Wing Aviation LLC

Martin Kubie

André Prager

Adam Woodworth

Giulia Pantalone

Jesse Blake

James Ryan Burgess

Stephen Lacy

Reinaldo Negron

Eric Teller

James Schmalzried

Dinuka Abeywardena

Jacob Huffman

Ealgoo Kim

Devan Dutta

A method includes receiving configuration data for an unmanned aerial vehicle (UAV) simulation system, the configuration data indicating at least one base location specification, at least one aircraft specification, and at least one virtual vehicle specification and determining an aircraft record comprising, for each of the at least one aircraft to be simulated, aircraft mission data associated with an aircraft identifier of the at least one aircraft to be simulated. The method further includes configuring the UAV simulation system so that each of the at least one aircraft has a corresponding base location as specified by the at least one base location specification and a corresponding vehicle software version as specified by the at least one virtual vehicle specification and executing a simulation of the at least one aircraft carrying out flying missions by using the configured UAV simulation system and updating the aircraft mission data in the aircraft record.

Backend automation system for simulation of drone deliveries through virtual fleets

An unmanned aerial vehicle (UAV) includes a propulsion system, a global navigation satellite system (GNSS) sensor, a camera and a controller. The controller includes logic that, in response to execution by the controller, causes the UAV to in response to detecting a loss of tracking by the GNSS sensor determine an estimated location of the UAV on a map based on a location image captured by the camera, determine a route to a destination using tracking parameters embedded in the map, wherein the map is divided into a plurality of sections and the tracking parameters indicate an ease of determining a location of the UAV using images captured by the camera with respect to each section, and control the propulsion system to cause the UAV to follow the route to the destination.

Map including data for routing aerial vehicles during GNSS failure

Techniques are provided to improve routing of autonomous vehicles through highly congested areas. In some embodiments, routes that include sequences of timed space reservations are provided to autonomous vehicles by a route reservation system. In some embodiments, the route reservation system detects route alteration states (including but not limited to an arrival of an autonomous vehicle at a waiting area), determines a new route for the autonomous vehicle that passes through the highly congested area, and transmits the new route to the autonomous vehicle for navigating from the waiting area to an endpoint.

Real-time optimization of autonomous vehicle routes

A computer implemented method of distributing noise exposures to unmanned aerial vehicles (UAVs) over a neighborhood includes: accessing a noise exposure map stored in a database and generating a new flight path over the neighborhood for a first UAV of the UAVs based at least in part on the noise exposure map. The noise exposure map includes noise exposure values indexed to locations within the neighborhood. Each of the noise exposure values quantifies a cumulative noise exposure of a corresponding one of the locations due at least in part to historical flight paths of the UAVs over the neighborhood.

Mitigating noise exposure to unmanned aerial vehicles

A technique of controlling tonal noises produced by an unmanned aerial vehicle (UAV) includes generating thrust with a plurality of rotor units mounted to the UAV to propel the UAV into flight. Each of the rotor units includes a bladed rotor. A rotation rate or a phase delay of at least one of the rotor units is adjusted relative to another of the rotor units. The adjustment causes a spread in the tonal noises generated by the rotor units.

Multi-rotor tonal noise control for UAV

An unmanned aerial vehicle (UAV) includes one or more sources of propulsion, a power source, and communication system. The UAV also includes a controller coupled to the communication system, the power source, and the one or more sources of propulsion. The controller includes logic that when executed by the controller causes the UAV to perform operations, including measuring a power source charge level of the UAV; sending a signal including the power source charge level of the UAV to an external device; receiving movement instructions from the external device; and engaging the one or more sources of propulsion to move the UAV from a first location on a storage rack to a second location within a storage facility.

Unmanned aerial vehicle fleet management

A computer implemented method of distributing noise exposures to unmanned aerial vehicles (UAVs) over a neighborhood includes: receiving flight routing requests to fly the UAVs over the neighborhood; accessing a noise exposure map stored in a noise exposure database in response to the flight routing requests; and generating new flight paths for the UAVs over the neighborhood that load level additional noise exposures that the new flight paths will contribute to the noise exposure map. The noise exposure map includes noise exposure values indexed to properties within the neighborhood. The noise exposure values quantify cumulative noise exposures of the properties due to historical flight paths of the UAVs over the neighborhood.

Growing community of inventors

San Francisco, CA, United States of America

Martin Kubie