Wing Aviation LLC

Sierra Nevada Corporation

Damien Bruno Jourdan

Dinuka Abeywardena

Kevin Jenkins

André Prager

Brandon L Jones

David McMartin Howard

Jack Philip Cross

Joseph Henry Hansen, Jr

Justin Michael Bode

Joshua D Gleason

William Edward Brandstetter, Iii

Igor Valigura

Rodney Lee Pickens

Timothy L Sweet

Scott Velez

Anthony Sekine

Richard Roberts

Jesse Blake

James Ryan Burgess

James Schmalzried

Kyle Krafka

Jeremie Gabor

Marco Caflisch

Reia Cho

Brandon Jones

Joseph H Hansen Jr

William Edward Brandstetter Iii

Described is a method that involves operating an unmanned aerial vehicle (UAV) to begin a flight, where the UAV relies on a navigation system to navigate to a destination. During the flight, the method involves operating a camera to capture images of the UAV's environment, and analyzing the images to detect features in the environment. The method also involves establishing a correlation between features detected in different images, and using location information from the navigation system to localize a feature detected in different images. Further, the method involves generating a flight log that includes the localized feature. Also, the method involves detecting a failure involving the navigation system, and responsively operating the camera to capture a post-failure image. The method also involves identifying one or more features in the post-failure image, and determining a location of the UAV based on a relationship between an identified feature and a localized feature.

Backup navigation system for unmanned aerial vehicles

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

Systems and methods for validating a position of an unmanned aerial vehicle (UAV) are provided. A method can include receiving map data for a location, the map data including labeled data for a plurality of landmarks in a vicinity of the location. The method can include generating image data for the location, the image data being derived from images of the vicinity generated by the UAV including at least a subset of the plurality of landmarks. The method can include determining a visual position of the UAV using the image data and the map data. The method can include determining a Global Navigation Satellite System (GNSS) position of the UAV. The method can include generating an error signal using the visual position and the GNSS position. The method can also include validating the GNSS position in accordance with the error signal satisfying a transition condition.

Techniques for validating UAV position using visual localization

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 for automated assignment of a staging pad to an unmanned aerial vehicle (UAV) includes: launching the UAV from a launch location; tracking a drift of the UAV from the launch location; determining a subsequent position of the UAV after the launching based upon geofiducial navigation; calculating an estimated position of the launch location by offsetting the subsequent position by the drift; attempting to match the estimated position to an available staging pad of a plurality of staging pads; and assigning the UAV to the available staging pad when the estimated position successfully matches to the available staging pad.

Automated assignment of UAVs to staging pads

An apparatus for visual navigation of a UAV includes a geo-fiducial mat and a plurality of geo-fiducials. The geo-fiducial mat includes a landing pad region that provides a location for aligning with a landing pad of a UAV. The geo-fiducials each includes a two-dimensional (2D) pattern that visually conveys a code. The 2D pattern has a shape from which a visual navigation system of the UAV can visually triangulate a position of the UAV.

Adhoc geo-fiducial mats for landing UAVs

An imaging system for a moving vehicle aggregates pre-existing data with sensor data to provide an image of the surrounding environment in real-time. The pre-existing data are combined with data from one or more 3-D sensors, and 2-D information from a camera, to create a scene model that is rendered for display. The system accepts data from a 3-D sensor, transforms the data into a 3-D data structure, fuses the pre-existing scene data with the 3-D data structure and 2-D image data from a 2-D sensor to create a combined scene model, and renders the combined scene model for display. The system may also weight aspects of data from first and second sensors to select at least one aspect from the first sensor and another aspect from the second sensor; wherein fusing the pre-existing scene data with the sensor data uses the selected aspect from the first sensor and the selected aspect from the second sensor.

Systems and methods for generating improved environmental displays for vehicles

An apparatus for visual navigation of a UAV includes a geo-fiducial mat and a plurality of geo-fiducials. The geo-fiducial mat includes a landing pad region that provides a location for aligning with a landing pad of a UAV and a survey point. The geo-fiducials are each specified for a unique directional and offset position in or about the landing pad region relative to the survey point. The geo-fiducials each includes a two-dimensional (2D) pattern that visually conveys an alphanumerical code. The 2D pattern has a shape from which a visual navigation system of the UAV can visually triangulate a position of the UAV.

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

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.

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San Jose, CA, United States of America

Damien Bruno Jourdan