Wing Aviation LLC

Kyle Julian

Kevin Jenkins

Ali Shoeb

Kyle Krafka

Marcus Hammond

Christopher Cobar

Louis Kenneth Dressel

Stephen Lacy

Dinuka Abeywardena

Konstantin Bozhkov

Aditya Undurti

Sai Bhargav Yalamanchi

John Mooney

Kyle Kakligian

Scott Barron

Brandon Jones

A method includes receiving a digital surface model of an area for unmanned aerial vehicle (UAV) navigation. The digital surface model represents an environmental surface in the area. The method includes determining, for each grid cell of a plurality of grid cells in the area, a confidence value of an altitude of the environmental surface at the grid cell and determining a terrain clearance value based at least on the confidence value of the altitude of the environmental surface at the grid cell. The method includes determining a route for a UAV through the area such that the altitude of the UAV is above the altitude of the environmental surface at each grid cell of a sequence of grid cells of the route by at least the terrain clearance value determined for the grid cell. The method includes causing the UAV to navigate through the area using the determined route.

Processes for generating and updating flyable airspace for unmanned aerial vehicles

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, during a delivery process of an unmanned aerial vehicle (UAV), receiving, by an image processing system, a depth image captured by a downward-facing stereo camera on the UAV. One or more pixels are within a sample area of the depth image and are associated with corresponding depth values indicative of distances of one or more objects to the downward-facing stereo camera. The method also includes determining, by the image processing system an estimated depth value representative of depth values within the sample area. The method further includes determining that the estimated depth value is below a trigger depth. The method further includes, based at least on determining that the estimated depth value is below the trigger depth, aborting the delivery process of the UAV.

Stereo abort of unmanned aerial vehicle deliveries

In some embodiments, a non-transitory computer-readable medium having logic stored thereon is provided. The logic, in response to execution by one or more processors of an unmanned aerial vehicle (UAV), causes the UAV to perform actions comprising receiving at least one ADS-B message from an intruder aircraft; generating a intruder location prediction based on the at least one ADS-B message; comparing the intruder location prediction to an ownship location prediction to detect conflicts; and in response to detecting a conflict between the intruder location prediction and the ownship location prediction, determining a safe landing location along a planned route for the UAV and descending to land at the safe landing location.

Autonomous control techniques for avoiding collisions with cooperative aircraft

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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Mountain View, CA, United States of America

Kyle Julian