A method for perception validation of an aerial vehicle includes: acquiring an image of a ground area with an onboard camera system of the aerial vehicle, generating an above ground altitude (AGL) estimate with a neural network trained to output the AGL estimate in response to the image fed as an input to the neural network, generating a motion estimate or a position estimate based upon sensor data output from a sensor disposed onboard the aerial vehicle, and cross-validating the motion or position estimate against the AGL estimate.
G08G 5/74 - Dispositions pour la surveillance des situations ou des conditions liées au trafic pour la surveillance du terrain
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
G08G 5/55 - Aides à la navigation ou au guidage pour un seul aéronef
G08G 5/57 - Aides à la navigation ou au guidage pour les aéronefs sans pilote
2.
Training uav neural networks for operation in new geographical regions
A technique for provisioning UAVs to operate in a specific geographical region includes acquiring reference aerial images of the specific geographical region from a secondary source. The reference aerial images have at least one style characteristic that is distinct from that of operational aerial images acquired by the UAVs during operation. The reference aerial images are transformed into training images that adopt a style of the operational aerial images using a neural style transfer. A neural network that facilitates vision-based navigation of the UAVs is trained using the training images. The neural network is trained to operate in the specific geographical region prior to operation in the specific geographical region.
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
G06V 20/70 - Étiquetage du contenu de scène, p. ex. en tirant des représentations syntaxiques ou sémantiques
3.
USING ASSET MAPS TO INFORM REAL-TIME MACHINE LEARNING MODELS FOR UAV NAVIGATION
A technique for informing navigation of a UAV includes storing an asset map of a ground area, wherein the asset map includes a reference aerial image of the ground area annotated with labels describing reference objects depicted in the reference aerial image; acquiring a current aerial image of the ground area with an onboard camera system of the UAV while the UAV is flying above the ground area; mapping correspondences between the reference aerial image and the current aerial image using a homography estimating tool executing onboard the UAV; analyzing the current aerial image with an object detection model to detect a first object positioned at the ground area; and validating or informing a detection of the first object by the object detection model based on the mapping of the correspondences.
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.
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.
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
6.
Generating Aerial Paths Based on Properties of Aerial Image Data
A method includes receiving an input specifying a starting location and a destination location for an aerial vehicle. The method additionally includes determining, based on the starting location and the destination location, an aerial path for the aerial vehicle to follow from the starting location to the destination location. The method also includes determining, based on the aerial path, a property of aerial image data, where the aerial image data is obtainable using the aerial vehicle while traversing the aerial path, and where the aerial image data represents an environment along the aerial path. The method further includes determining, based on the property, a path score associated with the aerial path, and outputting the aerial path based on the path score.
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
G05D 1/243 - Moyens de capture de signaux provenant naturellement de l’environnement, p. ex. signaux optiques, acoustiques, gravitationnels ou magnétiques ambiants
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
An unmanned aerial vehicle (UAV) includes a fuselage including a front, a rear, a top, a bottom, and an open cargo bay with a lower access opening in the bottom of the fuselage. An aerodynamic device is positioned on a surface of the bottom of the fuselage in front of the open cargo bay. A coupler is configured to secure a payload at the top of the open cargo bay.
B64U 101/60 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes
8.
USING NeRF MODELS TO FACILITATE OPERATIONS OF A UAV DELIVERY SERVICE
A technique for maintaining a terrain model includes: acquiring aerial images of a scene at an area of interest (AOI), wherein the aerial images are acquired by a UAV during a flight mission of the UAV that passes over the AOI; training a ML model onboard the UAV with one or more of the aerial images, wherein the ML model comprises a neural network, which after the training, encodes a volumetric representation of the scene; determining whether the terrain model of the AOI is deemed out-of-date based upon whether the training results in greater than a threshold change in the ML model; and uploading image data acquired by the UAV during the flight mission to a backend data system in response to determining that the terrain model is deemed out-of-date, wherein the image data includes, or is derived from, at least a portion of the aerial images.
A technique for localizing utility lines includes capturing aerial images of a ground area below a UAV; recording a position of the UAV when capturing the aerial images; detecting a presence of an object in the aerial images suspected to be a utility line; identifying two offset pixel points in each of the aerial images that coincide with the object in each aerial image; converting the two offset pixel points in each of the aerial images to a world frame; defining a plurality of geometric planes in the world frame each corresponding to one the aerial images, wherein the each of the geometric planes is defined by the position of the UAV when capturing a corresponding to one of the aerial images and the two offset pixel points in the world frame corresponding to the one of the aerial images; and determining an intersection approximation of the geometric planes.
A technique for localizing utility lines includes capturing aerial images of a ground area below a UAV; recording a position of the UAV when capturing the aerial images; detecting a presence of an object in the aerial images suspected to be a utility line; identifying two offset pixel points in each of the aerial images that coincide with the object in each aerial image; converting the two offset pixel points in each of the aerial images to a world frame; defining a plurality of geometric planes in the world frame each corresponding to one the aerial images, wherein the each of the geometric planes is defined by the position of the UAV when capturing a corresponding to one of the aerial images and the two offset pixel points in the world frame corresponding to the one of the aerial images; and determining an intersection approximation of the geometric planes.
An unmanned aerial vehicle fleet management system initiates creation of a digital airspace area of operation, presents in a user interface an area classifier selection field configured to select from a set of available area classifiers; receives first user input classifying the digital airspace area of operation as an oversight area; presents a ruleset selection field; receives second user input specifying one or more rulesets of the oversight area; creates the oversight area based at least in part on the first and second inputs; and creates a pilot area associated with the oversight area that inherits the specified ruleset(s) of the oversight area. The oversight area and pilot area define areas of operation of different types for different classes of users, such as pilots and flight operations managers. The specified ruleset(s) comprise rules related to flight approvals. Flight missions can be created or altered based on the specified ruleset(s).
A joiner includes a first and second portions configured to mount to respective first and second structural members. Each of the first and second portions includes a body with a receiving surface to receive the respective structural member. The first portion includes a group of first coupling structures disposed on the first body and the second portion includes a group of second coupling structures disposed on the second body. The first coupling structures are configured to mate with the second coupling structures such that engagement surfaces of the first coupling structures engage engagement surfaces of the second coupling structures so as to transfer loads between the second portion and first portion and to limit relative movement between the first portion and second portion with respect to the first direction and with respect to a vertical direction.
B64U 30/29 - Caractéristiques de construction des rotors ou des supports de rotorLeurs agencements
B64U 101/64 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis
A method includes securing a payload on a payload retriever, where the payload retriever is attached to a tether that extends from an uncrewed aerial vehicle (UAV). The method also includes retracting the tether to position the payload retriever at a first location, and securing the payload to a releasable coupler of the UAV. The method also includes further retracting the tether to move the payload retriever from the first location and disengage the payload retriever from the payload. Further, the method includes actuating the releasable coupler to release the payload from the UAV.
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
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.
G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p. ex. utilisant des pilotes automatiques
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64U 101/30 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’imagerie, à la photographie ou à la vidéographie
B64U 101/66 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis au retrait de colis
In one aspect an uncrewed aerial vehicle (UAV) is provided. The uncrewed aerial vehicle includes a fuselage and a drag reduction device. The fuselage has a front end, a rear end, a top, and a bottom. The drag reduction device includes a proximal end and a distal end. The proximal end of the drag reduction device is coupled to the bottom of the fuselage. The drag reduction device is rotatable between a rest position and an active position in which the drag reduction device extends downward. A standoff is disposed on a rear side of the drag reduction device and is configured to engage a payload secured under the fuselage and hold the drag reduction device at a distance from the payload when the drag reduction device is in the active position.
B64C 21/08 - Moyens permettant d'influencer l'écoulement d'air sur les surfaces des aéronefs en agissant sur la couche limite par utilisation de fentes, de conduits, de surfaces poreuses ou de dispositifs similaires réglables
B64C 7/00 - Structures ou carénages non prévus ailleurs
B64U 101/64 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis
16.
UAV Flight Control Operations For Predicted Traffic Encounter
A method is disclosed. The method includes receiving an indication of presence of an aircraft in a vicinity of an uncrewed aerial vehicle (UAV) which is flying along a flight path. The method also includes decelerating, based on the received indication, the UAV to reduce a ground speed along the flight path. The method additionally includes descending, after reducing the ground speed, the UAV to a hover position. The method further includes determining, while the UAV is in the hover position, whether to resume the flight path or to land the UAV based on a determination of continued presence of the aircraft in the vicinity of the UAV. The method also includes controlling the UAV based on the determination of whether to resume the flight path or to land the UAV.
An uncrewed aerial vehicle (UAV) which includes a payload coupling apparatus secured to a tether. The payload coupling apparatus is configured to engage a payload. The UAV also includes a winch system configured to extend the tether for lowering the payload on the payload coupling apparatus during a delivery. The UAV also includes a payload latch movable between an open position and a closed position, wherein the payload latch is configured to secure the payload at the UAV when in the closed position and release the payload when in the open position.
