1505results about "Remote controlled aircraft" patented technology

Methods, systems and apparatus, including computer programs encoded on computer storage media for unmanned aerial vehicle flight for performing an inspection of land, property, structures or other objects. Automated aerial surveys allow a UAV to obtain aerial data without human manual control of a UAV. For certain aerial surveys, a UAV is not capable of completing the survey without refueling, or exchanging out used batteries for fresh batteries. For such aerial surveys, a method and system is needed to allow an operator to automatically perform an aerial survey while determining battery usage and replacement. Also, in circumstances where manual control of the UAV is needed, contingency-based software controls may be needed.

A base station is disclosed for an unmanned aerial vehicle (UAV) that includes: an enclosure defining a window that is configured to receive the UAV to allow for entry of the UAV into the base station and exit of the UAV from the base station; a door that is movably connected to the enclosure such that the door is repositionable between a closed position and an open position; a sealing member that extends about the window and which is configured for engagement with the door so as to form a seal therewith in the closed position; and a heating system that is supported by the enclosure and which is configured to heat the door and / or the sealing member to support operation (e.g., opening and closure) of the door in a cold environment, wherein the heating system includes at least one light source and at least one heating element.

A base station is disclosed for an unmanned aerial vehicle (UAV). The base station includes: an enclosure; a slide mechanism that is connected to the enclosure and which is repositionable between a retracted position and an extended position; and a cradle that is connected to the slide mechanism and which defines a chamber that is configured to receive the UAV such that the UAV is movable into and out of the enclosure during repositioning of the slide mechanism between the retracted position and the extended position. The cradle includes: an upper shell; a lower shell that is connected to the upper shell; and at least one thermal insulator that is located between the upper shell and the lower shell.

A method and apparatus for measuring optical turbulence using an unmanned aerial vehicle are provided. The unmanned aerial vehicle may include a central body, at least one motor operatively connected to the central body, a mounting element, a three-dimensional anemometer mounted on the mounting element, a first differential temperature sensor mounted on the mounting element, a second differential temperature sensor mounted to the mounting element, and a base mounted to the central body via a payload mounting interface including a first barometric pressure sensor, a central processing board, memory operatively connected to the central processing board, and a processor configured to generate refractive-index structure coefficient information.

A system, method, and autonomous vehicle (AV) that generates a row-based world model for perceptive navigation of an AV are described. The system includes a client device, a cloud component, and the AV. The client device receives a map image of a field having a plurality of rows, in which each row includes a plurality of plants. The cloud component then generates a row-based frame of reference, in which each row has an associated frame of reference that includes a distance. A location is determined based on a row number and the distance associated with the row number. The cloud component also associates a semantic instruction with the row-based world model, and the cloud component communicates the row-based world model to the AV.

A landing system suitable for receiving an unmanned aerial vehicle (UAV) comprises an autonomous ground vehicle (AGV). A landing surface is disposed on the AGV, and the landing system comprises a loading channel suitable for passing an object delivered by the UAV through a first loading channel opening in the landing surface. The object passes within the loading channel through to a second loading channel opening at a bottom aspect of the AGV. In this way, a UAV can land on the landing surface, and the AGV positions the object in line with a target delivery location, where the object is delivered. Aspects of the landing system comprise an electromagnet or vacuum chamber for securing the UAV to the landing surface, thereby enhancing stability of the UAV during movement of the landing system.

An introduced autonomous aerial vehicle can include multiple cameras for capturing images of a surrounding physical environment that are utilized for motion planning by an autonomous navigation system. In some embodiments, the cameras can be integrated into one or more rotor assemblies that house powered rotors to free up space within the body of the aerial vehicle. In an example embodiment, an aerial vehicle includes multiple upward-facing cameras and multiple downward-facing cameras with overlapping fields of view to enable stereoscopic computer vision in a plurality of directions around the aerial vehicle. Similar camera arrangements can also be implemented in fixed-wing aerial vehicles.

Systems and methods related to landing unmanned aerial vehicles (UAVs) are provided. In one example, a method includes receiving a UAV on a surface of a landing platform. The method may further include operating a positioning device disposed under the surface to locate the UAV. The method may further include operating the positioning device to move the UAV to a location and / or an orientation on the surface. The UAV may comprise landing gear having a plurality of legs, where each leg comprises a shock absorption system. The method may further include operating the shock absorption system during the receiving operation to reduce force received at stress areas of the UAV, and after the receiving operation, operating the shock absorption system to dampen movement by the UAV. Related devices and systems are also provided.

A docking system is disclosed for an unmanned aerial vehicle (UAV). The docking system includes a base station that is configured to receive the UAV and a pedestal that is configured to support the base station in an elevated position. The pedestal defines an interior space that is configured in correspondence with an outer contour of the base station such that the base station is positionable within the pedestal to protect the base station during nonuse.

A drone system and method for deploying and autonomously refuelling. The drone system includes a base station and a drone. The base station and drone are configured for autonomous refuelling when the drone has landed in the base station. The base station also provides portability and security of the drone.

