 5A depicts weather radar data from a first data server indicating the current location of a storm system.
• FIG. 5A depicts weather radar data from a first data server indicating the current location of a storm system.
• FIG. 5B depicts weather radar data from a second data server indicating the current location of a storm system.
• FIG. 5C depicts an aggregate of weather data from the first data server and the second data server in which datapoints upon which the first data server and second data server agree are amplified, shown in FIG. 5C by darker shading.
• the amplified datapoints indicate a confidence score for the data.
• the amplified data points may also indicate a probability of risk.
• the method of FIG. 4 further comprises generating (440), in dependence upon telemetry data received from at least one UAV, a telemetry' data layer from the telemetry data.
• Generating (440), in dependence upon telemetry data received from at least one UAV, a telemetry data layer from the telemetry data may be carried out by the aggregation instructions (148) receiving telemetry data from each of the at least one UAV (102), determining a current location and heading for the UAV (102), and plotting the geographic location and heading.
• Receiving telemetry data from a UAV in-flight may be carried out by the aggregation instructions (148) receiving, at the server (140), aperiodic “heartbeat” signal from the UAV (102) in the UAV network (118).
• the heartbeat signal may be transmitted to the server, for example, every second by each UAV in the network that is currently in operation.
• the telemetry data may include directional components such as pitch, roll, yaw, compass heading, and air speed, as well as location components such as GPS coordinates and altitude.
• Other telemetry data transmitted by a UAV (102) may be transmitted less frequently than the heartbeat signal, transmitted as part of an alert, or may be transmitted at the request of the server.
• Other telemetry data transmitted by a UAV (102) may include but is not limited to flight time, battery level, propeller operation levels, payload weight, history log, details of maintenance performed, flight time, cumulative flight time, time since last maintenance, downtime, and any other type of information that could be collected by the UAV regarding its status, operational parameters, performance, or history.
• Other telemetry data may also include sensor readings from sensors and devices such as a video camera, an infrared camera, acoustic sensors, distance sensors, optic flow sensors, and other sensors and devices as will occur to those of skill in the art.
• acoustic sensors may detect an obstruction while the UAV is flying at a particular altitude.
• the acoustic sensor data, GPS location, and altimeter reading may be transmitted to the server (140).
• the aggregation instructions (148) may then include the obstruction as part of the data plotted in the telemetry data layer.
• the method of FIG. 4 further comprises generating (450) a map, wherein the map is overlaid with the telemetry data layer and the plurality aggregate data layers.
• Generating (450) a map, wherein the map is overlaid with the telemetry data layer and the plurality aggregate data layers may be carried out by the aggregation instructions (148) obtaining a map of a particular region, identifying geographic points in the map that correspond to geographic points associated with data represented in tire telemetry data layer and one or more aggregate data layers, and superimposing data from the telemetry data layer and the one or more aggregate data layers aligned to the identified geographic points.
• the generated map may be provided to a control device in response to a request for the map.
• a user interface of the aggregation instructions (148) at the control device allows the user to select which data layers to display in the map.
• the aggregation instructions (148) may transmit to the user interface the generated map with the selected data layers.
• the user interface may be a web browser.
• the generated map may be rendered in two dimensions or three dimensions. A two dimensional rendering of the map may pertain to specific altitudes or airspace classes, while a three dimensional rendering of the map may also illustrate the data in each data layer at range altitudes and multiple airspace classes. [0070] For further explanation, FIG.
• FIG. 6 sets forth a flow chart illustrating a further exemplar ⁇ ' method of aggregating data for unmanned aerial vehicle (UAV) navigation according to embodiments of the present invention.
• FIG. 6 also includes receiving (410) data corresponding to geographic locations from a plurality of data servers, determining (420) a data type for each of the data received from the plurality of data servers, generating (430), from the received data, a plurality of aggregate data layers each corresponding to one data type, generating (440), in dependence upon telemetry data received from at least one UAV, a telemetry data layer from the telemetry data, and generating (450) a map, wherein the map is overlaid with the telemetry data layer and the plurality aggregate data layers.
