A sewer inspection or maintenance system is provided having a sensor system with which at least three distances between a predetermined first point in the sewer pipe and the sewer wall of the sewer pipe are detected. The system has a processing unit adapted for determining a diameter of the sewer pipe and a predetermined second point in the sewer pipe based on the detected distances, and for determining a horizontal offset and a vertical offset between the predetermined first point and the predetermined second point based on the determined diameter and the detected distances. The system also has a positioning unit which is adapted to correct the position of an inspection unit in the sewer pipe by the two offsets. A corresponding method is provided as well.

A method is provided for autonomous anomaly management of an industrial site having industrial equipment in its field, including identifying industrial equipment to be inspected in the field, obtaining select autonomous sensor data about the identified industrial equipment from at least one mobile autonomous device routed along respective routes for accessing the identified industrial equipment, processing the autonomous sensor data to identify a potential anomaly, and in response to identifying a potential anomaly, taking action(s) to address the potential anomaly, wherein, as a combination, identifying the equipment, processing the autonomous sensor data, identifying the potential anomaly, and determining the action(s) use historical measurement and/or control data from the industrial equipment, historical autonomous sensor data, extrinsic data including at least one of guidance and/or constraint data about the industrial site, and supervisory and/or control data generated and/or collected by supervisory control of the industrial site disposed remote from the field.

Systems and methods are described for detecting changes at a location based on image data by a mobile robot. A system can instruct navigation of the mobile robot to a location. For example, the system can instruct navigation to the location as part of an inspection mission. The system can obtain input identifying a change detection. Based on the change detection and obtained image data associated with the location, the system can perform the change detection and detect a change associated with the location. For example, the system can perform the change detection based on one or more regions of interest of the obtained image data. Based on the detected change and a reference model, the system can determine presence of an anomaly condition in the obtained image data.

The present disclosure relates to a method for stochastic inspections on power grid lines based on unmanned aerial vehicle-assisted edge computing. According to the method, a stochastic distributed inspection unmanned aerial vehicle is adopted to acquire video images on a target power grid area, which can reduce funds and time costs of inspections. With assistance of superior unmanned aerial vehicle, a goal is to minimize energy consumption of an unmanned aerial vehicle system and extend operation time of the unmanned aerial vehicles under same payload conditions, while processing video image data collected from the inspection unmanned aerial vehicles. The near-far effect generated by communications between mobile unmanned aerial vehicles is eliminated by introducing a NOMA, and position coordinates, system resource allocations and task offload decision schemes are solved by using a method of combining a DDPG algorithm in a Deep reinforcement learning with a genetic algorithm.

The present disclosure relates to improvements in systems and methods in acquiring images via imaging devices, where such imaging devices can be configured, in some implementations, with an unmanned aerial vehicle or other vehicle types, as well as being hand-held. Images are acquired from the imaging devices according to capture plans where useful information types about a structure of interest (or objects, items, etc.) can be derived from a structure image acquisition event. Images acquired from capture plans can be evaluated to generate improvements in capture plans for use in subsequent structure image acquisition events. Capture plans provided herein generate accurate information as to all or part of the structure of interest, where accuracy is in relation to the real-life structure incorporated in the acquired images.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for an unmanned aerial system inspection system. One of the methods is performed by a UAV and includes obtaining, from a user device, flight operation information describing an inspection of a vertical structure to be performed, the flight operation information including locations of one or more safe locations for vertical inspection. A location of the UAV is determined to correspond to a first safe location for vertical inspection. A first inspection of the structure is performed is performed at the first safe location, the first inspection including activating cameras. A second safe location is traveled to, and a second inspection of the structure is performed. Information associated with the inspection is provided to the user device.

Methods, systems, and program products of inspecting solar panels using unmanned aerial vehicles (UAVs) are disclosed. A UAV can obtain a position of the Sun in a reference frame, a location of a solar panel in the reference frame, and an orientation of the solar panel in the reference frame. The UAV can determine a viewing position of the UAV in the reference frame based on at least one of the position of the Sun, the location of the solar panel, and the orientation of the solar panel. The UAV can maneuver to the viewing position and point a thermal sensor onboard the UAV at the solar panel. The UAV can capture, by the thermal sensor, a thermal image of at least a portion of the solar panel. A server onboard the UAV or connected to the UAV can detect panel failures based on the thermal image.

A UAV (unmanned aerial vehicle) including a first barometer and a processing unit is provided. The first barometer provides a first air pressure value. The processing unit is coupled to the first barometer for receiving the first air pressure value from the first barometer, performing timing-synchronization on the first air pressure value provided by the first barometer and an external reference air pressure value provided by an external reference barometer to obtain a timing-synchronized first air pressure value and recalculating the timing-synchronized first air pressure value to generate a compensated air pressure value, wherein the processing unit performs data fusion calculation on the first air pressure value, the compensated air pressure value and a sensor data to obtain a target fused data and real-timely controls the altitude and the posture of the UAV according to the target fused data.

The invention relates to a method of carrying out a departure inspection on an autonomous vehicle combination. In a method that shortens the inspection period, before departure of the vehicle combination (1), a first region (15) under the towing vehicle (3) of the vehicle combination (1) is checked for living beings and objects by means of a sensor system (13) fixedly installed on the towing vehicle (3), and a second region (21) under the trailer (5) of the vehicle combination (1) is checked by means of a mobile robot (19) comprising a further sensor system (15).

A method (600) for obtaining information about a structure (101) using a drone (102) equipped with a sensor system (103). The method includes, during a first period of time and while the drone's sensor system is pointing towards the structure, using (s602) the sensor system to obtain first depth data. The method also includes obtaining (s604) a first height value, Z1, indicating or being based on the height of the drone above a bottom point of the structure during the first period of time. The method also includes using (s606) the first depth data to determine a first vertical coordinate representing a top point of the structure. The method further includes estimating (s608) a height of the structure, wherein estimating the height of the structure comprises using the first vertical coordinate and the first height value, Z1, to estimate the height of the structure.