An introduced autonomous aerial vehicle can include multiple cameras for capturing images of a surrounding physical environment that are utilized for motion planning by an autonomous navigation system. In some embodiments, the cameras can be integrated into one or more rotor assemblies that house powered rotors to free up space within the body of the aerial vehicle. In an example embodiment, an aerial vehicle includes multiple upward-facing cameras and multiple downward-facing cameras with overlapping fields of view to enable stereoscopic computer vision in a plurality of directions around the aerial vehicle. Similar camera arrangements can also be implemented in fixed-wing aerial vehicles.

Technology for generating and displaying a graphical user interface for operating an unmanned aerial vehicle (UAV) is disclosed herein that generates and updates a representation of a spline flight path. In various implementations, a graphical user interface detects user interactions with a remote control device directing the flight control subsystem of the UAV to record keyframes and to compute a spline based on the keyframes during flight. The graphical user interface displays a real-time perspective of the UAV with a representation of the spline and the keyframes overlaying the view. The graphical user interface continually updates the representation as the UAV flies and when the spline is updated as the keyframes are updated.

Described herein are systems for automated docking of an unmanned aerial vehicle. For example, some systems include a landing surface configured to hold an unmanned aerial vehicle; a box configured to enclose the landing surface in a first arrangement of the dock and expose the landing surface in a second arrangement of the dock; and a retractable arm, wherein the landing surface is positioned at an end of the retractable arm and the retractable arm is configured to extend to move the landing surface outside of the box and contract to pull the landing surface inside of the box.

The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: receives LIDAR data generated from a LIDAR camera; measures plurality of dimensions of the home based upon processor analysis of the LIDAR data; builds a 3D model of the home based upon the measured plurality of dimensions; and displays a representation of the 3D model by visually navigating through the 3D model.

When a customer places an order for a first item and a second item, delivery systems utilize an aerial vehicle to transport the first item from a first source of the first item to a second source of the second item. At the second source, the first item and the second item are transferred into a carrier vehicle and transported toward a destination of the order. Once the carrier vehicle arrives within a vicinity of the destination, an autonomous ground vehicle departs the carrier vehicle with the first item and the second item, and delivers the first item and the second item to the destination. The delivery systems effectively expand the capacity of sources of items, and simplify processes for delivering orders for items, by enabling items from multiple sources to be combined at a single source prior to delivery, and transported to a destination from the single source.

A highly reliable and high-precision navigation and positioning method and system for UAV under GPS-DENIED conditions includes: obtaining measurements of inertial sensor and image of binocular camera, extracting and tracking point features of the image, and obtaining altitude value; calculating position and attitude of camera and depth of landmark point based on the parallax and baseline of the binocular camera; fusing the measurement data of the inertial sensor and the binocular camera, and optimizing the data to obtain high-precision position and attitude data; obtaining the altitude value of the ranging radar and performing four-degree-of-freedom position and attitude graph optimization after adding the detected keyframes when repeated occurrence of the UAV at the same location is detected; encapsulating optimized position and attitude data to form a pseudo-GPS signal, inputting the pseudo-GPS signal to the UAV for positioning.

An artificial intelligence (AI) entry management (EM) security system includes a camera, a microphone, a motion detector, a speaker, and a software platform. Geofences are utilized to create boundaries around physical areas, such as locations for package delivery or property boundaries. The EM device and other devices connected to the software platform are configured to monitor the geofences and detect a presence of an activity, an event, an object, or a device. At least one license plate reader is utilized to read license plate characters. Voice recognition and voice identification are used to grant or deny permissions for individuals on a property

A system for dynamic asset addition management is disclosed. The system includes a communication interface and processor(s). The processor(s) is / are configured to obtain data associated with task(s) being performed by a set of assets, wherein the set of assets comprises a plurality of drones and the plurality of drones is at least semi-autonomous; determine to modify the set of assets based at least in part on the data associated with the task(s), including: 1) determining one or more capabilities associated with remaining task(s); and 2) determining a modification to the set of assets based at least in part on (i) the one or more capabilities associated with the remaining task(s), and (ii) one or more drone capabilities respectively associated with the set of assets; and communicate, via the communication interface, an instruction to at least one drone, the instruction indicating the modification to the set of assets.

The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: receives LIDAR data generated from a LIDAR camera; measures a plurality of dimensions of a room of the home based upon processor analysis of the LIDAR data; builds a 3D model of the room based upon the measured plurality of dimensions; receives an indication of a proposed change to the room; modifies the 3D model to include the proposed change to the room; and displays a representation of the modified 3D model.

Aspects an autonomous driving system with air support are described herein. The aspects may include an unmanned aerial vehicle (UAV) in the air and a land vehicle on the ground communicatively connected to the UAV. The UAV may include at least one UAV camera configured to collect first ground traffic information and a UAV communication module configured to transmit the collected first ground traffic information. The land vehicle may include one or more vehicle sensors configured to collect second ground traffic information surrounding the land vehicle, a land communication module configured to receive the first ground traffic information from the UAV, and a processor configured to combine the first ground traffic information and the second ground traffic information to generate a world model.