B64U 50/38 - Chargement en période hors vol par transmission sans fil
B64U 101/30 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’imagerie, à la photographie ou à la vidéographie
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
A technique for managing an unplanned contingency landing of a unmanned aerial vehicle (UAV) includes determining by the UAV that the unplanned contingency landing is imminent, capturing aerial images of a ground area below the UAV with an onboard camera system as the UAV descends towards the ground area, semantically analyzing the aerial images to classify objects at the ground area into object classifications, depth analyzing the aerial images to determine above ground level (AGL) heights associated with each of the objects at the ground area, selecting a preferred landing site for the unplanned contingency landing that is coincident with one of the objects at the ground area based upon a contingency landing policy that ranks contingency landing sites based upon a combination of the object classifications and the AGL heights; and nudging the UAV towards the preferred landing site as the UAV descends towards the ground.
G06T 7/50 - Récupération de la profondeur ou de la forme
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
19.
Payload Retriever Having Multiple Slots For Use with a UAV
A payload coupling apparatus having a housing comprising an outer surface extending around a perimeter of the housing, an upper portion above the outer surface and including a tether attachment point, and a lower portion below the outer surface; a first slot extending into the outer surface of the housing thereby forming a first lower lip on the housing beneath the first slot; wherein the first slot is adapted to receive a handle of a payload; and a second slot extending into the outer surface of the housing thereby forming a second lower lip on the housing beneath the second slot; wherein the second slot is adapted to receive the handle of the payload.
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64F 1/32 - Installations au sol ou installations pour ponts d'envol des porte-avions pour la manutention du fret
B64U 101/66 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis au retrait de colis
A technique for managing an unplanned contingency landing of a unmanned aerial vehicle (UAV) includes determining by the UAV that the unplanned contingency landing is imminent, capturing aerial images of a ground area below the UAV with an onboard camera system as the UAV descends towards the ground area, semantically analyzing the aerial images to classify objects at the ground area into object classifications, depth analyzing the aerial images to determine above ground level (AGL) heights associated with each of the objects at the ground area, selecting a preferred landing site for the unplanned contingency landing that is coincident with one of the objects at the ground area based upon a contingency landing policy that ranks contingency landing sites based upon a combination of the object classifications and the AGL heights; and nudging the UAV towards the preferred landing site as the UAV descends towards the ground.
A method includes charging a battery of a vehicle to a charge threshold voltage. The method also includes discharging the battery from the charge threshold voltage to a post-task voltage by performing a travel task using the vehicle. The method additionally includes determining that a battery calibration condition has been met. The method further includes, based on determining that the battery calibration condition has been met, discharging the battery from the post-task voltage to a discharge threshold voltage by performing a battery discharge task. The method yet further includes determining a capacity of the battery based on a first electrical output of the battery during the travel task and a second electrical output of the battery during the battery discharge task.
B60L 58/10 - Procédés ou agencements de circuits pour surveiller ou commander des batteries ou des piles à combustible, spécialement adaptés pour des véhicules électriques pour la surveillance et la commande des batteries
B64U 50/30 - Alimentation en énergie électrique ou distribution de celle-ci
A technique for localization of an unmanned aerial vehicle (UAV) includes: acquiring aerial images of a terrain below the UAV with an onboard camera system of the UAV while the UAV is flying a mission along a preplanned route over the terrain; generating a current optical flow map based upon image pixel motion between consecutive images in a sequence of the aerial images; comparing the current optical flow map to reference optical flow maps stored onboard the UAV, wherein the reference optical flow maps are precomputed from a model of the terrain along the preplanned route; and determining a position in at least two lateral dimensions based on the comparing.
G05D 1/246 - Dispositions pour déterminer la position ou l’orientation utilisant des cartes d’environnement, p. ex. localisation et cartographie simultanées [SLAM]
A payload retrieval apparatus including a stand having an upper end and a lower end, a channel having a first end and a second end, the channel coupled to the stand, a first extension that extends in a first direction from the first end of the channel, wherein the first extension is configured to direct a tether extending from a UAV and a payload retriever attached to an end of the tether toward the first end of the channel, wherein the second end of the channel has a payload engaging member positioned near the second end of the channel that is adapted to secure a payload, and wherein the payload retrieval apparatus is configured to cause the UAV to pick up the payload with the payload retriever while maintaining flying.
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64C 39/02 - Aéronefs non prévus ailleurs caractérisés par un emploi spécial
B64F 1/32 - Installations au sol ou installations pour ponts d'envol des porte-avions pour la manutention du fret
B64U 101/66 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis au retrait de colis
B66C 1/04 - Éléments ou dispositifs de prise de la charge adjoints aux mécanismes de levage, de descente ou de halage, ou adaptés pour être utilisés avec ces mécanismes et transmettant les efforts à des articles ou à des groupes d'articles par moyens magnétiques
A method for unmanned aerial vehicle (UAV) mission planning includes acquiring a target aerial image of a geographic area representative of the geographic area illuminated by one or more artificial light sources, identifying a location of the one or more artificial light sources based on the target aerial image, rendering a simulated aerial image representative of the geographic area illuminated by the one or more artificial light sources at night using a digital surface model of the geographic area, the location of the one or more artificial light sources, and an irradiance parameter for the one or more artificial light sources, identifying one or more regions within the geographic area as having sufficient lighting for UAV operation at night based on the simulated aerial image, and generating a mission plan for the UAV based on the one or more regions within the geographic area.
G05D 1/243 - Moyens de capture de signaux provenant naturellement de l’environnement, p. ex. signaux optiques, acoustiques, gravitationnels ou magnétiques ambiants
G05D 1/46 - Commande de la position ou du cap dans les trois dimensions
G05D 1/245 - Dispositions pour déterminer la position ou l’orientation utilisant la navigation à l’estime
G05D 1/246 - Dispositions pour déterminer la position ou l’orientation utilisant des cartes d’environnement, p. ex. localisation et cartographie simultanées [SLAM]
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
B64U 20/87 - Montage des dispositifs d’imagerie, p. ex. montage des suspensions à cardan
G05D 105/28 - Applications spécifiques des véhicules commandés pour le transport de marchandises
A method for unmanned aerial vehicle (UAV) mission planning includes acquiring a target aerial image of a geographic area representative of the geographic area illuminated by one or more artificial light sources, identifying a location of the one or more artificial light sources based on the target aerial image, rendering a simulated aerial image representative of the geographic area illuminated by the one or more artificial light sources at night using a digital surface model of the geographic area, the location of the one or more artificial light sources, and an irradiance parameter for the one or more artificial light sources, identifying one or more regions within the geographic area as having sufficient lighting for UAV operation at night based on the simulated aerial image, and generating a mission plan for the UAV based on the one or more regions within the geographic area.
G05D 1/246 - Dispositions pour déterminer la position ou l’orientation utilisant des cartes d’environnement, p. ex. localisation et cartographie simultanées [SLAM]
G05D 1/646 - Suivi d’une trajectoire prédéfinie, p. ex. d’une ligne marquée sur le sol ou d’une trajectoire de vol
G05D 1/667 - Livraison ou récupération de charges utiles
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06V 10/60 - Extraction de caractéristiques d’images ou de vidéos relative aux propriétés luminescentes, p. ex. utilisant un modèle de réflectance ou d’éclairage
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
A method includes determining a portion of a flight path of an aerial vehicle. The method also includes determining an attribute value representing an operating condition expected to be experienced by the aerial vehicle at the portion of the flight path. The method additionally includes determining, based on the attribute value and using a non-linear model, a power value representing an amount of power expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method further includes determining, based on the power value, an energy value representing an amount of energy expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method yet further includes determining the flight path based on the energy value.
A method includes determining a portion of a flight path of an aerial vehicle. The method also includes determining an attribute value representing an operating condition expected to be experienced by the aerial vehicle at the portion of the flight path. The method additionally includes determining, based on the attribute value and using a non-linear model, a power value representing an amount of power expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method further includes determining, based on the power value, an energy value representing an amount of energy expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method yet further includes determining the flight path based on the energy value.
G05D 1/644 - Optimisation des paramètres de parcours, p. ex. consommation d’énergie, réduction du temps de parcours ou de la distance
G05D 101/15 - Détails des architectures logicielles ou matérielles utilisées pour la commande de la position utilisant des techniques d’intelligence artificielle [IA] utilisant l’apprentissage automatique, p. ex. les réseaux neuronaux
A technique for processing delivery aborts by a UAV delivery service includes: acquiring a delivery zone (DZ) image of a delivery destination including a DZ for a package being delivered to the delivery destination by a UAV; determining to abort a delivery mission for the package based upon the DZ image; converting the DZ image to a vector embedding; performing a similarity search on a vector database using the vector embedding, the vector database storing reference images of other delivery destinations indexed to reference vector embeddings and outcome attributes describing delivery outcomes associated with the reference images, and wherein the similarity search identifies a subset of the reference images deemed to have a threshold similarity to the DZ image; and prompting a vision language model with the DZ image and the subset of the reference images to provide an abort explanation or to determine a delivery disposition for the DZ.
In one aspect an uncrewed aerial vehicle (UAV) is provided. The uncrewed aerial vehicle includes a fuselage and a drag reduction device. The fuselage has a front end, a rear end, a top, and a bottom. The drag reduction device includes a proximal end and a distal end. The proximal end of the drag reduction device is coupled to the bottom of the fuselage. The drag reduction device is rotatable between a rest position and an active position in which the drag reduction device extends downward. A standoff is disposed on a rear side of the drag reduction device and is configured to engage a payload secured under the fuselage and hold the drag reduction device at a distance from the payload when the drag reduction device is in the active position.
