Provided is a drone with a multiple DOF flight mode according to the present invention. The drone may include: a fuselage in which a battery is mounted and a forward direction is set in an x-axis; a plurality of rotors disposed around the fuselage in four or more, each rotational axis of which is aligned in a z-axis direction; an x-axis tilting mechanism unit formed to tilt the plurality of rotors about an axis parallel to the x-axis; a y-axis tilting mechanism unit formed to tilt the plurality of rotors about an axis parallel to the y-axis; a first drive motor unit driving the y-axis tilting mechanism unit; a second drive motor unit guiding the x-axis tilting mechanism unit; and a control unit configured to implement a plurality of flight modes by controlling the first rotor, the second rotor, the third rotor, the fourth rotor, the first drive motor unit, and the second drive motor unit.

In some examples, a computing device receives, from an unmanned aerial vehicle (UAV), a first image from a first camera on the UAV and a plurality of second images from a plurality of second cameras on the UAV. The plurality of second cameras may be positioned on the UAV for providing a plurality of different fields of view in a plurality of different directions around the UAV. Further, the first camera has a longer focal length than the second cameras. The computing device presents, on a display, a composite image including at least a portion of the first image within a merged image generated from the plurality of second images. The presented composite image enables a user to at least one of: zoom out from the at least one first image to the merged image, or zoom in from the merged image to the at least one first image.

Provided is a control method of a drone with a multiple DOF flight mode according to the present invention. The drone may include a fuselage in which a battery is mounted and a forward direction is set in an x-axis, a plurality of rotors disposed about the fuselage in four or more, each rotational axis of which is aligned in a z-axis direction, an x-axis tilting mechanism unit formed to tilt the plurality of rotors about an axis parallel to the x-axis, a y-axis tilting mechanism unit formed to tilt the plurality of rotors about an axis parallel to the y-axis, a first drive motor unit driving the y-axis tilting mechanism unit, a second drive motor unit guiding the x-axis tilting mechanism unit, and a control unit configured to implement a plurality of flight modes by controlling the first rotor, the second rotor, the third rotor, the fourth rotor, the first drive motor unit, and the second drive motor unit.

An imaging system includes: an unmanned flight vehicle; an imager that is mounted on the unmanned flight vehicle and takes an image of a robot which performs work with respect to a target object; a display structure which is located away from the unmanned flight vehicle and displays the image taken by the imager to a user who manipulates the robot; and circuitry which controls operations of the imager and the unmanned flight vehicle. The circuitry acquires operation related information that is information related to an operation of the robot. The circuitry moves the unmanned flight vehicle such that a position and direction of the imager are changed so as to correspond to the operation related information.

An exemplary unmanned aerial vehicle (UAV) mountable to a conductor of an aerial power transmission line system includes a body having a rotor system, a motivation system attached to the body to motivate the UAV along the conductor, a battery carried by the body and electrically connected to at least one of the rotor system and the motivation system, a monitoring tool mounted with the body and an inductive coil carried by the body and in electric connection with the battery, wherein the inductive coil is configured to harvest electricity from the aerial power transmission line system and charge the battery.

A method and system for detecting a typical object of a transmission line based on UAV federated learning. The method includes: determining a detection model for a typical object of a transmission line by YOLOv3 object detection algorithm according to a prior database for the typical object; dividing a UAV network into multiple federated learning units; acquiring pictures, taken by the UAV network, of the typical object and tags corresponding to each picture to determine a training database; training, based on Horovod framework and FATE federated learning framework, each federated learning unit according to the training database and the detection model for the typical object, and determining the trained UAV network according to the trained federated learning unit; and determining, by the trained UAV network, the typical object in each picture. A congestion of communication links is avoided, thereby improving detection efficiency.

Methods and systems are described herein for generating composite data streams. A data stream processing system may receive multiple data streams from, for example, multiple unmanned vehicles and determine, based on the type of data within each data stream, a machine learning model for each data stream for processing the type of data. Each machine learning model may receive the frames of a corresponding data stream and output indications and locations of objects within those data streams. The data stream processing system may then generate a composite data stream with indications of the detected objects.

A method (400) of capturing and processing electroluminescence (EL) images (1910) of a PV array (40) is disclosed herein. In a described embodiment, the method 400 includes controlling the aerial vehicle (20) to fly along a flight path to capture EL images (1910) of corresponding PV array subsections (512b) of the PV array (40), deriving respective image quality parameters from at least some of the captured EL images, dynamically adjusting a flight speed of the aerial vehicle along the flight path, based on the respective image quality parameters for capturing the EL images (1910) of the PV array subsections (512b), extracting a plurality of frames (1500) of the PV array subsection (512b) from the EL images (1910); determining a reference frame having a highest image quality of the PV array subsection (512b) from among the extracted frames (2100); performing image alignment of the extracted frames (2100) to the reference frame to generate image aligned frames (2130), and processing the image aligned frames (2130) to produce an enhanced image (2140) of the PV array subsection (512b) having a higher resolution than the reference frame. A system, image processing device, and aerial vehicle for the method thereof are also disclosed.

A vehicle control system includes at least one imaging device attached to a vehicle and that captures multiple images, and a control circuit that generates a composite image from the multiple images and displays the composite image on a display unit. The vehicle is operated according to a user operation on a portion of the display unit on which the composite image is being displayed.

[Problem] To provide a flying body having a new structure capable of improving flight efficiency. [Solution] The problem is addressed by a flying body capable of traveling along at least a first direction and comprising an airframe part and an auxiliary part wherein the airframe part has a body part and a lift generating part, the body part having a right part and a left part extending along the first direction and a connecting part whereby the ends of the right part and the left part in a second direction opposing to the first direction are connected. The flying body is thus configured so as to create a surrounded space surrounded by the left part, the right part, and the connecting part when seen from a third direction perpendicular to the first direction.