An artificial intelligence (AI) entry management (EM) security system includes a camera, a microphone, a motion detector, a speaker, and a software platform. Geofences are utilized to create boundaries around physical areas, such as locations for package delivery or property boundaries. The EM device and other devices connected to the software platform are configured to monitor the geofences and detect a presence of an activity, an event, an object, or a device. At least one license plate reader is utilized to read license plate characters. Voice recognition and voice identification are used to grant or deny permissions for individuals on a property.

A method for controlling a plurality of drones to survey a location, the method comprising, at a computing system: automatically generating preliminary flight plans for a plurality of drones to survey the location based on a 3D model; receiving survey data from the plurality of drones as the plurality of drones are surveying the location based on the preliminary flight plans; updating the 3D model based on the survey data received from the plurality of drones; and automatically updating at least a portion of the flight plans based on the updated 3D model

Systems, apparatuses, methods, and computer program products are disclosed for facilitating extended range encrypted communication. An example method includes automatically navigating, via navigation circuitry of a first drone, to a first location of a first device. The example method also includes generating, by quantum random number generator circuitry of the first drone, a cryptographic key. The example method also includes establishing, by communications hardware of the first drone, a first connection between the first drone and the first device. The example method also includes causing transmission, by the communications hardware of the first drone, of the cryptographic key to the first device, such that the cryptographic key facilitates secure communication between the first drone and the first device.

A data processing solution for data generated by autonomous vehicles, unmanned aerial vehicles (UAVs), and other Internet of Things (IoT) devices may include a network of edge devices and one or more cloud servers to gather and process data. Initially, all data generated by the edge devices may be gathered, transported, and processed by the cloud server(s) to train machine learning models. The cloud server(s) may push machine learning (ML) models to the network of edge devices. Using the ML models running on the edge devices, the data gathered by the individual edge devices may be filtered by its relevance score, which correlates to the impact the data would have as training data. The relevance score is used by the edge device to identify the data to upload to the cloud.

A method comprising receiving a plurality of images of a scene captured by at least one drone; identifying features within the plurality of images; identifying similar images of the plurality of images based on the features identified within the plurality of images; comparing the similar images based on the features identified within the similar images to determine a proportion of features shared by the similar images; selecting a subset of the plurality of images that have a proportion of shared features that meets a predetermined range; generating a first 3D model of the scene from the subset of images using a first 3D model building algorithm; generating a second 3D model of the scene from the subset of images using a second 3D model building algorithm; computing errors for the first and second 3D models; and selecting as the model of the scene the first or second 3D model.

A UAV for transporting a payload comprising a vehicle body; a retractable rail exposed on an underside of the vehicle body; a retraction mechanism coupling the rail to the vehicle body for causing the rail to raise and lower relative to the vehicle body; and a barrier located on the vehicle body so as to confront the rail when the rail is in its raised position to block the removal from the rail of a payload slidably engaged with the rail. A mechanism for advancing a payload onto and along the rail and pushing it off.

A vehicle control system includes: an on-board control module, configured to control driving of the vehicle according to a control instruction sent by a vehicle dispatching command platform; a vehicle state module, configured to collect state information of the vehicle; an environment sensing module, configured to collect first road environment information of the vehicle; a UAV scanning module, configured to collect second road environment information of the vehicle; a data center module, configured to generate fusion information according to the state information, the first road environment information, and the second road environment information; a map module, configured to generate a driving route map of the vehicle according to the fusion information; and a vehicle dispatching command platform, configured to generate the control instruction according to the fusion information and the driving route map.

Systems and methods are disclosed for tracking objects in a physical environment using visual sensors onboard an autonomous unmanned aerial vehicle (UAV). In certain embodiments, images of the physical environment captured by the onboard visual sensors are processed to extract semantic information about detected objects. Processing of the captured images may involve applying machine learning techniques such as a deep convolutional neural network to extract semantic cues regarding objects detected in the images. The object tracking can be utilized, for example, to facilitate autonomous navigation by the UAV or to generate and display augmentative information regarding tracked objects to users.

An electronic apparatus and method for retransmission of signals using unmanned aerial vehicles (UAV) is disclosed. The electronic apparatus includes a controller to control a movement of a UAV to a location within a signal coverage area associated with a remote transmitter. The electronic apparatus further includes receiver circuitry disposed on the UAV to receive a first signal from the remote transmitter. The first signal corresponds to a first wireless communication standard. The electronic apparatus further includes a signal processor to process the first signal to obtain a second signal and transmission circuitry to control one or more antennas disposed on the UAV to transmit a beam of the second signal to an electronic device that includes one or more receivers. The second signal corresponds to a second wireless communication standard which is same as or different from the first wireless communication standard.

A drone station according to an embodiment of the present invention may comprise: a station main body (10) having, mounted thereon, a charging device (20) allowing a drone to land thereon so as to be charged with drone power; a cover (30) provided at the station main body (10) so as to open / close the charging device (20) while rotating around an axle (12); and a window panel (34) provided in the cover (30), and made of a nonconductor material to transmit a GPS signal therethrough.