B64C 7/00 - Structures ou carénages non prévus ailleurs
B64C 21/08 - Moyens permettant d'influencer l'écoulement d'air sur les surfaces des aéronefs en agissant sur la couche limite par utilisation de fentes, de conduits, de surfaces poreuses ou de dispositifs similaires réglables
B64U 101/64 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis
A technique for processing delivery aborts by a UAV delivery service includes: acquiring a delivery zone (DZ) image of a delivery destination including a DZ for a package being delivered to the delivery destination by a UAV; determining to abort a delivery mission for the package based upon the DZ image; converting the DZ image to a vector embedding; performing a similarity search on a vector database using the vector embedding, the vector database storing reference images of other delivery destinations indexed to reference vector embeddings and outcome attributes describing delivery outcomes associated with the reference images, and wherein the similarity search identifies a subset of the reference images deemed to have a threshold similarity to the DZ image; and prompting a vision language model with the DZ image and the subset of the reference images to provide an abort explanation or to determine a delivery disposition for the DZ.
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
31.
Dynamic UAV Transport Tasks for Pickup and Delivery of Packages
Example implementations relate to a method of dynamically updating a transport task of a UAV. The method includes receiving, at a transport-provider computing system, an item provider request for transportation of a plurality of packages from a loading location at a given future time. The method also includes assigning, by the transport-provider computing system, a respective transport task to each of a plurality of UAVs, where the respective transport task comprises an instruction to deploy to the loading location to pick up one or more of the plurality of packages. Further, the method includes identifying, by the transport-provider system, a first package while or after a first UAV picks up the first package. Yet further, the method includes based on the identifying of the first package, providing, by the transport-provider system, a task update to the first UAV to update the respective transport task of the first UAV.
B64U 101/60 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
32.
Drag reduction device for externally carried payloads on aircraft
An uncrewed aerial vehicle (UAV) includes a fuselage extending along a first direction from a rear end to a front end. The fuselage has a cross-sectional area at an intermediate position between the front and rear end. An external payload storage area is positioned at the intermediate position and is configured to receive a payload that is secured to the fuselage and that extends laterally outward from the cross-sectional area of the fuselage. A drag reduction device is coupled to the fuselage. The drag reduction device has a length extending from a free end to an attached end that is secured to the fuselage, a width that extends perpendicular to the first direction of the fuselage, and a depth. In an operating position, the drag reduction device is positioned in front of the payload storage area, is spaced from the payload storage area, and extends outward from the fuselage.
An uncrewed aerial vehicle (UAV) includes a support extending along a flight direction of the UAV. The support includes an elongate structural member, a cap coupled to a front end of the elongate structural member, and an energy absorber. The support extends along an axis that runs through the support from a front end to a rear end. The cap is coupled to the front end of the elongate structural member. The cap includes a support platform that has a front surface opposite the elongate structural member and that extends outward from the axis. The energy absorber is disposed on the front surface of the support platform and includes a crushable material configured to absorb energy under impact. The UAV also includes a first propeller unit coupled to the support.
A UAV having a fuselage body including a cavity that forms a cargo bay for transporting a payload, and a lower access opening providing an exit for the payload from the cargo bay, the lower access opening including a lower cargo bay door, an actuator positioned in the fuselage body, a linkage assembly connected to the actuator and connected to the lower cargo bay door, wherein the actuator and linkage assembly are operable to open and/or close the lower cargo bay door.
09 - Appareils et instruments scientifiques et électriques
12 - Véhicules; appareils de locomotion par terre, par air ou par eau; parties de véhicules
35 - Publicité; Affaires commerciales
39 - Services de transport, emballage et entreposage; organisation de voyages
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable software for browsing and purchasing consumer
goods for delivery; navigation apparatus for autonomous
aircrafts and drones; downloadable software for operating,
maintaining, monitoring, logging, and navigating drones and
autonomous aircraft. Drones; autonomous aircraft; unmanned aerial vehicles
(UAVs). Business management of logistics for others; business
management of logistics in the field of drone delivery,
retail, delivery, and transportation; business advisory
services in the field of transportation logistics. Transportation and delivery services of goods by air;
management of autonomous aircraft and drone navigation in
the nature of traffic flow through advanced communications
network and technology; routing of autonomous aircraft and
drones by computer on data networks; aeronautic navigation
services, namely, aeronautic radio navigation services;
expedited shipping service of goods for others; GPS
navigation services for autonomous aircrafts and drones; air
navigation services for autonomous aircrafts and drones;
storage of goods; storage of goods for later pickup and
delivery purposes; storage of goods at designated pickup
locations; transportation logistics services, namely,
arranging, planning, and scheduling the delivery of goods by
drone for others. Providing on-line non-downloadable software for browsing and
purchasing consumer goods for delivery; software as a
service (SAAS) services featuring software for browsing and
purchasing consumer goods for delivery; providing on-line
non-downloadable software for operating, maintaining,
monitoring, logging, and navigating drones and autonomous
aircraft; software as a service (SAAS) services featuring
software for operating, maintaining, monitoring, logging,
and navigating drones and autonomous aircraft.
36.
METHODS AND SYSTEMS FOR DEEP STALL CONTROL OF UNCREWED AERIAL VEHICLES
Examples relate to uncrewed aerial vehicles (UAVs) and methods for controlled descent during control tier failures. A computing device may initially detect a control tier failure at an UAV. In some examples, the UAV includes a fuselage, a pair of wings extending outwardly from the fuselage, and a pair of stabilizers arranged in a V-shape configuration. Each stabilizer has a control surface that is adjustable relative to a fixed portion of the stabilizer. Based on detecting the control tier failure at the UAV, the computing device may adjust the control surface of each stabilizer from a. first angle to a. second angle relative to the fixed portion of the stabilizer. By adjusting the angle between the control surfaces and fixed portions of one or both stabilizers, the UAV may induce a deep stall maneuver that can enable a controlled descent of the UAV.
B64U 40/10 - Dispositions mécaniques embarquées pour régler les surfaces de commande ou les rotorsDispositions mécaniques embarquées pour régler en vol la configuration de base pour régler les surfaces de commande ou les rotors
B64U 10/20 - Aéronefs à décollage et atterrissage verticaux [ADAV, en anglais VTOL]
Examples relate to uncrewed aerial vehicles (UAVs) and methods for controlled descent during control tier failures. A computing device may initially detect a control tier failure at an UAV. In some examples, the UAV includes a fuselage, a pair of wings extending outwardly from the fuselage, and a pair of stabilizers arranged in a V-shape configuration. Each stabilizer has a control surface that is adjustable relative to a fixed portion of the stabilizer. Based on detecting the control tier failure at the UAV, the computing device may adjust the control surface of each stabilizer from a first angle to a second angle relative to the fixed portion of the stabilizer. By adjusting the angle between the control surfaces and fixed portions of one or both stabilizers, the UAV may induce a deep stall maneuver that can enable a controlled descent of the UAV.
B64U 40/20 - Dispositions mécaniques embarquées pour régler les surfaces de commande ou les rotorsDispositions mécaniques embarquées pour régler en vol la configuration de base pour régler en vol la configuration de base
B64U 10/20 - Aéronefs à décollage et atterrissage verticaux [ADAV, en anglais VTOL]
B64U 70/40 - Atterrissage caractérisé par des manœuvres de vol, p. ex. décrochage
B64U 101/64 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis
G05D 1/495 - Commande de l’attitude, c.-à-d. commande du roulis, du tangage ou des embardées pour assurer la stabilité
A joiner includes a first and second portions configured to mount to respective first and second structural members. Each of the first and second portions includes a body with a receiving surface to receive the respective structural member. The first portion includes a group of first coupling structures disposed on the first body and the second portion includes a group of second coupling structures disposed on the second body. The first coupling structures are configured to mate with the second coupling structures such that engagement surfaces of the first coupling structures engage engagement surfaces of the second coupling structures so as to transfer loads between the second portion and first portion and to limit relative movement between the first portion and second portion with respect to the first direction and with respect to a vertical direction.
B64U 101/64 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis
A method includes navigating, by a UAV, to a delivery location in an environment; capturing, by at least one sensor on the UAV, sensor data representative of the delivery location; determining, based on the sensor data, a segmented point cloud of the delivery location, wherein the segmented point cloud defines a plurality of point cloud areas with corresponding semantic classifications; determining, based on the segmented point cloud, that a pre-selected delivery point at the delivery location satisfies a condition indicating that a descent path through a cylinder, the cylinder being centered above the pre-selected delivery point and having a radius of a particular lateral distance, does not intersect with any point cloud areas having semantic classifications indicative of an obstacle at the delivery location; and based on determining that the pre-selected delivery point satisfies the condition, initiating, by the UAV, a payload delivery operation towards the pre-selected delivery point.
