Abstract: 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.

Abstract: 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.

Abstract: A technique for a UAV includes: acquiring an aerial image of an area below a UAV that includes one or more instances of an object; analyzing the aerial image with an image classifier to classify select pixels of the aerial image as being keypoint pixels associated with keypoints of the object; grouping the keypoint pixels into one or more groups each associated with one of the instances of the object, wherein first keypoint pixels of the keypoint pixels are grouped into a first group of the one or more groups associated with a first instance of the one or more instances of the object; generating an estimate of a relative position of the UAV to the first instance of the object based at least upon a machine vision analysis of the first keypoint pixels; and navigating the UAV into alignment with the first instance based upon the estimate.

Abstract: An uncrewed aerial vehicle (UAV) may be configured to hover above a particular charging pad within a portion of a cluster of charging pads for UAVs. The cluster may include the charging pads arranged in a layout and fiducial markers distributed at positions across the layout. While hovering above the particular charging pad, the UAV may capture an aerial image of the portion of the cluster. The UAV may derive cluster-portion observation data from the image, the cluster-portion observation data including information indicating a position of the particular charging pad, and positions of one or more fiducial markers within the portion of the cluster relative to the particular charging pad. The UAV may send the cluster-portion observation data to a computing system in an infrastructure support network for UAVs, and thereafter receive, from the computing system, location information indicating that UAV's geolocation is a geolocation of the particular charging pad.

Abstract: A computing system in an infrastructure support network for uncrewed aerial vehicles (UAVs) may receive, from a UAV, aerial observation data of a ground-based cluster of charging pads for UAVs, the cluster comprising assets including the charging pads arranged in a layout and fiducial markers distributed across the layout. The aerial observation data may comprise position measurements of the UAV at aerial geolocations above the cluster, and vector positions of one or more assets with respect to the aerial geolocations. The computing system may generate a map graph from the aerial observation data, the map graph comprising (i) nodes corresponding to both the aerial geolocations and vector positions, and (ii) edges between pairs of selected nodes, the edges corresponding to distances between selected nodes and including measurement uncertainties. The computing system may generate a spatial map of cluster assets of the cluster by computationally optimizing the map graph.

Abstract: A technique for detection of an obstacle by a UAV includes arriving above a location at a first altitude by the UAV; navigating a descent flight pattern from the first altitude towards the location; acquiring aerial images of the location below the UAV with a camera system disposed onboard the UAV; and analyzing the aerial images with a machine vision system disposed onboard the UAV that is adapted to detect a presence of the obstacle in the aerial images. The descent flight pattern is selected to increase perception by the machine vision system of the obstacle.

Abstract: 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.

Abstract: In an example embodiment, a method carried out by an uncrewed aerial vehicle (UAV) may involve receiving a reference map of a cluster of charging pads from a server. The cluster may include a layout of charging pads and fiducial markers distributed across the layout, the reference map representing the layout and fiducial markers. The UAV may fly to the cluster and acquire an image of charging pads and observed fiducial markers near the charging pads. The image may capture an observed constellation of fiducial markers at apparent positions and orientations relative to the charging pads. A reference constellation of fiducial markers at reference positions and orientations relative to reference charging pads may be identified in the reference map. Identities of the reference charging pads and a match of the reference constellation to the observed constellation may be used to disambiguate a particular charging pad from among the charging pads.

Abstract: A delivery method using curbside payload pickup by a UAV is provided. The method includes providing instructions to cause physical loading of a payload onto an autoloader device for subsequent UAV transport of the payload. A communication signal is received indicating that the autoloader device has been physically loaded with the payload. A UAV from a group of one or more UAVs is selected to pick up the payload from the autoloader device. Instructions are provided to cause the selected UAV to navigate to the autoloader device to pick up the payload and transport the payload to a delivery location.

