Wing Aviation LLC

Kyle Krafka

Ali Shoeb

Kevin Jenkins

Marcus Hammond

Kyle Julian

Christopher Cobar

Dinuka Abeywardena

Trevor Shannon

Jesse Blake

James Ryan Burgess

James Schmalzried

Damien Bruno Jourdan

Brandon L Jones

Domitille Commun

Linhao Jin

Marco Caflisch

Xinzhi Fan

Konstantin Bozhkov

Brandon Jones

A method of operation of an unmanned aerial vehicle (UAV) service includes acquiring aerial images of a scene at an area of interest (AOI), wherein the aerial images are acquired with a UAV of the UAV service during a flight mission of the UAV that passes over the AOI; uploading a mission log of the flight mission to a backend data system of the UAV service, the mission log including image data that includes, or is derived from, at least a portion of the aerial images; and training a neural radiance field (NeRF) model with one or more of the aerial images, wherein the NeRF model comprises a neural network, which after the training, encodes a volumetric representation of the scene capable of generating novel views of the scene different than any of the aerial images used to train the NeRF model.

Using NeRF models to facilitate operations of a UAV delivery service

Systems, apparatus, and methods are presented for testing a device. One method includes activating an actuator device to cause a carriage, coupled to a device, to be moved in one or more directions along a guide rail, wherein the device includes at least one processing device and one or more sensor devices. The method may also comprise receiving, by the device, one or more input commands and executing, by the device based on the one or more input commands, a software application to generate an output while the device is moving in the one or more directions. Further, the method may comprise verifying the execution of the software application on the device based on the output.

Systems, methods, and apparatus for testing UAV devices

A method includes capturing, by a sensor on an unmanned aerial vehicle (UAV), an image of a delivery location. The method also includes determining, based on the image of the delivery location, a segmentation image. The segmentation image segments the delivery location into a plurality of pixel areas with corresponding semantic classifications. The method additionally includes determining, based on the segmentation image, a percentage of obstacle pixels within a surrounding area of a delivery point at the delivery location, wherein each obstacle pixel has a semantic classification indicative of an obstacle in the delivery location. The method further includes based on the percentage of obstacle pixels being above a threshold percentage, aborting a delivery process of the UAV.

Semantic abort of unmanned aerial vehicle deliveries

A method includes capturing, by a sensor on an unmanned aerial vehicle (UAV), an image of a delivery location. The method further includes determining, based on the image of the delivery location, a segmentation image. The segmentation image segments the delivery location into a plurality of pixel areas with corresponding semantic classifications. The method also includes determining, based on the segmentation image, a distance-to-obstacle image of a delivery zone at the delivery location. The distance-to-obstacle image comprises a plurality of pixels, each pixel representing a distance in the segmentation image from a nearest pixel area with a semantic classification indicative of an obstacle in the delivery location. Additionally, the method includes selecting, based on the distance-to-obstacle image, a delivery point in the delivery zone. The method also includes positioning the UAV above the delivery point in the delivery zone for delivery of a payload.

Semantic adjustment of unmanned aerial vehicle delivery points

A method includes navigating, by an unmanned aerial vehicle (UAV), to a first altitude above a first delivery point at a delivery location. The method further includes determining, by the UAV, a second delivery point at the delivery location. The method includes navigating, by the UAV, through a descending trajectory to move the UAV from the first altitude above the first delivery point to a second altitude above the second delivery point at the delivery location. The second altitude is lower than the first altitude. The method additionally includes delivering, by the UAV, a payload to the second delivery point at the delivery location. The method includes after delivering the payload, navigating, by the UAV, through an ascending trajectory to move the UAV from a third altitude above the second delivery point to a fourth altitude above the first delivery point. The fourth altitude is higher than the third altitude.

Unmanned aerial vehicle trajectories for nudging and un-nudging

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

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Los Altos, CA, United States of America

Kyle Krafka