G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p. ex. utilisant des pilotes automatiques
B64C 39/02 - Aéronefs non prévus ailleurs caractérisés par un emploi spécial
B64U 101/64 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis
G05D 1/247 - Dispositions pour déterminer la position ou l’orientation utilisant des signaux fournis par des sources artificielles extérieures au véhicule, p. ex. balises de navigation
G05D 1/667 - Livraison ou récupération de charges utiles
41.
USER INTERFACE FOR AREA MANAGEMENT FOR UAV OPERATIONS
An unmanned aerial vehicle fleet management system initiates creation of a digital airspace area of operation, presents in a user interface an area classifier selection field configured to select from a set of available area classifiers; receives first user input classifying the digital airspace area of operation as an oversight area; presents a ruleset selection field; receives second user input specifying one or more rulesets of the oversight area; creates the oversight area based at least in part on the first and second inputs; and creates a pilot area associated with the oversight area that inherits the specified ruleset(s) of the oversight area. The oversight area and pilot area define areas of operation of different types for different classes of users, such as pilots and flight operations managers. The specified ruleset(s) comprise rules related to flight approvals. Flight missions can be created or altered based on the specified ruleset(s).
A method includes causing an uncrewed aerial vehicle (UAV) to navigate through a trajectory. The method also includes receiving first motor data representing operation of a first motor during navigation through the trajectory and receiving second motor data representing operation of a second motor during navigation through the trajectory. The method further includes comparing the first motor data with the second motor data. The method also includes, based on the comparison of the first motor data and the second motor data, determining a motor failure state. The method additionally includes causing the UAV to navigate based on the motor failure state.
In some embodiments, a system for efficient coordination of unmanned aerial vehicle (UAV) operations by a first UAV service supplier (USS) in a geographic area within which the first USS and one or more third-party USSes operate UAVs is provided. The system comprises an interoperability computing system configured to perform actions comprising: receiving, by the interoperability computing system, a new operational intent from a rule engine of the first USS; retrieving, by the interoperability computing system, a set of relevant operational intents that are relevant to the new operational intent from a geographic information data store of the first USS; comparing, by the interoperability computing system, the new operational intent to each relevant operational intent of the set of relevant operational intents to detect conflicts; and in response to detecting no conflicts, transmitting, by the interoperability computing system, an approval request to a discovery and synchronization service (DSS).
G05D 1/227 - Transfert de la commande entre la commande à distance et la commande embarquéeTransfert de la commande entre plusieurs dispositions de commande à distance
G05D 1/226 - Liaisons de communication avec les dispositions de commande à distance
44.
EFFICIENTLY AND ACCURATELY MONITORING AGGREGATE CONFORMANCE WITH OPERATIONAL INTENTS FOR A FLEET OF UNMANNED AERIAL VEHICLES
In some embodiments, a computer-implemented method of efficiently and accurately monitoring aggregate conformance with operational intents for a fleet of unmanned aerial vehicles (UAVs) is provided. A computing system receives telemetry data and operational intents for a plurality of flights during a monitoring period. For each flight of the plurality of flights, the computing system compares the telemetry data associated with the flight to the operational intent associated with the flight; labels each data point of the telemetry data as conformant or non-conformant based on the comparing; and generates a set of excursions based on the labeled data points. The computing system determines a level of aggregate conformance based on the set of excursions, and performs one or more actions in response to the level of aggregate conformance.
In some embodiments, a system for efficient coordination of unmanned aerial vehicle (UAV) operations by a first UAV service supplier (USS) in a geographic area within which the first USS and one or more third-party USSes operate UAVs is provided. The system comprises an interoperability computing system configured to perform actions comprising: receiving, by the interoperability computing system, a new operational intent from a rule engine of the first USS; retrieving, by the interoperability computing system, a set of relevant operational intents that are relevant to the new operational intent from a geographic information data store of the first USS; comparing, by the interoperability computing system, the new operational intent to each relevant operational intent of the set of relevant operational intents to detect conflicts; and in response to detecting no conflicts, transmitting, by the interoperability computing system, an approval request to a discovery and synchronization service (DSS).
In some embodiments, a computer-implemented method of efficiently and accurately monitoring aggregate conformance with operational intents for a fleet of unmanned aerial vehicles (UAVs) is provided. A computing system receives telemetry data and operational intents for a plurality of flights during a monitoring period. For each flight of the plurality of flights, the computing system compares the telemetry data associated with the flight to the operational intent associated with the flight; labels each data point of the telemetry data as conformant or non-conformant based on the comparing; and generates a set of excursions based on the labeled data points. The computing system determines a level of aggregate conformance based on the set of excursions, and performs one or more actions in response to the level of aggregate conformance.
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.
In some embodiments, a computer-implemented method for managing resources of a fleet of unmanned aerial vehicles (UAVs) is provided. A computing system creates a mission record and one or more candidate records. Each candidate record of the one or more candidate records represents one or more resources for accomplishing a mission represented by the mission record. The computing system adds a mission node representing the mission record to a resource competition network graph (RCN graph). The computing system adds one or more candidate nodes representing the one or more candidate records to the RCN graph. The computing system determines an optimized allocation of candidate records to mission records using at least a subgraph of the RCN graph. A candidate record is determined to commit to a mission record, and the computing system updates the RCN graph to commit the candidate record to the mission record.
In some embodiments, a computer-implemented method for managing resources of a fleet of unmanned aerial vehicles (UAVs) is provided. A computing system creates a mission record and one or more candidate records. Each candidate record of the one or more candidate records represents one or more resources for accomplishing a mission represented by the mission record. The computing system adds a mission node representing the mission record to a resource competition network graph (RCN graph). The computing system adds one or more candidate nodes representing the one or more candidate records to the RCN graph. The computing system determines an optimized allocation of candidate records to mission records using at least a subgraph of the RCN graph. A candidate record is determined to commit to a mission record, and the computing system updates the RCN graph to commit the candidate record to the mission record.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G08G 5/32 - Gestion des plans de vol pour la préparation des plans de vol
50.
COLLABORATIVE INFERENCE BETWEEN CLOUD AND ONBOARD NEURAL NETWORKS FOR UAV DELIVERY APPLICATIONS
A method of collaborative analysis of a ground area by UAV delivery service includes acquiring first and second aerial images of the ground area. The first and second aerial images include depictions of objects at the ground area. A query including an encoding of the first aerial image is transmitted to a cloud-based neural network trained to identify objects. A motion of the UAV is tracked between acquiring the first and second aerial images. A response is received from the cloud-based neural network identifying one or more of the objects depicted in the first aerial image. An onboard neural network disposed on board the UAV is used to identify the objects at the ground area. The onboard neural network receives the response, an indication of the motion tracked between the first and second aerial images, and the second aerial image as input when identifying the objects.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
A cleaning structure for a payload retrieval apparatus includes a main body having an upper end and a lower end. The upper end includes a tether attachment point. The cleaning structure also includes a first cleaning component extending outward from the main body. The first cleaning component has a flexible construction for fitting into crevices in the payload retrieval apparatus and defines a cleaning zone around the main body.
B08B 1/10 - Nettoyage par des procédés impliquant l'utilisation d'outils caractérisé par le type d'outil de nettoyage
B08B 1/30 - Nettoyage par des procédés impliquant l'utilisation d'outils par le mouvement d’éléments de nettoyage sur une surface
B64F 1/32 - Installations au sol ou installations pour ponts d'envol des porte-avions pour la manutention du fret
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64U 101/29 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à la fabrication ou à l’entretien au nettoyage
52.
Payload Retrieval Apparatus with Internal Unlocking Feature and Security Features for Use With a UAV
A payload retrieval apparatus having a base, an autoloader assembly mounted to the base including: a payload holder configured to hold a payload for retrieval by a UAV, a channel coupled to the payload holder configured to direct a payload retriever suspended from the UAV to the payload holder, and a locking feature configured to lock access to the payload on the payload holder that includes has a movable end that extends through a wall of the channel into an interior of the channel, wherein when the payload retriever contacts the movable end of the locking member, the movable end moves outwardly thereby unlocking the payload on the payload holder, wherein the payload holder is positioned such that when the payload retriever exits the channel, the payload retriever engages a handle of the payload and removes the payload from the payload holder.
B64U 101/66 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis au retrait de colis
53.
CONVEYOR SYSTEM FOR PAYLOAD RETRIEVAL SYSTEM AND METHOD OF USE
A payload retrieval system includes a support structure and a retriever guide coupled to the support structure. The retriever guide forms a channel having an inlet end and an exit end. The retriever guide is adapted to receive a payload retriever at the inlet end of the channel and direct the payload retriever to the exit end of the channel. The payload retrieval system also includes a holding frame having a payload holder configured to hold a payload for retrieval by the payload retriever when the holding frame is held in an operating position at the exit end of the channel. The system also includes a conveyance system configured to transport the holding frame from a waiting position to the operating position.
B64F 1/32 - Installations au sol ou installations pour ponts d'envol des porte-avions pour la manutention du fret
B65G 35/00 - Transporteurs mécaniques non prévus ailleurs
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
54.