Abstract: A method includes determining, by an unmanned aerial vehicle (UAV), a position of an autoloader device for the UAV; based on the determined position of the autoloader device, causing the UAV to follow a descent trajectory in which the UAV moves from a starting position to a first nudged position in order to deploy a tethered pickup component of the UAV to a payout position on an approach side of the autoloader device; deploying the tethered pickup component of the UAV to the payout position; causing the UAV to follow a side-step trajectory in which the UAV moves laterally to a second nudged position in order to cause the tethered pickup component of the UAV to engage the autoloader device; and retracting the tethered pickup component of the UAV to pick up a payload from the autoloader device.

Abstract: A method includes determining, by an unmanned aerial vehicle (UAV), a position of an autoloader device for the UAV; based on the determined position of the autoloader device, causing the UAV to follow a descent trajectory in which the UAV moves from a starting position to a first nudged position in order to deploy a tethered pickup component of the UAV to a payout position on an approach side of the autoloader device; deploying the tethered pickup component of the UAV to the payout position; causing the UAV to follow a side-step trajectory in which the UAV moves laterally to a second nudged position in order to cause the tethered pickup component of the UAV to engage the autoloader device; and retracting the tethered pickup component of the UAV to pick up a payload from the autoloader device.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Techniques for performing multiple simultaneous pose generation for an autonomous vehicle. For instance, a system that navigates the autonomous vehicle can include at least a first component that determines first poses for the autonomous vehicle using at least a first portion of sensor data captured by one or more sensors and a second component that determines second poses for the autonomous vehicle using at least a second portion of the sensor data. The first component may have more computational resources than the second component and determine poses at a different frequency than the second component. The system may generate trajectories for the autonomous vehicle using the first poses when the first component is operating correctly. Additionally, the system may generate trajectories for the autonomous vehicle using the second poses when the first component is not operating correctly.

Abstract: A beverage warmer comprises a plurality of product shelves, where each product shelf has a plurality of product lanes. Each product lane has an ambient area, a heating area, and a serving area. A product is loaded into the beverage warmer in the ambient area of a product lane and transported through each of the heating area and serving area in response to a product being removed from the beverage warmer. The product may be maintained in the heating area for a period of time and subject to a first temperature. Upon expiration of the period of time, the product is transported from the heating area to the serving area. The product is maintained in the serving area at a second temperature, where the second temperature is lower than the first temperature.

Abstract: A method for controlling a vehicle based on a prediction from a semantic map is presented. The method includes receiving a snapshot of an environment from one or more sensors. The method also includes generating the semantic map based on the snapshot and predicting an action of a dynamic object in the snapshot based on one or more surrounding objects. The method still further includes controlling an action of the vehicle based on the predicted action.

Abstract: Remote controlling of a vehicle, such as an autonomous vehicle, may sometimes be more efficient and/or reliable. Such control, however, may require processes for ensuring safety of surrounding persons and objects. Aspects of this disclosure include using onboard sensors to detect objects in an environment and alter remote commands according to such objects, e.g. by reducing a maximum permitted velocity of the vehicle as a function of distance to detected objects. In some examples described herein, such remote controlling may be performed by using objects in the environment as control objects, with movements of the control objects resulting in movement of the vehicle.

Abstract: Techniques for performing multiple simultaneous pose generation for an autonomous vehicle. For instance, a system that navigates the autonomous vehicle can include at least a first component that determines first poses for the autonomous vehicle using at least a first portion of sensor data captured by one or more sensors and a second component that determines second poses for the autonomous vehicle using at least a second portion of the sensor data. The first component may have more computational resources than the second component and determine poses at a different frequency than the second component. The system may generate trajectories for the autonomous vehicle using the first poses when the first component is operating correctly. Additionally, the system may generate trajectories for the autonomous vehicle using the second poses when the first component is not operating correctly.

Abstract: A packaged food product processing machine. The machine comprises a food consumer interface configured to receive a food consumer selection identifying an end state of a food product, a package cooling sub-system comprising a chilled fluid bath, a gripper component configured to agitate a package containing the food product in the chilled fluid bath, and a controller configured to command the gripper to control the rate of heat transfer from the package to the chilled fluid bath based on receiving an input identifying an end state selection from the food consumer interface and based on receiving an input containing a value of the physical parameter of the food product from the gripper component.