Wing Aviation LLC

Christopher Cobar

Ali Shoeb

Kevin Jenkins

Kyle Krafka

Marcus Hammond

Kyle Julian

Jeremie Gabor

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

In some embodiments, an unmanned aerial vehicle (UAV) is provided. The UAV comprises one or more processors; a camera; one or more propulsion devices; and a computer-readable medium having instructions stored thereon that, in response to execution by the one or more processors, cause the UAV to perform actions comprising: receiving at least one image captured by the camera; generating labels for pixels of the at least one image by providing the at least one image as input to a machine learning model; identifying one or more landing spaces in the at least one image based on the labels; determining a relative position of the UAV with respect to the one or more landing spaces; and transmitting signals to the one or more propulsion devices based on the relative position of the UAV with respect to the one or more landing spaces.

Pixel-by-pixel segmentation of aerial imagery for autonomous vehicle control

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.

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Shoreview, MN, United States of America

Christopher Cobar