Abstract: Methods, systems, apparatuses, and computer program products for UAV module management are disclosed. In a particular embodiment, UAV module management includes software module library management by a computing system. In this embodiment, the computing system presents information representing a plurality of UAV software modules, receives information representing a UAV software module selection, and adds the UAV software module identified by the information representing a UAV software module selection to a UAV software module library. According to this embodiment, the computing system adds, based on a selection of a UAV software module, the selected UAV module to a UAV software module library.

Abstract: In a particular embodiment, parallel deconfliction processing of unmanned aerial vehicles (UAVs) is disclosed that includes a UAV control system partitioning the airspace over a geographic region into a plurality of airspace regions. In this example embodiment, the UAV control system includes a plurality of deconfliction controllers. For each deconfliction controller in the plurality of deconfliction controllers, the UAV control system assigns to the deconfliction controller, one or more airspace regions of the plurality of airspace regions. The UAV control system also inputs, in parallel, flight path data for a plurality of unmanned aerial vehicles (UAVs) to the plurality of deconfliction controllers. This embodiment also includes processing, in parallel by the plurality of deconfliction controllers, the flight path data to identify a flight conflict between two or more UAVs. Each deconfliction controller outputs a deconfliction result for each airspace region.

Abstract: Methods, systems, apparatuses, and computer program products for predictive maintenance of an unmanned aerial vehicle (UAV) are disclosed. In a particular embodiment, a method of predictive maintenance of a UAV includes a maintenance controller detecting a deviation in an expected behavior of a UAV and determining whether to attribute the deviation to any environmental interferences. In this example embodiment, after determining to not attribute the deviation to any environmental interferences, the maintenance controller schedules the UAV for maintenance.

Abstract: In a particular embodiment, recording data associated with an unmanned aerial vehicle (UAV) is disclosed that includes a blockchain manager receiving a transaction message associated with a UAV. The particular embodiment also includes the blockchain manager using the information within the transaction message to create a block of data. In this particular embodiment, the blockchain manager stores the created block of data in a blockchain data structure associated with the UAV.

Abstract: Methods, systems, apparatuses, and computer program products for increasing awareness of an environmental condition for an unmanned aerial vehicle (UAV) are disclosed. In a particular embodiment, a method of increasing awareness of an environmental condition for a UAV includes an environmental awareness controller utilizing data associated with a first UAV to detect an environmental condition at a location associated with the first UAV. In this embodiment, the environmental awareness controller also updates environmental condition information associated with the location to indicate detection of the environmental condition at the location and provides the updated environmental condition information to a device associated with second UAV.

Abstract: Methods, apparatuses, system, devices, and computer program products for unmanned aerial vehicle (UAV) response to object detection are disclosed. In a particular embodiment, a detection response controller receives data generated by one or more sensors of a UAV. Based on the generated data, the detection response controller determines a presence and an identification of an object in a proximity of the UAV. Based on the presence and the identification of the object, the detection response controller determines one or more actions for the UAV to perform.

Abstract: In a particular embodiment, parallel deconfliction processing of unmanned aerial vehicles (UAVs) is disclosed that includes a UAV control system partitioning the airspace over a geographic region into a plurality of airspace regions. In this example embodiment, the UAV control system includes a plurality of deconfliction controllers. For each deconfliction controller in the plurality of deconfliction controllers, the UAV control system assigns to the deconfliction controller, one or more airspace regions of the plurality of airspace regions. The UAV control system also inputs, in parallel, flight path data for a plurality of unmanned aerial vehicles (UAVs) to the plurality of deconfliction controllers. This embodiment also includes processing, in parallel by the plurality of deconfliction controllers, the flight path data to identify a flight conflict between two or more UAVs. Each deconfliction controller outputs a deconfliction result for each airspace region.

Abstract: Methods, systems, apparatuses, and computer program products for automated mission planning and execution for an unmanned aerial vehicle (UAV) are disclosed. In a particular embodiment, automated mission planning and execution for a UAV includes a computing device receiving from a user, mission parameters for planning a mission and identifying, based on the mission parameters, constraints on the mission. In this example embodiment, the computing device determines, based on the identified constraints, a set of pairs of UAVs and flight paths. Each pair includes a flight path and a UAV that is available and capable to perform the flight path in accordance with the mission parameters. The computing device selects from the set of pairs to assign to the mission, a pair of a particular UAV and a particular flight path.

Abstract: Methods, systems, apparatuses, and computer program products for multi-objective mission planning and execution for an unmanned aerial vehicle (UAV) are disclosed. In a particular embodiment, multi-objective mission planning and execution for a UAV includes a computing device detecting a mode change event associated with a UAV executing a mission in a first mission mode. In this embodiment, the computing device determines, based on the mode change event, a second mission mode for the UAV and switches a current mission mode of the UAV from the first mission mode to the second mission mode.

