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 computer-implemented method includes obtaining an aerial image representing an object in an environment and providing the aerial image as input to a machine learning model. Based on the aerial image, and using the machine learning model, a textual description of a location of the object in the environment is generated and the textual description of the location of the object is outputted.

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 holding system for uncrewed aerial vehicles (UAVs) includes a first track having a retaining section with a length sufficient to hold a sequence of UAVs and a takeoff section enabling vertical UAV takeoff. The system also includes at least one UAV navigable area configured to receive the UAV from the first track to change the sequence of UAVs prior to the UAVs taking off from the takeoff section.

Abstract: A holding system is described for uncrewed aerial vehicles (UAVs). The holding system includes a landing pad including a support surface for receiving a UAV. The holding system further includes a first track extending from the landing pad, the first track having a length sufficient to hold one or more UAVs. The holding system additionally includes at least one guidance component configured to direct the UAV to laterally navigate along the landing pad onto the first track after landing on the landing pad.

Abstract: A method includes navigating, by a UAV, onto a first track of a UAV holding system, where the first track has a length sufficient to hold a plurality of UAVs. The method also includes determining, by the UAV, a location of the UAV along the first track based on a locating component associated with the UAV holding system. The method also includes performing, by the UAV, a control operation based on the determined location of the UAV along the first track.

Abstract: A method includes causing an aerial vehicle to retract, to within a first distance of the aerial vehicle, a payload that is coupled to the aerial vehicle by a tether. The method also includes causing the aerial vehicle to lower the payload from the aerial vehicle by at least a second distance. The method additionally includes obtaining data indicative of a force applied to the tether by the payload while the payload is lowered from the aerial vehicle, and determining a mass of the payload based on the data indicative of the force applied to the tether by the payload. The method further includes determining, based on the mass of the payload, a maneuver to be performed with the payload by the aerial vehicle, and causing the aerial vehicle to perform the maneuver.

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 computer-implemented method includes obtaining an aerial image representing an object in an environment and providing the aerial image as input to a machine learning model. Based on the aerial image, and using the machine learning model, a textual description of a location of the object in the environment is generated and the textual description of the location of the object is outputted.

Abstract: In some embodiments, a computer-implemented method of communicating sensor data between an autonomous vehicle computing system and a remote computing system is provided. The autonomous vehicle computing system gathers sensor data generated by at least one sensor of the autonomous vehicle computing system. The autonomous vehicle computing system determines an amount of available bandwidth between the autonomous vehicle computing system and the remote computing system. The autonomous vehicle computing system retrieves a local portion of a split machine learning model corresponding to the amount of available bandwidth from a model data store. The autonomous vehicle computing system processes the sensor data using the local portion to produce an intermediate result. The autonomous vehicle computing system transmits the intermediate result to the remote computing system for processing of the intermediate result using a remote portion of the split machine learning model.

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 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 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: 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 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 computer-implemented method includes obtaining an aerial image representing an object in an environment and providing the aerial image as input to a machine learning model. Based on the aerial image, and using the machine learning model, a textual description of a location of the object in the environment is generated and the textual description of the location of the object is outputted.

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: 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 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.