COLLABORATIVE INFERENCE BETWEEN CLOUD AND ONBOARD NEURAL NETWORKS FOR UAV DELIVERY APPLICATIONS
A method of collaborative analysis of a ground area by UAV delivery service includes acquiring first and second aerial images of the ground area. The first and second aerial images include depictions of objects at the ground area. A query including an encoding of the first aerial image is transmitted to a cloud-based neural network trained to identify objects. A motion of the UAV is tracked between acquiring the first and second aerial images. A response is received from the cloud-based neural network identifying one or more of the objects depicted in the first aerial image. An onboard neural network disposed on board the UAV is used to identify the objects at the ground area. The onboard neural network receives the response, an indication of the motion tracked between the first and second aerial images, and the second aerial image as input when identifying the objects.
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
55.
Conveyor System for Payload Retrieval System and Method of Use
A payload retrieval system includes a support structure and a retriever guide coupled to the support structure. The retriever guide forms a channel having an inlet end and an exit end. The retriever guide is adapted to receive a payload retriever at the inlet end of the channel and direct the payload retriever to the exit end of the channel. The payload retrieval system also includes a holding frame having a payload holder configured to hold a payload for retrieval by the payload retriever when the holding frame is held in an operating position at the exit end of the channel. The system also includes a conveyance system configured to transport the holding frame from a waiting position to the operating position.
A cleaning structure for a payload retrieval apparatus includes a main body having an upper end and a lower end. The upper end includes a tether attachment point. The cleaning structure also includes a first cleaning component extending outward from the main body. The first cleaning component has a flexible construction for fitting into crevices in the payload retrieval apparatus and defines a cleaning zone around the main body.
B08B 1/14 - LingettesÉléments absorbants, p. ex. écouvillons ou éponges
F16N 7/12 - Installations à huile ou autre lubrifiant non spécifié, à réservoir ou autre source portés par la machine ou l'organe machine à lubrifier avec alimentation par action capillaire, p. ex. par des mèches
57.
Impact-attenuating tip for a structural member of an aircraft
An uncrewed aerial vehicle (UAV) includes a support extending along a flight direction of the UAV. The support includes an elongate structural member, a cap coupled to a front end of the elongate structural member, and an energy absorber. The support extends along an axis that runs through the support from a front end to a rear end. The cap is coupled to the front end of the elongate structural member. The cap includes a support platform that has a front surface opposite the elongate structural member and that extends outward from the axis. The energy absorber is disposed on the front surface of the support platform and includes a crushable material configured to absorb energy under impact. The UAV also includes a first propeller unit coupled to the support.
A method includes receiving an input specifying a starting location and a destination location for an aerial vehicle. The method additionally includes determining, based on the starting location and the destination location, an aerial path for the aerial vehicle to follow from the starting location to the destination location. The method also includes determining, based on the aerial path, a property of aerial image data, where the aerial image data is obtainable using the aerial vehicle while traversing the aerial path, and where the aerial image data represents an environment along the aerial path. The method further includes determining, based on the property, a path score associated with the aerial path, and outputting the aerial path based on the path score.
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
G05D 1/243 - Moyens de capture de signaux provenant naturellement de l’environnement, p. ex. signaux optiques, acoustiques, gravitationnels ou magnétiques ambiants
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
A method includes charging a battery of a vehicle to a charge threshold voltage. The method also includes discharging the battery from the charge threshold voltage to a post-task voltage by performing a travel task using the vehicle. The method additionally includes determining that a battery calibration condition has been met. The method further includes, based on determining that the battery calibration condition has been met, discharging the battery from the post-task voltage to a discharge threshold voltage by performing a battery discharge task. The method yet further includes determining a capacity of the battery based on a first electrical output of the battery during the travel task and a second electrical output of the battery during the battery discharge task.
H02J 7/00 - Circuits pour la charge ou la dépolarisation des batteries ou pour alimenter des charges par des batteries
G01R 31/3835 - Dispositions pour la surveillance de variables des batteries ou des accumulateurs, p. ex. état de charge ne faisant intervenir que des mesures de tension
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.
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
G06T 5/50 - Amélioration ou restauration d'image utilisant plusieurs images, p. ex. moyenne ou soustraction
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
Example embodiments may include determining test flight data based on actual flight data that has been captured by a sensor of an aerial vehicle during a previous flight performed by the aerial vehicle in a physical environment. The test flight data may be processed using a software component that forms part of an aerial vehicle control system. An observed performance of the software component may be determined based on processing the test flight data using the software component. A performance metric may be determined for the software component based on comparing (i) the observed performance of the software component to (ii) an expected performance of the software component. The performance metric may be output.
A technique for a UAV includes acquiring a query aerial image with an onboard camera of the UAV and a reference aerial image, the query aerial image including multiple instances of an asset and the reference aerial image including annotated pixels indicating an expected location and an identification for the multiple instances of the asset. The technique further includes identifying a plurality of corresponding pixels between the query aerial image and the reference aerial image, determining a homography transformation describing a relationship between the query aerial image and the reference aerial image, annotating the query aerial image to identify a first instance of the asset included in the multiple instances of the asset within the query aerial image, and instructing the UAV to perform an action associated with the first instance of the asset.
G01S 19/48 - Détermination de position en combinant ou en commutant entre les solutions de position dérivées du système de positionnement par satellite à radiophares et les solutions de position dérivées d'un autre système
G01S 19/26 - Acquisition ou poursuite des signaux émis par le système faisant intervenir une mesure par capteur pour faciliter l'acquisition ou la poursuite
G01S 19/40 - Correction de position, de vitesse ou d'attitude
G05D 1/656 - Interaction avec des charges utiles ou des entités externes
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/72 - Préparation de données, p. ex. prétraitement statistique des caractéristiques d’images ou de vidéos
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/77 - Traitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source
G06V 10/778 - Apprentissage de profils actif, p. ex. apprentissage en ligne des caractéristiques d’images ou de vidéos
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
G06V 20/70 - Étiquetage du contenu de scène, p. ex. en tirant des représentations syntaxiques ou sémantiques
B64F 1/35 - Installations au sol ou installations pour ponts d'envol des porte-avions pour l'alimentation en énergie électrique des aéronefs en stationnement
B64U 70/90 - Lancement à partir de ou atterrissage sur des plates-formes
B64U 101/31 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’imagerie, à la photographie ou à la vidéographie à la surveillance
A technique for a UAV includes acquiring a query aerial image with an onboard camera of the UAV and a reference aerial image, the query aerial image including multiple instances of an asset and the reference aerial image including annotated pixels indicating an expected location and an identification for the multiple instances of the asset. The technique further includes identifying a plurality of corresponding pixels between the query aerial image and the reference aerial image, determining a homography transformation describing a relationship between the query aerial image and the reference aerial image, annotating the query aerial image to identify a first instance of the asset included in the multiple instances of the asset within the query aerial image, and instructing the UAV to perform an action associated with the first instance of the asset.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/75 - Organisation de procédés de l’appariement, p. ex. comparaisons simultanées ou séquentielles des caractéristiques d’images ou de vidéosApproches-approximative-fine, p. ex. approches multi-échellesAppariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexteSélection des dictionnaires
G06T 3/14 - Transformations pour le recalage d’images, p. ex. ajustement ou mappage pour l’alignement d’images
G06T 7/30 - Détermination des paramètres de transformation pour l'alignement des images, c.-à-d. recalage des images
64.
GENERATING AERIAL PATHS BASED ON PROPERTIES OF AERIAL IMAGE DATA
A method includes receiving an input specifying a starting location and a destination location for an aerial vehicle. The method additionally includes determining, based, on the starting location and the destination location, an aerial path for the aerial vehicle to follow from the starting location to the destination location. The method also includes determining, based on the aerial path, a property of aerial image data, where the aerial image data is obtainable using the aerial vehicle while traversing the aerial path, and where the aerial image data, represents an environment along the aerial path. The method further includes determining, based on the property, a path score associated with the aerial path, and outputting the aerial path based on the path score.
G01C 21/00 - NavigationInstruments de navigation non prévus dans les groupes
G01C 23/00 - Instruments combinés indiquant plus d’une valeur de navigation, p. ex. pour l’aviationDispositifs de mesure combinés pour mesurer plusieurs variables du mouvement, p. ex. la distance, la vitesse ou l’accélération
G05D 1/46 - Commande de la position ou du cap dans les trois dimensions
B64U 101/30 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’imagerie, à la photographie ou à la vidéographie
A technique for maintaining a backend terrain model used by a fleet of unmanned aerial vehicles (UAVs) of a UAV service supplier (USS) includes acquiring sensor data of a terrain below a first UAV of the fleet of UAVs as the first UAV executes a mission. The sensor data is analyzed with a terrain detection module disposed on-board the first UAV to determine whether the terrain deviates from a local terrain model describing the terrain. The local terrain model is stored on-board the first UAV. A terrain deviation message is issued from the first UAV to a backend management system of the USS that maintains the backend terrain model in response to a determination that the terrain deviates from the local terrain model. The terrain deviation message includes an indication that a deviant terrain has been identified and location data indicating an approximate location of the deviant terrain.