Abstract: Methods, apparatuses, system, devices, and computer program products for updating airspace awareness for unmanned aerial vehicles are disclosed. In a particular embodiment, the classification of a detected object is identified based on sensor data collected by an in-flight unmanned aerial vehicle (UAV). The location of the object is determined based on the sensor data. An airspace awareness controller generates, in dependence upon the classification of the object and the location of the object, an airspace awareness update concerning the object.

Abstract: A personal protection and pathogen disinfection system includes personal protective equipment (“PPE”) configured to cover at least a portion of a person's face when worn by the person, a disinfection device configured to be worn or carried by the person, an input device configured to receive input from the person, and at least one processor configured to selectively activate the disinfection device responsive to the input.

Abstract: In a particular embodiment, recording data associated with an unmanned aerial vehicle (UAV) is disclosed that includes a blockchain manager receiving a transaction message associated with a UAV. The particular embodiment also includes the blockchain manager using the information within the transaction message to create a block of data. In this particular embodiment, the blockchain manager stores the created block of data in a blockchain data structure associated with the UAV.

Abstract: Flight path deconfliction among unmanned aerial vehicles is disclosed. Telemetry data received from an unmanned aerial vehicle (UAV) and air traffic data received from a server or other data sources are analyzed, using deconfliction circuitry, to determine whether a flight path conflict indicative of a potential collision exists. The deconfliction circuitry reroutes the flight path of the UAV to avoid the flight path conflict, and transmits, in dependence upon the rerouted flight path, navigation instructions to the UAV for avoiding the potential collision. The deconfliction circuitry includes hardware-implemented logic optimized for processing the navigation data.

Abstract: Controlling the operation of one or more components in an electric vehicle, including: receiving, from one or more vehicle operation sensors, operation data including sensor data corresponding to a condition of one or more components of the vehicle; determining, using a trained model, whether the one or more components of the vehicle are operating in an acceptable manner; and generating a control signal to adjust operation of the one or more components of the vehicle.

Abstract: Contactless payment from a vehicle, including: configuring the vehicle with payment information associated with a payment mechanism; receiving a request for payment from a payment recipient; requesting, by the vehicle, permission to transmit at least a portion of the payment information associated with the payment mechanism to the payment receiving mechanism; receiving, by the vehicle, permission to transmit at least a portion of the payment information associated with the payment mechanism to the payment receiving mechanism; and transmitting, from the vehicle to a payment receiving mechanism, at least a portion of the payment information associated with a payment mechanism.

Abstract: Flight path deconfliction among unmanned aerial vehicles is disclosed. Telemetry data received from an unmanned aerial vehicle (UAV) and air traffic data received from a server or other data sources are analyzed, using deconfliction circuitry, to determine whether a flight path conflict indicative of a potential collision exists. The deconfliction circuitry reroutes the flight path of the UAV to avoid the flight path conflict, and transmits, in dependence upon the rerouted flight path, navigation instructions to the UAV for avoiding the potential collision. The deconfliction circuitry includes hardware-implemented logic optimized for processing the navigation data.

Abstract: An autonomous underwater device includes one or more receiver arrays. Each receiver array includes a plurality of receiver elements, and each receiver element is configured to generate a signal responsive to detecting sound energy in an aquatic environment. The autonomous underwater device also includes one or more processors coupled to the one or more receiver arrays and configured to receive signals from the receiver elements, to generate input data based on the received signals, and to provide the input data to an on-board machine learning model to generate model output data. The model output data includes sonar image data based on the sound energy, a label associated with the sonar image data, or both.

Abstract: A method includes determining, based at least in part on parameters of a software defined radio (SDR), waveform data descriptive of an electromagnetic waveform. The method also includes obtaining sensor data distinct from the waveform data. The method further includes generating feature data based on the sensor data and the waveform data and providing the feature data as input to a first machine learning model and initiating a response action based on an output of the first machine learning model.

Abstract: A method includes determining, based at least in part on parameters of a software-defined radio (SDR), waveform data descriptive of an electromagnetic waveform. The method also includes generating feature data based on the waveform data and based on one or more symbols decoded from the electromagnetic waveform. The method further includes providing the feature data as input to a first machine-learning model and initiating a response action based on an output of the first machine-learning model.

Abstract: A method includes obtaining route information at an unmanned aerial vehicle (UAV). The route information indicates a plurality of zones between an origin and a destination and a corresponding set of mobile devices for each zone. The method includes receiving first control data at the UAV from a first mobile device while in a first zone of the plurality of zones. The first mobile device is included in a first set of mobile devices corresponding to the first zone. The method further includes receiving second control data at the UAV from a second mobile device while in a second zone of the plurality of zones. The second mobile device is included in a second set of mobile devices corresponding to the second zone.