• UAV unmanned aerial vehicle
• the method of FIG. 6 differs from FIG. 4 in that the wherein receiving (410) data corresponding to geographic locations from a plurality of data servers includes receiving (610) data of the same data type for the same geographic location from at least two different data servers.
• FIG. 7A depicts radar data from a major international airport with high resolution radar showing a number of aircraft indicated by shaded circles.
• FIG. 7B depicts radar data from a small regional airport without high resolution radar for the same geographic area.
• One aircraft (710) present in the radar data of FIG. 7B is absent from the radar data of Figure 7A.
• the method of FIG. 6 differs from FIG. 4 in that receiving (410) data corresponding to geographic locations from a plurality' of data servers further includes attributing (620) a trust value to the each of the plurality of data servers.
• the reliability of the data may depend on how trusted the source can be to provide reliable data.
• the reliability of the source may be measured, for example, by the technology with which the data was collected, past history of providing reliable or unreliable data, accountability, or the age of the data.
• the major international airport with high resolution radar maybe attributed a higher trust value than the small regional airport without high resolution radar based on a technology factor, i.e., high resolution radar.
• the method of FIG. 6 differs from FIG. 4 in that receiving (410) data corresponding to geographic locations from a plurality of data servers further includes scoring (630), in dependence upon the trust value of each data server, the data received from each of the at least two sources. Scoring (630), in dependence upon the trust value of each data server, the data received from each of the at least two sources be carried out by increasing the weight of data received from a data server with a higher trust value, or by decreasing the weight of data received from a data server with a lower trust value.
• the data point corresponding to the additional aircraft (710) may be scored with a weight of 0.50, thus indicating a 50% reliability or confidence rating in the data.
• the scored data may be used differently according to different data models.
• a risk-accepting data model may dictate that data with less than a 75% confidence score should be disregarded as outlier data, while a risk-averse data model may dictate that data with a confidence score of 50% or greater should be regarded as true data.
• the aircraft (710) may be reflected as true data in the data layer.
• FIG. 8 sets forth a flow chart illustrating a further exemplary method for aggregating data for unmanned aerial vehicle (UAV) navigation according to embodiments of the present invention.
• FIG. 8 also includes receiving (410) data corresponding to geographic locations from a plurality of data servers, determining (420) a data type for each of the data received from the plurality of data servers, generating (430), from the received data, a plurality of aggregate data layers each corresponding to one data type, generating (440), in dependence upon telemetry data received from at least one UAV, a telemetry data layer from the telemetry data, and generating (450) a map, wherein the map is overlaid with the telemetry' data layer and the plurality aggregate data layers.
• UAV unmanned aerial vehicle
• the method of FIG. 8 differs from the method of FIG. 4 in that generating (450) a map, wherein the map is overlaid with the telemetry data layer and the plurality aggregate data layers, includes partitioning (810) the map into contiguous geographic cells, wherein data from each data layer having a geographic location with a geographic cell boundary is included in the geographic cell. Partitioning (810) the map into contiguous geographic cells, wherein data from each data layer having a geographic location with a geographic cell boundary is included in the geographic cell, may be carried out by the aggregation instructions (148) overlaying a set of contiguous cells over the map, as shown in the exemplary map (900) of Figure 9. Each geographic cell represents a geographic boundary of fixed size. However, a geographic cell may be further subdivided into smaller geographic cells.
• the method of FIG. 8 further differs from the method of FIG. 4 in that generating (450) a map, w'hercin the map is overlaid with the telemetry data layer and the plurality' aggregate data layers, further includes determining (820), in dependence upon the data from each data layer, a navigability risk value for each geographic cell in each data layer. Determining (820), in dependence upon the data from each data layer, a navigability risk factor for each geographic cell in each data layer may be carried out by the aggregation instructions (148) determining a relative navigability risk for particular data point.
• an airspace risk factors F a may include a relative range of airspace congestion, for example, wherein 0 is the least congested and 9 is the most congested.
• Weather risk factors F w may include a relative measure of adverse weather conditions, wherein 0 is no adverse weather conditions and 9 is highly adverse weather conditions.