A technique for maintaining a backend terrain model used by a fleet of unmanned aerial vehicles (UAVs) of a UAV service supplier (USS) includes acquiring sensor data of a terrain below a first UAV of the fleet of UAVs as the first UAV executes a mission. The sensor data is analyzed with a terrain detection module disposed on-board the first UAV to determine whether the terrain deviates from a local terrain model describing the terrain. The local terrain model is stored on-board the first UAV. A terrain deviation message is issued from the first UAV to a backend management system of the USS that maintains the backend terrain model in response to a determination that the terrain deviates from the local terrain model. The terrain deviation message includes an indication that a deviant terrain has been identified and location data indicating an approximate location of the deviant terrain.
G06F 30/13 - Conception architecturale, p. ex. conception architecturale assistée par ordinateur [CAAO] relative à la conception de bâtiments, de ponts, de paysages, d’usines ou de routes
67.
USER INTERFACE FOR CREATION OF FLIGHT RESTRICTIONS ON UAV OPERATIONS BASED ON NON-DIGITAL DATA INPUTS
A technique for managing an airspace used by a fleet of UAVs includes presenting a user interface (UI) adapted for creating an airspace restriction based on non-digitized information available to a human supervisor and input into the UI by the human supervisor, soliciting with a selectable field of the UI a restriction type for the airspace restriction from a plurality of available restriction types, soliciting with duration fields of the UI start and end times for the airspace restriction, soliciting with location fields of the UI a location of the airspace restriction, creating a new entry for the airspace restriction in a restriction data store based on the selectable, duration, and location fields, and creating a new flight mission or altering an existing flight mission based upon the airspace restriction.
A technique of camera exposure control for vision-based navigation of an unmanned aerial vehicle (UAV) includes acquiring an aerial image of a ground area below the UAV with an onboard camera system of the UAV, estimating a visual motion factor based on a speed of the UAV and an altitude of the UAV, and adjusting an exposure control setting of the onboard camera system based on the visual motion factor.
G01S 19/47 - Détermination de position en combinant les mesures des signaux provenant du système de positionnement satellitaire à radiophares avec une mesure supplémentaire la mesure supplémentaire étant une mesure inertielle, p. ex. en hybridation serrée
G05D 1/611 - Maintien de la position, p. ex. pour un ancrage en vol stationnaire ou dynamique
A tool for loading a payload on a payload retrieval apparatus includes a support body and a guide extending outward from the support body. The guide has an elongated configuration and a cross-sectional area that is narrower than the support body. The guide is configured to be inserted into a channel of the pay load retrieval apparatus and to position the support body at an end of the channel adjacent to a payload holding structure. The tool also includes a hanger configured to hold the payload. The hanger is movable with respect to the support body between a closed position and a release position.
B64F 1/32 - Installations au sol ou installations pour ponts d'envol des porte-avions pour la manutention du fret
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
70.
PAYLOAD HOLDING FACEPLATE FOR PAYLOAD RETRIEVAL SYSTEM
A payload retrieval system includes a support structure and a retriever guide coupled to the support structure. The retriever guide forms a channel having an inlet end and an exit end. The retriever guide is adapted to receive a payload retriever at the inlet end of the channel and direct the payload retriever to the exit end of the channel. The payload retrieval system also includes a faceplate coupled to the retriever guide. The faceplate includes a body extending around a passage aligned with the exit end of the channel, and a payload holder including first and second hooks adapted to hold a payload for receipt by a payload retriever that passes through the retriever guide.
B64F 1/32 - Installations au sol ou installations pour ponts d'envol des porte-avions pour la manutention du fret
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64U 101/64 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
A payload retrieval system includes a support structure and a retriever guide coupled to the support structure. The retriever guide includes a group of modular components that form a channel having an inlet end and an exit end. The retriever guide is adapted to receive a payload retriever at the inlet end of the channel and direct the payload retriever to the exit end of the channel. The modular components include a funnel that forms the inlet end of the channel, a rotator downstream of the funnel that is configured to rotate the payload retriever about a direction of travel through the rotator, and an angle adjuster downstream of the rotator that reduces the angle of inclination of the channel. The system also includes a payload holder disposed at the exit end of the channel.
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64F 1/32 - Installations au sol ou installations pour ponts d'envol des porte-avions pour la manutention du fret
B64U 101/66 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis au retrait de colis
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
72.
PAYLOAD RETRIEVAL SYSTEM WITH TETHER RETAINING WALLS
A payload retrieval system includes a support structure and a retriever guide coupled to the support structure. The retriever guide includes a body that forms a channel having an inlet end and an exit end and a tether slot that provides access to the channel. The retriever guide is adapted to receive, at the inlet end of the channel, a payload retriever secured to a tether and to direct the payload retriever to the exit end of the channel while the tether pulls the payload retriever through the retriever guide. The retriever guide also includes a first retaining wall extending upward from the body of the retriever guide along a first side of the tether slot and a second retaining wall extending upward from the body of the retriever guide along a second side of the tether slot. The payload retrieval system also includes a payload holder disposed at the exit end of the channel.
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64F 1/32 - Installations au sol ou installations pour ponts d'envol des porte-avions pour la manutention du fret
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
73.
ADAPTIVE CAMERA EXPOSURE CONTROL FOR NAVIGATING A UAV IN LOW LIGHT CONDITIONS
A technique of camera exposure control for vision-based navigation of an unmanned aerial vehicle (UAV) includes acquiring an aerial image of a ground area below the UAV with an onboard camera system of the UAV, estimating a visual motion factor based on a speed of the UAV and an altitude of the UAV, and adjusting an exposure control setting of the onboard camera system based on the visual motion factor.
A package adapted for use with an uncrewed aerial vehicle (UAV) is provided. The package forms a container and has a handle at the top. Embodiments of the package include features for efficient manufacturing and storage.
B65D 25/22 - Accessoires externes pour faciliter le levage ou la suspension des réceptacles
B65D 88/14 - Grands réceptacles rigides spécialement conçus pour le transport par air
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
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.
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
G06T 5/50 - Amélioration ou restauration d'image utilisant plusieurs images, p. ex. moyenne ou soustraction
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
An apparatus for landing and launching unmanned aerial vehicles (UAVs) includes a landing pad adapted for receiving the UAVs, a track extending from the landing pad and positioned to engage with the UAVs after the UAVs land on the landing pad, and a charging system positioned to charge the UAVs engaged with the track. The track is adapted to guide or carry the UAVs along the track from the landing pad.
E04H 6/44 - Bâtiments pour garer des voitures, du matériel roulant, des avions, des bateaux ou d’autres véhicules, p. ex. garages pour garer des avions
B60L 53/30 - Détails de construction des stations de charge
B64U 70/90 - Lancement à partir de ou atterrissage sur des plates-formes
B64U 70/95 - Moyens de guidage du véhicule aérien sans pilote atterrissant vers la plate-forme, p. ex. moyens d’éclairage
B64U 80/10 - Transport ou stockage spécialement adaptés aux véhicules aériens sans pilote avec des moyens de déplacement du véhicule aérien sans pilote vers un emplacement d’alimentation ou de lancement, p. ex. armes robotiques ou carrousels
B64U 80/25 - Transport ou stockage spécialement adaptés aux véhicules aériens sans pilote avec des dispositions pour assurer le service du véhicule aérien sans pilote pour la recharge de batteriesTransport ou stockage spécialement adaptés aux véhicules aériens sans pilote avec des dispositions pour assurer le service du véhicule aérien sans pilote pour le ravitaillement en combustible
B64U 80/40 - Transport ou stockage spécialement adaptés aux véhicules aériens sans pilote à plusieurs véhicules aériens sans pilote
B64U 80/70 - Transport ou stockage spécialement adaptés aux véhicules aériens sans pilote dans des réceptacles
E04H 6/12 - Garages pour de nombreux véhicules avec moyens mécaniques pour déplacer ou élever les véhicules
G08G 5/55 - Aides à la navigation ou au guidage pour un seul aéronef
G08G 5/57 - Aides à la navigation ou au guidage pour les aéronefs sans pilote
77.
Efficient raster data range query for planned UAV flight segments
A computer system obtains a raster cell of terrain data from a database, a query shape corresponding to a planned flight segment of a UAV that intersects with the raster cell, and a range of acceptable terrain elevation values for the planned flight segment. The raster cell comprises a minimum terrain elevation value, a maximum terrain elevation value, and links to sub-cells of the raster cell. The computer system determines whether the minimum and maximum terrain elevation values of the raster cell are within the range of acceptable terrain elevation values for the planned flight segment and validates the planned flight segment with respect to the raster cell based at least in part on whether the minimum and maximum terrain elevation values of the raster cell are within the range of acceptable terrain elevation values for the planned flight segment.
A technique for mitigating nuisance to a neighborhood from operations of an UAV delivery service includes: calculating nuisance contributions to the neighborhood for each of a plurality of UAV flights over the neighborhood; aggregating the nuisance contributions for each of the UAV flights into a nuisance heat map stored in a nuisance exposure database; receiving, at a machine learning (ML) model, a flight routing request to fly a new delivery mission over the neighborhood; and generating a new flight path for the new delivery mission with the ML model in response to receiving the flight routing request. The ML model is trained to receive the nuisance heat map and the flight routing request as inputs and output the new flight path that optimizes a total nuisance contribution that the new delivery mission will contribute to the neighborhood.
