The present invention provides an unmanned aerial vehicle (100) comprising: a flight system (4) for producing thrust to manoeuvre the unmanned aerial vehicle (100). The flight system (4) comprises: one or more flight rotors (42) defining a plane passing through each flight rotor and a thrust direction generally perpendicular to the plane; and one or more electric motor (44) for driving the one or more flight rotors (42). The unmanned aerial vehicle (100) further comprises: a cargo area for coupling to or receiving a load (200); and a load system (6) for providing thrust additional to the thrust provided by the flight system (4) to thereby lift a load attached to the connection point. The load system (6) comprises: a first gas turbine propulsion system; and a controller configured to control the flight system (4) and load system (6).

An unmanned aerial vehicle with deployable components (UAVDC) is disclosed. The UAVDC may comprise a fuselage, at least one wing, and at least one control surface. In some embodiments, the UAVDC may further comprise a propulsion means and/or a modular payload. The UAVDC may be configured in a plurality of arrangements. For example, in a compact arrangement, the UAVDC may comprise the at least one wing stowed against the fuselage and the at least one control surface stowed against the fuselage. In a deployed arrangement, the UAVDC may comprise the at least one wing deployed from the fuselage and the least one control surface deployed from the fuselage. In an expanded arrangement, the UAVDC may comprise the at least one wing telescoped to increase a wingspan of the deployed arrangement.

A system that is mountable to an unmanned vehicle and a method of operation is provided. The system includes an attachment plate configured to couple to the unmanned vehicle, the attachment plate having a first feature. A control module is configured to removably couple to the attachment plate, the control module having one or more processors and a power source, the control module having a pin arranged to move from a first position to a second position when the control module is coupled to the attachment plate, the one or more processors being energized when the pin is moved from the first position to the second position. A payload having an energetic element is provided, the payload being coupled to the control module.

A system and method usable for receiving data from an optical device and converting the data to a condition value, apply analytical tools to the condition value as determined by the optical device in real time, or substantially real time, analyze data, and generate automated reports in real time, or substantially real time at the location and time that condition values are received.

The battery thermal management system includes a battery pack, a circulation subsystem, and a heat exchanger. The system can optionally include a cooling system, a reservoir, a de-ionization filter, a battery charger, and a controller.

Stations for a drone are described as well as a monitoring system that is configured to monitor a property using one or more drones. The drone is launched from a docking station and configured to navigate the property to perform operations to monitor the property. The docking station is located at an area of the property. The docking station includes a landing surface that is parallel to a particular area of the property that supports the docking station. A positioning surface of the docking station slopes toward the landing surface. The positioning surface, including its slope, is configured to receive the drone and guide the drone toward the landing surface.

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.

An approach for determining an infrastructure service interruption is disclosed. The approach relies on utilizing UAVs (unmanned aerial vehicle) to map electronic signals (e.g., Wi-Fi, etc.) that emanates from building structures (e.g., residential, commercial, etc.). Electronic signals having a certain frequency or multiple frequencies may be used. Essentially, the approach can detect power/signal loss by comparing differences in Wi-Fi signal maps pre and post event (e.g., severe thunderstorm, etc.). The 24/7 event monitoring is carried out by using UAVs and the UAVs can operate on a regular or event driven schedule vs. continuously operating multiple fixed data collection units.

A method of deploying an unmanned aerial vehicle (UAV) operation system may be provided. A method may include estimating an amount of traffic for one or more routes based on a demand of the one or more routes. The method may also include determining a required number of docking stations for each route of the one or more routes based on the estimated amount of traffic for the route, a distance of the route, and a maximum travel distance for a UAV. Further, the method may include installing the required number of docking stations for each route of the one or more routes, wherein each docking station of the required number of docking stations including at least one of a power supply, a wireless charger, a communication module, a control module, and a camera.