• Topographic risk factors Ft may include a relative measure difficulty imposed by the topography of the cell, wherein 0 is no difficulty and 9 is the most difficult topography to navigate.
• Regulatory risk factors F r may include a relative measure of difficulty imposed by regulations, laws and restrictions on navigation through a cell.
• the exemplary map (900) of a weather data layer in FIG. 9 illustrates that tight rain in a cell (910) associated with a low risk factor of “1” is indicated by tight shading, average rain in cell (920) is associated with a medium risk factor of “5’' is indicated by medium shading, and heavy rain in cell (930) associated with a high risk factor of “9” is indicated by dark shading.
• the method of FIG. 8 further differs from the method of FIG. 4 in that generating (450) a map, wherein the map is overlaid with the telemetry data layer and the plurality aggregate data layers, further includes determining (830) an aggregate risk factor for a geographic cell by combining the risk factors from each data layer. Determining (830) an aggregate risk factor for a geographic cell by combining the risk factors from each data layer may be carried out by adding, for each geographic cell, the risk factors in each data layer. [0079] For example, the exemplary map (1000) of an airspace data layer in FIG.
• FIG. 10 illustrates a that medium congestion in the cell (910) associated with a risk factor of “5” is indicated by medium shading, heavy congestion in the a (1010) associated with a risk factor of “9” is indicated by dark shading, and tight congestion in the a (1020) associated with a risk factor of “1” is indicated by tight shading.
• FIG. 12 sets forth a flow chart illustrating a further exemplary method for aggregating data for unmanned aerial vehicle (UAV) navigation according to embodiments of the present invention.
• UAV unmanned aerial vehicle
• the method of FIG. 12 also includes receiving (410) data corresponding to geographic locations from a plurality of data servers, determining (420) a data type for each of the data received from the plurality of data servers, generating (430), from the received data, a plurality of aggregate data layers each corresponding to one data type, generating (440), in dependence upon telemetry data received from at least one UAV, a telemetry data layer from the telemetry data, and generating (450) a map, wherein the map is overlaid with the telemetry data layer and the plurality aggregate data layers, including partitioning (810) the map into contiguous geographic cells, wherein data from each data layer having a geographic location with a geographic cell boundary is included in the geographic cell, determining (820), in dependence upon the data from each data layer, a navigability risk factor for each geographic cell in each data layer, and determining (830) an aggregate risk factor for a geographic cell by combining the risk factors from each data layer. [0082] The method of FIG. 12 differs from the method of FIG
• each geographic cell includes indicia of the aggregate navigability risk factor.
• Displaying (1140), in a user interface of the control device, the map partitioned into geographic cells, wherein each geographic cell includes indicia of the aggregate navigability risk may be carried out by indicating a navigability risk for a geographic with degrees of indicia, e.g., shading or stippling, that the represents calculated total risk factor value.
• Displaying ( 1140), in a user interface of the control device, the map partitioned into geographic cells, wherein each geographic cell includes indicia of the aggregate navigability risk, may also be carried out by combining indicia from multiple data layers such that the combination of indicia represents an aggregated risk factor.
• shading indicated in the weather data layer may be combined with shading in the airspace data layer to demonstrate the enhanced risk.
• FIG. 13 sets forth a flow chart illustrating a further exemplary method for aggregating data for unmanned aerial vehicle (UAV) navigation according to embodiments of the present invention.
• FIG. 13 also includes receiving (410) data corresponding to geographic locations from a plurality of data servers, determining (420) a data type for each of the data received from the plurality of data servers, generating (430), from the received data, a plurality of aggregate data layers each corresponding to one data type, generating (440), in dependence upon telemetry data received from at least one UAV, a telemetry data layer from the telemetry data, and generating (450) a map, wherein the map is overlaid with the telemetry data layer and the plurality aggregate data layers.
• UAV unmanned aerial vehicle
• the method of FIG. 13 differs from the method of FIG. 4 in that the method of FIG.
• Receiving (1310), from a user, a request to display a region of the map overlaid with a specific subset of the plurality of data layers may be carried out by receiving, through a control device (120) in the UAV network (118) a request from an operator for a map of a region in which a UAV (102) is flying.
