Systems, devices, and methods for identifying a target aerial vehicle, deploying an interceptor aerial vehicle comprising at least one effector, maneuvering the interceptor aerial vehicle to a position to engage a target aerial vehicle, deploying the at least one effector to intercept the target aerial vehicle, and confirming that the target aerial vehicle has been intercepted.

Provided is a user equipment for controlling a drone. The user equipment analyzes an original video to control the drone to photograph a reproduction video giving a feeling identical to or similar to the original video. An electronic device may be connected to an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 5G service, and the like.

An information processing device determines, based on location information of a plurality of delivery destinations for package delivery: one or a plurality of first delivery destinations for package delivery by a vehicle; and one or a plurality of second delivery destinations for package delivery by a drone mounted on the vehicle. In addition, the information processing device determines: a travel route including a route for the vehicle to perform package delivery to the one or plurality of first delivery destinations; a first point on the travel route for the drone to start flying from the vehicle to the one or plurality of second delivery destinations; and a second point on the travel route for the drone to return to the vehicle.

The present invention relates to a mobile security robot equipped with a micro flight device, which uses a camera mounted on the mobile security robot to patrol a predetermined area by the mobile security robot capable of autonomous driving and to patrol an area where the mobile security robot cannot move by the mounted micro flight device. Accordingly, there is an advantage in that it can efficiently patrol a much wider area compared to the patrol using only the mobile security robot.

An anti-collision system for an aircraft and an aircraft including the anti-collision system are disclosed including a sensor data processing unit configured to process data received from multiple sensors installed on a tow tug to detect objects around the aircraft, and output information about detected objects; a safeguarding box building unit configured to generate, based on an aircraft geometry database, a three-dimensional safeguarding box for the aircraft; and a risk assessment unit configured to update the safeguarding box based on data corresponding to different operation modes of the tow tug, calculate relative distances between the detected objects and the aircraft based on the information about the detected objects that is output from the sensor data processing unit, and determine whether there is a collision risk between the aircraft and an object, among the detected objects based on the updated safeguarding box. The system is configured to output an alarm or a warning when there is the collision risk.

Provided is a detection and classification system and method for small unmanned aircraft systems (sUAS). The system and method detect and classify multiple simultaneous heterogeneous RC transmitters/sUAS downlinks from the RF signature using Object Detection Deep Convolutional Neural Networks (DCNNs). The method further utilizes not only passive RF, but may also utilize Electro Optic/Infrared (EO/IR), radar and acoustic sensors as well, with a fusion of the individual sensor classifications. Detection and classification with Identification Friend or Foe (IFF) of individual sUAS in a swarm, multi-modal approach for high confidence classification, decision, and implementation on a low C-SWaP (cost, size, weight and power) NVIDIA Jetson TX2 embedded AI platform is achieved.

A controller for an unmanned aerial vehicle (UAV) comprising an image capture means, the controller comprising: inputs arranged to receive: positional data relating to the UAV, a vehicle and a user device; image data captured by the image capture means; a processor arranged to process the received positional data to determine the relative locations of the UAV, vehicle and user device; an output arranged to output a control signal for controlling the UAV and to output an image signal comprising captured image data; wherein the processor is arranged to: generate the control signal for the UAV such that the image data captured by the image capture means comprises at least an image of an obscured portion of the vehicle that is obscured from a field of view of a user of the user device.

In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

A terrestrial acoustic sensor array for detecting and preventing airspace collision with an unmanned aerial vehicle (UAV) includes a plurality of ground-based acoustic sensor installations, each of the acoustic sensor installations including a sub-array of microphones. The terrestrial acoustic sensor array may further include a processor for detecting an aircraft based on sensor data collected from the microphones of at least one of the plurality of acoustic sensor installations and a network link for transmitting a signal based on the detection of the aircraft to a control system of the UAV.

System and method for determining distance in navigation of an electric aircraft is illustrated. The system and method comprise a sensor and a computing device. The sensor is configured to detect a surface and transmit at least a first signal and a first frequency and at least a second signal at a second frequency to a computing device, wherein the first signal and the second signal comprise a corresponding distance. The computing device is configured to receive a returned signal from the sensor, wherein the returned signal comprises an intermodulation product associated to the first signal and the second signal, detect an amplitude of the returned signal as a function of the frequency, identify a distance datum as a function of the amplitude and an amplitude threshold, determine an aircraft adjustment as a function of the distance datum, and transmit the distance datum and aircraft adjustment to a remote device.