Wing Aviation LLC

Brandon L Jones

Damien Bruno Jourdan

André Prager

Kevin Jenkins

James Schmalzried

Richard Roberts

Shirley Kozler

Jesse Blake

James Ryan Burgess

Stephen Lacy

Jonathan Lesser

Dinuka Abeywardena

Kyle Krafka

John FitzSimons

Steven Dodson

Krzysztof Choromanski

Hakim Sidahmed

Vikas Sindhwani

Aditya Undurti

Benjamin Jacobs

Marco Caflisch

Unmanned aerial vehicle (UAV) navigation systems include a UAV charging pad positioned at a storage facility, a plurality of fiducial markers positioned at the storage facility, and a UAV. Each of the fiducial markers is associated with a fiducial dataset storing a position of the corresponding fiducial marker, and the fiducial datasets are stored in a fiducial map. The UAV includes a camera and logic that when executed causes the UAV to image a first fiducial marker, to access from the fiducial map a first fiducial dataset storing the position of the first fiducial marker, and to navigate based upon the first fiducial dataset.

Geo-fiducials for UAV navigation

A method includes determining an operational condition associated with an unmanned aerial vehicle (UAV). The method includes, responsive to determining the operational condition, causing the UAV to perform a pre-flight check. The pre-flight check includes hovering the UAV above a takeoff location. The pre-flight check includes, while hovering the UAV, moving one or more controllable components of the UAV in accordance with a predetermined sequence of movements. The pre-flight check includes obtaining, by one or more sensors of the UAV, sensor data indicative of a flight response of the UAV to moving the one or more controllable components while hovering the UAV. The pre-flight check includes comparing the sensor data to expected sensor data associated with an expected flight response to the predetermined sequence of movements while hovering the UAV. The pre-flight check includes, based on comparing the sensor data to the expected sensor data, evaluating performance of the UAV.

Systems and methods for autonomous airworthiness pre-flight checks for UAVs

A technique for operating unmanned aerial vehicles (UAVs) in a terminal area from which the UAVs are staged includes charging a plurality of the UAVs on charging pads disposed in a staging array at the terminal area. Merchant facilities for preparing packages for delivery by the UAVs are disposed about a periphery of the staging array. The UAVs are relocated under their own propulsion from interior charging pads to peripheral loading pads of the staging array as the peripheral loading pads become available and the UAVs are deemed sufficiently charged and ready for delivery missions.

Terminal area operation of UAVs for package delivery system

In some embodiments, techniques are provided for verifying operability of an automatic dependent surveillance-broadcast (ADS-B) receiver included in a first unmanned aerial vehicle (UAV), which includes receiving ADS-B data representative of ADS-B messages broadcast by traffic within a reception range of the ADS-B receiver during a first period of time, estimating a traffic environment for a service area spanning, at least in part, a first operating area of the first UAV during the first period of time, determining an expected observed traffic of the first UAV during the first period of time based on the estimated traffic environment, and verifying operability of the ADS-B receiver of the first UAV based on a comparison between the expected observed traffic of the first UAV and the traffic associated with the ADS-B data received by the ADS-B receiver of the first UAV.

Verification of unmanned aerial vehicle ADS-B receiver operability

In some embodiments, techniques are provided for analyzing time series data to detect anomalies. In some embodiments, the time series data is processed using a machine learning model. In some embodiments, the machine learning model is trained in an unsupervised manner on large amounts of previous time series data, thus allowing highly accurate models to be created from novel data. In some embodiments, training of the machine learning model alternates between a fitting optimization and a trimming optimization to allow large amounts of training data that includes untagged anomalous records to be processed. Because a machine learning model is used, anomalies can be detected within complex systems, including but not limited to autonomous vehicles such as unmanned aerial vehicles. When anomalies are detected, commands can be transmitted to the monitored system (such as an autonomous vehicle) to respond to the anomaly.

Unsupervised anomaly detection for autonomous vehicles

A technique for controlling an unmanned aerial vehicle (UAV) includes monitoring a sensed airspeed of the UAV, obtaining a commanded speed for the UAV, wherein the commanded speed representing a command to fly the UAV at a given speed relative to an airmass or to Earth, and when the commanded speed is greater than the sensed airspeed, using the commanded speed in lieu of the sensed airspeed to inform flight control decisions of the UAV.

Contingent use of commanded speed in lieu of sensed airspeed to inform flight control decisions

A technique for validating a balcony to receive delivery of a parcel via a UAV includes obtaining a first identification of a general location of the balcony; generating a first image representing a building including the balcony where the first image is selected based upon the location identified; obtaining a second identification or a confirmation of a precise location of the balcony in the building where the second identification or the confirmation are received in response to an end-user interaction with the first image; determining a deliverability score based at least in part on the precise location of the balcony; and indicating an enrollment status to the end-user where the enrollment status is generated based upon the deliverability score.

UAV balcony deliveries to multi-level buildings

A method is provided that includes causing an operational member of a system to move. The method includes driving a power or control signal through a conductive coupling member. The conductive coupling member is connected between a first terminal and a second terminal in a power circuit, and the coupling member secures the operational member to a structural member of the system. The method includes detecting an electrical disconnect between the first terminal and a second terminal. The method includes determining a mechanical break associated with the coupling member based on the electrical disconnect between the first terminal and the second terminal. The method includes causing the operational member of the system to stop moving based on determining the mechanical break associated with the coupling member.

Systems and methods for stopping movement of operational members

Unmanned aerial vehicle (UAV) navigation systems include a UAV charging pad positioned at a storage facility, a plurality of fiducial markers positioned at the storage facility, and a UAV. Each fiducial marker is associated with a fiducial dataset storing a position of the fiducial marker, and each fiducial dataset is stored in a fiducial map. The UAV has a navigation system that includes a camera, a fiducial navigation sub-system, a non-fiducial navigation sub-system, and logic that when executed causes the UAV to image a first fiducial marker with the camera, to transition from a non-fiducial navigation mode to a fiducial navigation mode, to access from the fiducial map the fiducial dataset storing the position of the first fiducial marker, and to navigate based upon the fiducial dataset storing the position of the first fiducial marker, into alignment with and land on the UAV charging pad.

Growing community of inventors

Redwood City, CA, United States of America

Brandon L Jones