An unmanned aerial vehicle (UAV) a fixed frame and a rotating arm pivotably coupled to the fixed frame at a central axis. The fixed frame includes peripheral propellers and corresponding motors for flying the UAV, and a central electronics enclosure for housing electronics used to control the UAV. The rotating arm is between the propellers and configured to rotate with respect to the fixed frame about the central axis. The rotating arm includes magnetic feet at a first end of the rotating arm and configured to perch and magnetically attach the UAV to a ferromagnetic surface, a docking station at the first end and configured to release and dock a releasable crawler, and a battery at a second end of the rotating arm opposite the first end and configured to supply power to the motors and the housed electronics, and to counterbalance the first end about the central axis.

A route setting apparatus (10) includes a first acquisition unit (130) and a route setting unit (140). The first acquisition unit (130) acquires specification information that specifies a plurality of points. When setting the plurality of points, inspectors superimpose each of the plurality of points on an inspection target. The route setting unit (140) takes, as a flight route of an aircraft, a line connecting points acquired by moving the plurality of points indicated by the specification information in the same direction and for the same distance. For example, the route setting unit (140) sets a temporary flight route by connecting the plurality of points indicated by the specification information. Then, the route setting unit (140) sets a flight route by moving the temporary flight route.

A mobile drone communication and control system comprises a mobile drone station configured to be driven to a work area; a scanning drone carried by the mobile station and configured to scan an image of the work area and transmit the scanned image to the mobile station via a private WIFI network; an edge computer configured to analyze the scanned images and select the failure suspect points with an exact position identified coordinates; an inspection drone configured to fly out to the identified points and to perform a detailed inspection and equipped with a high pressure water or an air jet cleaner to clean the suspect points for detailed images; and a remote pilot center configured to program the routing plans of the scanning drone, the inspection drone and the mobile drone station.

Embodiments describe a remote deployable transient sensory kit comprising a shipping container having a remote deployable transient sensory system. The sensory system includes a controller, a set of batteries configured according to a flight plan optimization associated with a building energy modeling mission, and a mobile device for wireless communication with the remote deployable transient sensory system. The mobile device includes a processor, a computer-readable memory comprising an application executable via the processor for collecting energy usage data and building characteristics via the remote deployable transient sensory system, and a transceiver configured for wireless communication with the remote deployable transient sensory system.

Embodiments describe computer-implemented methods for generating a continuously calibrated (C.sup.2) building energy model (BEM) associated with a built environment with one or more remote deployable transient sensory systems configured as autonomous or semi-autonomous drones. The method can include receiving, via a processor, from a remote deployable transient sensory system, a sensory dataset indicative of a building envelope feature disposed on an exterior surface of a built environment. The method includes modifying a 3-D model of the building envelope to include the building envelope feature, determining an energy loss characteristic associated with the building envelope feature based on the point cloud model, and generating the C.sup.2 BEM based on the point cloud model and the sensory dataset.

Systems and methods for tracking the location of a non-destructive inspection (NDI) scanner using scan data converted into images of a target object. Scan images are formed by aggregating successive scan strips acquired using one or two one-dimensional sensor arrays. An image processor computes a change in location of the NDI scanner relative to a previous location based on the respective positions of common features in partially overlapping scan images. The performance of the NDI scanner tracking system is enhanced by: (1) using depth and intensity filtering of the scan image data to differentiate features for improved landmark identification during real-time motion control; and (2) applying a loop-closure technique using scan image data to correct for drift in computed location. The enhancements are used to improve localization, which enables better motion control and coordinate accuracy for NDI scan data.

An unmanned aircraft (100) according to the present disclosure is equipped with a flight propeller (2) and includes a main body (1), a locomotion unit having an aquatic locomotion mechanism and a terrestrial locomotion mechanism independent of the flight propeller, and a connector that connects the main body and the locomotion mechanisms.

A system for determining work vehicle operational state includes an unmanned aerial vehicle (UAV) configured to fly relative to a work vehicle and an imaging device supported on the UAV. In this respect, the imaging device is configured to capture a plurality of images of the work vehicle as the UAV flies relative to the work vehicle, with each captured image depicting one of a plurality of inspection points on the work vehicle. Furthermore, the system includes a computing system configured is receive the plurality of captured images from the imaging device and access a plurality of stored images, with each stored image depicting one of the plurality of inspection points in an operational state. Additionally, the computing system is configured to compare each captured image to one of the stored images to determine when the corresponding inspection point is in the operational state.

A drone system for collecting structural condition data about a structure having an array of sensors disposed at various locations on the structure and methods of using such a drone system are disclosed herein. The drone inspection system leverages neural networks to calculate a drone flight path to classify the location of passive sensors and calculate a drone flight path to collect structural condition data about the structure using line of sight sensors for digital twin generation. Some of the sensors disposed on the structure may be passive sensors that comprise energy harvesters and must be energized to report the structural collection data to the drone. The drone inspection system may comprise an energy transfer module for energizing the passive sensor via the energy harvester.

An aerial utility meter read system and a custom utility meter read equipment carrying case are disclosed. The custom utility meter read equipment carrying case is configured to attach to a drone while securely carrying operable wireless radio-based utility meter read equipment used to conduct aerial utility meter reads.