An unmanned aerial vehicle is described and includes a computer carried by the unmanned aerial vehicle to control flight of the unmanned aerial vehicle and at least one sensor. The unmanned aerial vehicle is caused to fly to a specific location within a facility, where the unmanned aerial vehicle enters a hover mode, where the unmanned aerial vehicle remains in a substantially fixed location hovering over the specific location within the facility and sends raw or processing results of sensor data from the sensor to a remote server system.

An access management system includes a mobile device with a processor and a memory and a software platform including at least a processor and a memory. The software platform is configured to analyze data obtained from the mobile device and other devices connected to the software platform. Specifically, the software platform is operable to determine if an access key received, read, or captured by a mobile device matches an access key for an authorized account, object, device, or space for the mobile device, and to provide access to the mobile device if the access key received, read, or captured by the mobile device matches the authorized access key.

An apparatus for supporting a ceiling mounted unmanned aerial vehicle docking station in a premises security system is provided for a premises including a ceiling grid and a support structure disposed above the ceiling grid. The apparatus includes an unmanned aerial vehicle docking station mounting bracket including a top surface and a bottom surface which is affixable to an exposed surface of a ceiling element in the ceiling grid by a first plurality of fastening elements. The bottom surface of the bracket is affixable to the ceiling mounted unmanned aerial vehicle docking station. The apparatus further includes a panel including a top surface and a bottom surface which is affixable to a concealed surface of the ceiling element by the first plurality of fastening elements. The top surface of the panel includes at least one anchoring element for physically coupling the panel to the support structure.

An unmanned vehicle (UV) navigation system is provided. The UV navigation system comprises a processor, a communication interface for communicating with a remote station, and a non-transitory memory device storing machine-readable instructions that, when executed by the processor, causes the processor to navigate the UV. The processor is configured to receive data from sensors, camera or data line for UV processor analysis, determine that a link-free trigger event has occurred, and autonomously navigate the UV in response to the trigger event.

Provided is a surveillance system employing a plurality of unmanned aerial vehicles (UAVs), the surveillance system showing improved surveillance performance while optimizing common energy consumption for computing of all the UAVs and also providing a stable visual monitoring service using autonomous mobility of the plurality of UAVs regardless of movement of an object to be monitored and action uncertainty of an adjacent UAV.

An unmanned system maneuver controller (USMC) includes an inertial navigation system (INS) for state estimation of the USMC in three-dimensional (3D) space, a communications device configured to communicate with an unmanned system, and a processor configured to receive, via the communications device, flight, maneuver, or dive data from the unmanned system, and generate flight, maneuver, or dive control instructions based at least on the flight, maneuver, or dive data and data received from the INS. The flight, maneuver, or dive control instructions are configured to pilot the unmanned system based on movement of the USMC in 3D space. A remote may selectively control an operation of the USMC. The USMC may be mounted to a weapon or observation device, such that movement of the weapon or observation device in 3D space controls a movement of the unmanned system. Additional systems and associated methods are also provided.

The present invention provides a system and method for identifying trash within a predetermined geographic boundary using Unmanned Aerial Vehicles (UAVs). The system comprises a UAV equipped with a camera, a wireless transceiver, and a first processor. The UAV communicates with a controller via a wireless network. The controller, which can be cloud-based or located on a computer connected to the wireless network, is configured to process image data captured by the camera using a neural network. The neural network helps to identify and geolocate trash within the images. The controller is further configured to route the UAV according to a preprogrammed flight path, plot the locations of any detected trash onto a map, and generate a report that can be made accessible to subscribers.

When an acceleration equal to or larger than a threshold value is exerted to a vehicle, an unmanned air vehicle takeoff and landing dock unlocks an unmanned air vehicle in a direction opposite to a direction in which the acceleration is exerted. When an acceleration detector of the unmanned air vehicle detects the acceleration equal to or larger than a threshold value, a flight controller of the unmanned air vehicle controls flight of the unmanned air vehicle to cause the unmanned air vehicle to ascend. Further, the unmanned air vehicle starts imaging by an imaging unit and starts transmitting imaging data acquired.

A system and method for dynamic probabilistic tracking of search targets within a search area generates a displayable occupancy map of the search area. The occupancy map is divided into individual map cells and seeded with a list of unlocated targets (e.g., targets of one or more types known or thought to be within the search area but which have not been precisely located therewithin). Each map cell of the occupancy map reflects the confidence or likelihood that an unlocated target will be detected within that cell. As a search mission proceeds, sensor datasets are received from the searching assets and correlated to identify unlocated targets. As unlocated targets are detected, located with sufficient accuracy, and removed from the list, the confidence levels of other map cells are reflected to indicate changes in the probability that any remaining unlocated targets will be detected therein.

A method of aerial surveillance including sending from a building management system (BMS), to a computer system, an alarm condition. The method further including programming, by the computer system, a drone with a route to a location of interest, wherein the route includes a portion of a map, the map including a data structure representation of a number of radio-frequency (RF) devices associated with one or more surveillance locations. The drone navigates to the location of interest by triangulating its position on the route using signals from the number of RF devices. The method further including sending, by the drone, to the computer system, sensor data including image data in response to arriving at the location of interest.