An inspection apparatus includes one or more memories, and one or more processors. The one or more processors are configured to identify a first item as a target whose level of consumption is to be observed, and control a drive device to make the level of consumption of the first item, identified as the target, observable by an information acquisition device that observes the level of consumption of the target.

A system for capturing VIN numbers and vehicles images to track vehicle damage through vehicle supply chains which includes a mobile software application and/or robot(s) which moves autonomously around parking lots. The mobile application can direct the user to capture VIN images and/or vehicle images from certain views and collect GPS positions of the same and the robot includes various cameras and sensors to identify vehicles and take pictures of them. All of the captured images of vehicles are sent to a central server/storage where the vehicle images can be checked for damage as compared to locations so that it can be determined who was in possession of the vehicle when damage occurred.

A system for capturing VIN numbers and vehicles images to track vehicle damage through vehicle supply chains which includes a mobile software application and/or robot(s) which moves autonomously around parking lots. The mobile application can direct the user to capture VIN images and/or vehicle images from certain views and collect GPS positions of the same and the robot includes various cameras and sensors to identify vehicles and take pictures of them. All of the captured images of vehicles are sent to a central server/storage where the vehicle images can be checked for damage as compared to locations so that it can be determined who was in possession of the vehicle when damage occurred.

The invention is a flexible and configurable inspection system for the inspection of container units that combines and integrates a holding assembly for multiple containers integrating servo-controlled rotation of the units, transport and positioning of the containers that simulate human handling, and camera stations employing automated vision inspection. The system performs horizontal inspection for particulate and any other container defect that promotes particulate to better locate within the inspection area of the cameras. Inspection sequences and product recipes combine the typical manual inspection agitation with automated inspection rotational techniques to optimize detection. The system allows for semi-automatic operation with the operator at the front of the station feeding and out-feeding material manually or fully automated with conveyance system feeding and out-feeding material from the back of the station.

Disclosed herein are systems and methods for profiling a surface. In some embodiments, the systems and methods perform profiling using a robotic vehicle. The vehicle can include a drive system, one or more wheel encoders, and one or more distance sensors and/or inertial measurement units for capturing measurement data, such as the slope of the surface or the angle of the robotic vehicle relative to the surface or the gravity vector. A control computing system is included having one or more processors that execute instructions stored in software modules to process movement data. In some embodiments, the processed movement data determines a plurality of snapshots of the surface at different times and positions as the robotic vehicle traverses the surface. These snapshots are combined to generate a profile of the surface.

A flexible inspection system includes a robot with a plurality of scanners and a robot controller. The robot controller is configured to receive a vehicle inspection protocol (VIP) for a vehicle being assembled on an assembly line. The VIP includes checkpoints to be scanned on the vehicle and the checkpoints correspond to components installed on the vehicle and connections between components installed on the vehicle. The robot controller commands the robot to move the plurality of scanners per the VIP such that the checkpoints are scanned. A characteristic of each checkpoint is recorded and compared to a reference characteristic such that a pass or no-pass determination of each checkpoint is provided. A vehicle inspection report with the pass/no-pass determinations is provided to an operator such that operator inspections and/or repairs of the checkpoints are made.

The present approach relates to streaming data derived from inspection data acquired using one or more robots performing inspections of an asset or assets. Such inspections may be fully or partially automated, such as being controlled by one or more computer-based routines, and may be planned or dynamically altered in response to inputs or requirements associated with an end-user of the inspection data, such as a subscriber to the data in a publication/subscription distribution scheme. Thus, an inspection may be planned or altered based on the data needs or subscription levels of the user or customers.

Systems, methods, and related technologies for automated inspection are described. In certain aspects, one or more images of a reference part can be captured and the one or more images of the reference part can be processed to generate an inspection model of the reference part. One or more regions of the inspection model can be associated with one or more analysis parameters. An inspection plan can be generated based on the inspection model and the one or more analysis parameters. Based on the inspection plan, one or more images of a part to be inspected can be captured and the one or more images of the part can be processed in relation to the analysis parameters to compute one or more determinations with respect to the part. One or more outputs can be providing based on the one or more determinations.

A method according to the disclosure configures a processor to predict metal loss in a structure for remediation. The method uses a machine learning model, trained based upon historical data, to predict metal loss over locations of a structure at a time of the prediction. The method identifies from among the predicted locations a high-risk location on the structure in which a magnitude of metal loss indicates potential remediation being needed, dispatches a robotic vehicle to the high-risk location on the structure and inspects the high-risk location using the robotic vehicle to confirm whether the magnitude of metal loss at the location requires remediation.

In further methods, remediation is performed. In still further methods, a three-dimensional visualization of the structure is generated with an overlay which depicts predicted metal loss over the sections of the structure.

An inspection station identifies irregularities of surfaces of an object and a finishing station corrects irregularities identified. The inspection station includes a plurality of cameras configured to detect the irregularities under a light having a wavelength of ≥ about 380 nm to ≤ about 740 nm. The finishing station includes at least one robot, and the robot includes a light source configured to emit light having a wavelength of ≥ about 380 nm to ≤ about 740 nm, a camera, and an abrasive tool for correcting detected irregularities of the surfaces of the object.