B64U 10/20 - Aéronefs à décollage et atterrissage verticaux [ADAV, en anglais VTOL]
G06N 3/044 - Réseaux récurrents, p. ex. réseaux de Hopfield
G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
G08G 5/32 - Gestion des plans de vol pour la préparation des plans de vol
G08G 5/55 - Aides à la navigation ou au guidage pour un seul aéronef
G08G 5/57 - Aides à la navigation ou au guidage pour les aéronefs sans pilote
B64U 101/64 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis
79.
Machine-Learned Monocular Depth Estimation and Semantic Segmentation for 6-DOF Absolute Localization of a Delivery Drone
A method includes receiving a two-dimensional (2D) image captured by a camera on a unmanned aerial vehicle (UAV) and representative of an environment of the UAV. The method further includes applying a trained machine learning model to the 2D image to produce a semantic image of the environment and a depth image of the environment, where the semantic image comprises one or more semantic labels. The method additionally includes retrieving reference depth data representative of the environment, wherein the reference depth data includes reference semantic labels. The method also includes aligning the depth image of the environment with the reference depth data representative of the environment to determine a location of the UAV in the environment, where the aligning associates the one or more semantic labels from the semantic image with the reference semantic labels from the reference depth data.
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
B64U 50/19 - Propulsion utilisant des moteurs électriques
B64U 101/30 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’imagerie, à la photographie ou à la vidéographie
B64U 101/60 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes
G01S 19/48 - Détermination de position en combinant ou en commutant entre les solutions de position dérivées du système de positionnement par satellite à radiophares et les solutions de position dérivées d'un autre système
G06T 7/50 - Récupération de la profondeur ou de la forme
In one aspect an uncrewed aerial vehicle (UAV) is provided. The uncrewed aerial vehicle includes a fuselage and a drag reduction device. The fuselage has a front end, a rear end, a top, and a bottom. The drag reduction device includes a proximal end and a distal end. The proximal end of the drag reduction device is coupled to the bottom of the fuselage. The drag reduction device is rotatable between a rest position and an active position in which the drag reduction device extends downward. A standoff is disposed on a rear side of the drag reduction device and is configured to engage a payload secured under the fuselage and hold the drag reduction device at a distance from the payload when the drag reduction device is in the active position.
B64U 20/70 - Caractéristiques de construction du corps du véhicule aérien sans pilote
B64U 10/20 - Aéronefs à décollage et atterrissage verticaux [ADAV, en anglais VTOL]
B64C 7/00 - Structures ou carénages non prévus ailleurs
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64D 1/10 - Arrimage de ces dispositifs sur aéronefs
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
81.
UAV FLIGHT CONTROL OPERATIONS FOR PREDICTED TRAFFIC ENCOUNTER
A method is disclosed. The method includes receiving an indication of presence of an aircraft in a vicinity of an uncrewed aerial vehicle (II AV) which is flying along a flight path. The method also includes decelerating, based on the received indication, the UAV to reduce a ground speed along the flight path. The method additionally includes descending, after reducing the ground, speed, the UAV to a hover position. The method further includes determining, while the UAV is in the hover position, whether to resume the flight path or to land the UAV based on a determination of continued presence of the aircraft in the vicinity of the UAV. The method also includes controlling the UAV based on the determination of whether to resume the flight path or to land the UAV.
An unmanned aerial vehicle (UAV) is disclosed that includes a retractable payload delivery system. The payload delivery system can lower a payload to the ground using a delivery device that secures the payload during descent and releases the payload upon reaching the ground. The location of the delivery device can be determined as it is lowered to the ground using image tracking. The UAV can include an imaging system that captures image data of the suspended delivery device and identifies image coordinates of the delivery device, and the image coordinates can then be mapped to a location. The UAV may also be configured to account for any deviations from a planned path of descent in real time to effect accurate delivery locations of released payloads.
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64U 101/30 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’imagerie, à la photographie ou à la vidéographie
B64U 101/64 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
G05D 1/46 - Commande de la position ou du cap dans les trois dimensions
G05D 1/689 - Interaction avec des charges utiles ou des entités externes dirigeant des charges utiles vers des cibles fixes ou en mouvement
83.
INTEGRATION OF UAV DELIVERY SERVICES IN THIRD-PARTY SYSTEMS
In some embodiments, a method of managing deliveries of orders by a fleet of unmanned aerial vehicles (UAVs) from retail fulfillment locations to delivery locations is provided. A fleet management computing system receives a request for delivery of an order from a retailer ordering computing system. The fleet management computing system transmits one or more package identifiers to be associated with one or more packages for the order. The fleet management computing system receives a notification that a package of the one or more packages for the order is ready for pickup at a retail fulfillment location. The fleet management computing system determines a navigation route for a UAV to the delivery location via the retail fulfillment location. The fleet management computing system transmits the navigation route to the UAV for autonomous navigation of the navigation route to the delivery location via the retail fulfillment location to deliver the package.
In some embodiments, a method of planning a navigation route for an autonomous vehicle is provided. A computing system receives mission information including a start location and a goal location. The computing system generates a representation of an operation area that includes the start location and the goal location. The computing system updates the representation of the operation area based on one or more temporary obstacles. The computing system provides the representation of the operation area, the start location, and the goal location as input to a machine-learning model to generate a cost-to-go map of the operation area. The computing system determines the navigation route using the cost-to-go map of the operation area.
An apparatus may include a substrate. The apparatus may also include a first charger terminal disposed on the substrate, including silver, and configured, to apply a first electric potential to a first battery terminal of a battery. The apparatus may additionally include a second charger terminal disposed on the substrate, comprising silver, and configured to apply a second electric potential to a second battery terminal of the battery.
An apparatus may include a substrate. The apparatus may also include a first charger terminal disposed on the substrate and configured to apply a first electric potential to a first battery terminal of a battery. The apparatus may additionally include a second charger terminal disposed on the substrate and configured to apply a second electric potential to a second battery terminal of the battery. The apparatus may further include a barrier located on the substrate between the first charger terminal and the second charger terminal and configured to electrically isolate the first charger terminal and the second charger terminal by obstructing formation of a conductive path by way of a liquid disposed on the substrate.
In some embodiments, a method of managing deliveries of orders by a fleet of unmanned aerial vehicles (UAVs) from retail fulfillment locations to delivery locations is provided. A fleet management computing system receives a request for delivery of an order from a retailer ordering computing system. The fleet management computing system transmits one or more package identifiers to be associated with one or more packages for the order. The fleet management computing system receives a notification that a package of the one or more packages for the order is ready for pickup at a retail fulfillment location. The fleet management computing system determines a navigation route for a UAV to the delivery location via the retail fulfillment location. The fleet management computing system transmits the navigation route to the UAV for autonomous navigation of the navigation route to the delivery location via the retail fulfillment location to deliver the package.
In some embodiments, a method of planning a navigation route for an autonomous vehicle is provided. A computing system receives mission information including a start location and a goal location. The computing system generates a representation of an operation area that includes the start location and the goal location. The computing system updates the representation of the operation area based on one or more temporary obstacles. The computing system provides the representation of the operation area, the start location, and the goal location as input to a machine-learning model to generate a cost-to-go map of the operation area. The computing system determines the navigation route using the cost-to-go map of the operation area.
An apparatus may include a substrate. The apparatus may also include a first charger terminal disposed on the substrate, including silver, and configured to apply a first electric potential to a first battery terminal of a battery. The apparatus may additionally include a second charger terminal disposed on the substrate, comprising silver, and configured to apply a second electric potential to a second battery terminal of the battery.
B60L 53/16 - Connecteurs, p. ex. fiches ou prises, spécialement adaptés pour recharger des véhicules électriques
B64F 1/35 - Installations au sol ou installations pour ponts d'envol des porte-avions pour l'alimentation en énergie électrique des aéronefs en stationnement
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.
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
B64C 39/02 - Aéronefs non prévus ailleurs caractérisés par un emploi spécial
B64U 101/30 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’imagerie, à la photographie ou à la vidéographie
B64U 101/60 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes
G05D 1/485 - Commande du taux de modification de l’altitude ou de la profondeur
G05D 1/689 - Interaction avec des charges utiles ou des entités externes dirigeant des charges utiles vers des cibles fixes ou en mouvement
G06Q 10/0832 - Marchandises spéciales ou procédures de manutention spéciales, p. ex. manutention de marchandises dangereuses ou fragiles
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
91.
Day/night filter glass for aircraft camera systems
A technique for using an onboard camera capable of day and night operation on an unmanned aerial vehicle (UAV) includes: traveling along a route of the UAV at night; and acquiring an aerial image with an onboard camera. The onboard camera includes: a sensor device for receiving photons and converting the photons into photoelectrons; a processor for processing the photoelectrons into an image file; a lens positioned adjacent to the sensor device for focusing the photons on the sensor device; and a filter positioned adjacent an outer surface of the lens. The filter can permit photons in the infrared light spectrum to pass to the lens, and attenuate at least a portion of the photons in the visible light spectrum prior to reaching the lens.
