Verizon Patent and Licensing Inc.

Skyward Io, Inc.

Jonathan Evans

Matthew S Fanelli

Dana Maher

Parvez Ahmad

Amit Gupta

Eric B Miller

Tariq Rashid

Matt Fanelli

Andrew McCollough

Jake Nylund

Chase Fiedler

Derek Wade Ohlarik

Eric T Ringer

Shane Pierce Williams

Warren J Westrup

Joe Cozzarelli

An Aerial Robotics Network (ARN) Management Infrastructure (MI) (also referred to as ARNMI) that provides a mechanism for the management of aerobots.

Aerial robotics network management infrastructure

A device receives network condition information. The network condition information is indicative of network resource availability at a plurality of locations. The device processes the network condition information to associate network resource availability identified in the network condition information with one or more airspace voxels that represent one or more three-dimensional (3D) portions of airspace corresponding to the plurality of locations. The device receives flight parameters relating to a proposed flight plan of an unmanned aerial vehicle (UAV) through airspace represented by a set of airspace voxels, and network performance parameters associated with the proposed flight plan. The device determines that the network resource availability that is associated with the set of airspace voxels fails to satisfy the network performance parameters, and performs one or more actions to enable the UAV to access network resources that satisfy the network performance parameters.

Network resource allocation for unmanned aerial vehicles

A device can be configured to receive flight data from an unmanned aerial vehicle (UAV), where the flight data indicates at least one of an identifier that identifies the UAV, a location of the UAV, an altitude of the UAV, a bearing of the UAV, or a speed of the UAV. The device can be further configured to convert at least a portion of the flight data from a first format to a second format; generate automatic dependent surveillance-broadcast (ADS-B) data based on the converted flight data; and perform an action associated with the ADS-B data.

Providing automatic dependent surveillance-broadcast data for unmanned aerial vehicles

A device can receive obstacle data from a plurality of sources. The obstacle data can include location data associated with obstacles. The device can determine weightings for the obstacles based on the plurality of sources. Each of the weightings can indicate a measure of reliability/accuracy of the information regarding an obstacle. The device can process the obstacle data to associate the obstacles with airspace voxel(s), that represent one or more 3D portions of airspace, based on the location data, receive flight parameters relating to a proposed flight plan of a UAV through airspace represented by a set of airspace voxels, determine whether the set of airspace voxels includes any of the airspace voxel(s), and perform one or more actions to cause a recommendation, regarding the proposed flight plan and based on the determination, to be provided. The recommendation can be based on one or more of the weightings.

Flight planning using obstacle data

A device can be configured to receive flight data associated with an unmanned aerial vehicle (UAV), at least some of the flight data being received from the UAV; store the flight data; receive, from a requesting device, a flight data request, the flight data request including information identifying the UAV or a flight of the UAV; determine response data based on the flight data request and based on an entity associated with the requesting device, the response data including a subset of the flight data, which is available to be accessed by the entity; and perform an action associated with the response data.

Managing unmanned aerial vehicle flight data

Providing automatic dependent surveillance&#x2014;broadcast data for unmanned aerial vehicles

A device can receive information that identifies an airspace voxel that represents a three-dimensional portion of airspace during a particular time period. The device can associate one or more airspace parameters with the airspace voxel. The one or more airspace parameters can represent one or more conditions that relate to flight through the airspace voxel during the particular time period. The device can receive one or more flight parameters regarding a potential flight plan of an aircraft through a plurality of airspace voxels, including the airspace voxel, during the particular time period. The device can analyze the one or more flight parameters and a plurality of airspace parameters, associated with the plurality of airspace voxels, using one or more airspace rules. The device can output a recommendation regarding the potential flight plan based on analyzing the one or more flight parameters and the plurality of airspace parameters.

Flight plan recommendation based on analysis of airspace voxels

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Portland, OR, United States of America

Jonathan Evans