• the request may be transmitted via a user interface of the control device (120).
• the operator may select a subset of the data layers for display on in the requested map. For example, the operator may select to receive the telemetry data layer, the weather data layer, and the airspace congestion data layer.
• the request is received at the server (140) by the aggregation instructions (148).
• the method of FIG. 13 further differs from the method of FIG. 4 in that the method of FIG. 13 further comprises responsive to the request, displaying (1320), in the user interface of the control device, the map overlaid with the specified subset of the plurality of data layers, wherein the map includes indicia of navigability risk.
• Displaying (1320), in the user interface of the control device, the map overlaid with the specified subset of the plurality of data layers, wherein the map includes indicia of navigability risk may be carried out by the aggregation instructions (148) transmitting, to the control device (120), the map region overlaid with only the specified data layers, i.e., the telemetry data layer, the weather data layer, and the airspace congestion data layer.
• the map may include indicia of navigability such as relative measures of risk to UAV navigation posed by data in each of the selected data layers. Relative measures of risk may be represented as risk factors, as discussed above. [0086]
• the benefits of aggregating data for UAV navigation include a navigation data aggregator that multiple different types of information from multiple different data servers pertaining to geographical data, flight conditions, flight path rules and restrictions, and other data relevant to UAV navigation.
• the navigation data aggregator can receive this data in real-time and organize the data into meaningful layers that enable the UAV operator or autonomous route planning software to assess risks in a particular flight path. It will be appreciated that the navigation data aggregator processes large amounts of data in-real time in a manner than could not be performed by a human operator, and the information is aggregated and organized into a subset of visual data that can be readily consumed by a human operator.
• Exemplary embodiments of the present invention are described largely in the context of a fully functional computer system for aggregating data for unmanned aerial vehicle (UAV) navigation. Readers of skill in the art will recognize, however, that the present invention also may be embodied in a computer program product disposed upon computer readable storage media for use with any suitable data processing system.
• Such computer readable storage media may be any storage medium for machine-readable information, including magnetic media, optical media, or other suitable media. Examples of such media include magnetic disks in hard drives or diskettes, compact disks for optical drives, magnetic tape, and others as will occur to those of skill in the art.
• the present invention may be a system, a method, and/or a computer program product.
• the computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry- out aspects of the present invention.
• the computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device.
• the computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
• a non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD- ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing.
• RAM random access memory
• ROM read-only memory
• EPROM or Flash memory erasable programmable read-only memory
• SRAM static random access memory
• CD- ROM compact disc read-only memory
• DVD digital versatile disk
• memory stick a floppy disk
• a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon
• a computer readable storage medium is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiberoptic cable), or electrical signals transmitted through a wire.
• Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network.
• the network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.
• a network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
• Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instraction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
• the computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
• the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
• electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
• These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
• These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
• the computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
• each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s).
• the functions noted in the block may occur out of the order noted in the figures.
• two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

Applications Claiming Priority (3)

Publications (1)

Family

ID=72521718

Family Applications (1)

Country Status (2)

Families Citing this family (1)

Family Cites Families (7)

• 2020
  • 2020-09-01 WO PCT/US2020/048858 patent/WO2021046005A1/en unknown
  • 2020-09-01 EP EP20772482.4A patent/EP4014218A1/de active Pending

Also Published As

Similar Documents

Legal Events