G03B 11/00 - Filtres ou autres intercepteurs spécialement adaptés pour les besoins photographiques
B64U 101/30 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’imagerie, à la photographie ou à la vidéographie
G03B 15/00 - Procédés particuliers pour prendre des photographiesAppareillage à cet effet
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06V 20/17 - Scènes terrestres transmises par des avions ou des drones
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c.-à-d. systèmes dans lesquels le signal vidéo n'est pas diffusé
H04N 23/11 - Caméras ou modules de caméras comprenant des capteurs d'images électroniquesLeur commande pour générer des signaux d'image à partir de différentes longueurs d'onde pour générer des signaux d'image à partir de longueurs d'onde de lumière visible et infrarouge
A payload retrieval system includes a support structure and a retriever guide coupled to the support structure. The retriever guide includes a group of modular components that form a channel having an inlet end and an exit end. The retriever guide is adapted to receive a payload retriever at the inlet end of the channel and direct the payload retriever to the exit end of the channel. The modular components include a funnel that forms the inlet end of the channel, a rotator downstream of the funnel that is configured to rotate the payload retriever about a direction of travel through the rotator, and an angle adjuster downstream of the rotator that reduces the angle of inclination of the channel. The system also includes a payload holder disposed at the exit end of the channel.
B64U 101/66 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis au retrait de colis
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
93.
Payload Holding Faceplate for Payload Retrieval System
A payload retrieval system includes a support structure and a retriever guide coupled to the support structure. The retriever guide forms a channel having an inlet end and an exit end. The retriever guide is adapted to receive a payload retriever at the inlet end of the channel and direct the payload retriever to the exit end of the channel. The payload retrieval system also includes a faceplate coupled to the retriever guide. The faceplate includes a body extending around a passage aligned with the exit end of the channel, and a payload holder including first and second hooks adapted to hold a payload for receipt by a payload retriever that passes through the retriever guide.
B64U 101/64 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes à la livraison ou au retrait de colis
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
94.
Payload Retrieval System with Tether Retaining Walls
A payload retrieval system includes a support structure and a retriever guide coupled to the support structure. The retriever guide includes a body that forms a channel having an inlet end and an exit end and a tether slot that provides access to the channel. The retriever guide is adapted to receive, at the inlet end of the channel, a payload retriever secured to a tether and to direct the payload retriever to the exit end of the channel while the tether pulls the payload retriever through the retriever guide. The retriever guide also includes a first retaining wall extending upward from the body of the retriever guide along a first side of the tether slot and a second retaining wall extending upward from the body of the retriever guide along a second side of the tether slot. The payload retrieval system also includes a payload holder disposed at the exit end of the channel.
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
95.
PAYLOAD RETRIEVAL APPARATUS WITH INTERNAL UNLOCKING FEATURE AND SECURITY FEATURES FOR USE WITH A UAV
A payload retrieval apparatus having a base, an autoloader assembly mounted to the base including: a payload holder configured to hold a payload for retrieval by a UAV, a channel coupled to the payload holder configured to direct a payload retriever suspended from the UAV to the payload holder, and a locking feature configured to lock access to the payload on the pay load holder that includes has a movable end that extends through a wall of the channel into an interior of the channel, wherein when the payload retriever contacts the movable end of the locking member, the movable end moves outwardly thereby unlocking the pay load on the payload holder, wherein the payload holder is positioned such that when the payload retriever exits the channel, the pay load retriever engages a handle of the pay load and removes the payload from the payload holder.
B64F 1/32 - Installations au sol ou installations pour ponts d'envol des porte-avions pour la manutention du fret
B64D 1/22 - Enlèvement d'objets à la surface du sol
B64U 101/67 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de passagersVéhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques au transport de marchandises autres que des armes les véhicules aériens sans pilote comprenant des attaches pour descendre les marchandises
96.
DAY/NIGHT FILTER GLASS FOR AIRCRAFT CAMERA SYSTEMS
A technique for using an onboard camara capable of day and night operation on an unmanned aerial vehicle (UAV) includes: traveling along a route of the UAV at night; and acquiring an aerial image with an onboard camera. The onboard camera includes: a sensor device for receiving photons and converting the photons into photoelectrons; a processor for processing the photoelectrons into an image file; a lens positioned adjacent to the sensor device for focusing the photons on the sensor device; and a filter positioned adjacent an outer surface of the lens. The filter can permit photons in the infrared light spectrum to pass to the lens, and attenuate at least a portion of the photons in the visible light spectrum prior to reaching the lens.
H04N 23/57 - Détails mécaniques ou électriques de caméras ou de modules de caméras spécialement adaptés pour être intégrés dans d'autres dispositifs
G03B 15/00 - Procédés particuliers pour prendre des photographiesAppareillage à cet effet
B64U 20/87 - Montage des dispositifs d’imagerie, p. ex. montage des suspensions à cardan
B64U 101/30 - Véhicules aériens sans pilote spécialement adaptés à des utilisations ou à des applications spécifiques à l’imagerie, à la photographie ou à la vidéographie
97.
USER INTERFACE FOR CREATION OF FLIGHT RESTRICTIONS ON UAV OPERATIONS BASED ON NON-DIGITAL DATA INPUTS
A technique for managing an airspace used by a fleet of UAVs includes presenting a user interface (UI) adapted for creating an airspace restriction based on non-digitized information available to a human supervisor and input into the UI by the human supervisor, soliciting with a selectable field of the UI a restriction type for the airspace restriction from a plurality of available restriction types, soliciting with duration fields of the UI start and end times for the airspace restriction, soliciting with location fields of the UI a location of the airspace restriction, creating a new entry for the airspace restriction in a restriction data store based on the selectable, duration, and location fields, and creating a new flight mission or altering an existing flight mission based upon the airspace restriction.
G05D 1/229 - Données d’entrée de commande, p. ex. points de passage
G06F 3/04883 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p. ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p. ex. des gestes en fonction de la pression exercée enregistrée par une tablette numérique utilisant un écran tactile ou une tablette numérique, p. ex. entrée de commandes par des tracés gestuels pour l’entrée de données par calligraphie, p. ex. sous forme de gestes ou de texte
A method for perception validation of an unmanned aerial vehicle (UAV) includes: acquiring an aerial image of a ground area with an onboard camera system of the UAV, generating a semantic above ground altitude (AGL) estimate with a neural network trained to output the semantic AGL estimate in response to the aerial image fed as an input to the neural network, generating a motion estimate or a position estimate based upon perception sensor data output from a perception sensor disposed onboard the UAV, and cross-validating the motion or position estimate against the semantic AGL estimate.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G01C 5/00 - Mesure des hauteursMesure des distances transversales par rapport à la ligne de viséeNivellement entre des points séparésNiveaux à lunette
G01C 21/16 - NavigationInstruments de navigation non prévus dans les groupes en utilisant des mesures de la vitesse ou de l'accélération exécutées à bord de l'objet navigantNavigation à l'estime en intégrant l'accélération ou la vitesse, c.-à-d. navigation par inertie
09 - Appareils et instruments scientifiques et électriques
12 - Véhicules; appareils de locomotion par terre, par air ou par eau; parties de véhicules
35 - Publicité; Affaires commerciales
39 - Services de transport, emballage et entreposage; organisation de voyages
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable software for browsing and purchasing consumer goods for delivery; Navigation apparatus for autonomous aircrafts and drones; Downloadable software for operating, maintaining, monitoring, logging, and navigating drones and autonomous aircraft Drones; Autonomous aircraft; Unmanned aerial vehicles (UAVs) Transportation logistics services, namely, arranging, planning, and scheduling the delivery of goods by drone for others; Business management of logistics for others; Logistics management in the field of drone delivery, retail, delivery, and transportation; Business advisory services in the field of transportation logistics Transportation and delivery services of goods by air; Management of autonomous aircraft and drone navigation in the nature of traffic flow through advanced communications network and technology; Routing of autonomous aircraft and drones by computer on data networks; Aeronautic navigation services, namely, aeronautic radio navigation services; Expedited shipping service of goods for others; GPS navigation services for autonomous aircrafts and drones; Air navigation services for autonomous aircrafts and drones; Storage of goods; Storage of goods for later pickup and delivery purposes; Storage of goods at designated pickup locations Providing on-line non-downloadable software for browsing and purchasing consumer goods for delivery; Software as a service (SAAS) services featuring software for browsing and purchasing consumer goods for delivery; Providing on-line non-downloadable software for operating, maintaining, monitoring, logging, and navigating drones and autonomous aircraft; Software as a service (SAAS) services featuring software for operating, maintaining, monitoring, logging, and navigating drones and autonomous aircraft
100.
UAV perception validation based upon a semantic AGL estimate
A method for perception validation of an unmanned aerial vehicle (UAV) includes: acquiring an aerial image of a ground area with an onboard camera system of the UAV, generating a semantic above ground altitude (AGL) estimate with a neural network trained to output the semantic AGL estimate in response to the aerial image fed as an input to the neural network, generating a motion estimate or a position estimate based upon perception sensor data output from a perception sensor disposed onboard the UAV, and cross-validating the motion or position estimate against the semantic AGL estimate.
G08G 5/74 - Dispositions pour la surveillance des situations ou des conditions liées au trafic pour la surveillance du terrain
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
G08G 5/55 - Aides à la navigation ou au guidage pour un seul aéronef
G08G 5/57 - Aides à la navigation ou au guidage pour les aéronefs sans pilote
