Patent Publication Number: US-10775314-B2

Title: Systems and method for human-assisted robotic industrial inspection

Description:
BACKGROUND 
     The subject matter disclosed herein relates to asset inspection, and more specifically to human-assisted inspection of one or more assets by one or more robots. 
     Various entities may own or maintain different types of assets as part of their operation. Such assets may include physical or mechanical devices, structures, or facilities which may, in some instances, have electrical and/or chemical aspects as well. Such assets may be used or maintained for a variety of purposes and may be characterized as capital infrastructure, inventory, or by other nomenclature depending on the context. For example, assets may include distributed assets, such as a pipeline or an electrical grid as well as individual or discrete assets, such as an airplane, a wind turbine generator, a radio tower, a steam or smoke stack or chimney, a bridge or other structure, a vehicle, and so forth. Assets may be subject to various types of defects (e.g., spontaneous mechanical defects, electrical defects, as well as routine wear-and-tear) that may impact their operation. For example, over time, the asset may undergo corrosion or cracking due to weather or may exhibit deteriorating performance or efficiency due to the wear or failure of component parts. 
     Typically, one or more human inspectors may inspect, maintain, and repair the asset. For example, the inspector may locate corrosion on the asset, may locate and quantitatively or qualitatively assess cracks or defects on the asset, may assess an asset for the degree of wear-and-tear observed versus what is expected, and so forth. However, depending on the location, size, and/or complexity of the asset, having one or more human inspectors performing inspection of the asset may take away time for the inspectors to perform other tasks or may otherwise be time consuming and labor intensive, requiring personnel time that might be more productively spent elsewhere. Additionally, some inspection tasks may be dull, dirty, or may be otherwise unsuitable for a human to perform. For instance, some assets may have locations that may not be accessible to humans due to height, confined spaces, or the like. Further, inspections may be performed at times that are based on schedules resulting in either over-inspection or under-inspection. 
     However, developing a fully autonomous inspection system may involve collecting extremely large amounts of data to assemble training data sets to train the system, as well as acquiring one or more computing systems with significant processing power to run the system. As a result, developing a fully autonomous inspection system may utilize a significant investment of resources. 
     BRIEF DESCRIPTION 
     Certain embodiments commensurate in scope with the originally claimed subject matter are summarized below. These embodiments are not intended to limit the scope of the claimed subject matter, but rather these embodiments are intended only to provide a brief summary of possible embodiments. Indeed, the disclosure may encompass a variety of forms that may be similar to or different from the embodiments set forth below. 
     In one embodiment, an asset inspection system includes a robot and a server. The robot collects inspection data corresponding to an asset. The server, includes a user interface, a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive the inspection data from the robot, display the inspection data via the user-interface, receive feedback on the inspection data via the user interface, generate a human-assisted inspection based on the received feedback, analyze the inspection data via a trained model, generate an automated inspection based on the analysis by the trained model, combine the automated inspection and the human-assisted inspection to generate an inspection report, and transmit the inspection report for review. 
     In another embodiment, an asset inspection system includes a server. The server includes a user interface, a processor, and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive inspection data from a robot, display the inspection data via the user-interface, receive feedback on the inspection data via the user interface, generate a human-assisted inspection based on the received feedback, analyze the inspection data via a trained model, generate an automated inspection based on the analysis by the trained model, combine the automated inspection and the human-assisted inspection to generate an inspection report, and transmit the inspection report for review. 
     In a further embodiment, a method of inspecting an asset includes collecting, via a robot, inspection data related to an asset, transmitting the inspection data to a server, displaying the inspection data via a user-interface, receiving feedback on the inspection data via the user interface, generating a human-assisted inspection based on the received feedback, analyzing the inspection data via a trained model, generating an automated inspection based on the analysis by the trained model, combining the automated inspection and the human-assisted inspection to generate an inspection report, and transmitting the inspection report for review. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: 
         FIG. 1  illustrates an asset inspection system and various types of assets suitable for inspection, in accordance with an embodiment; 
         FIG. 2  is a schematic of a robot of the asset inspection system of  FIG. 1 , in accordance with an embodiment; 
         FIG. 3  is a block diagram of a remote server of the asset inspection system of  FIG. 1 , in accordance with an embodiment; 
         FIG. 4  is a schematic illustrating interactions between the robot and the remote server of the asset inspection system of  FIG. 1 , in accordance with an embodiment; 
         FIG. 5  is a schematic illustrating an example of a trained model as shown in  FIG. 4 , in accordance with an embodiment; and 
         FIG. 6  is a flow chart of a process for performing a human-assisted inspection via the robot of  FIG. 1 , in accordance with an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     One or more specific embodiments will be described below. In an effort to provide a concise description of these embodiments, all features of an actual implementation may not be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers&#39; specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure. 
     When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Furthermore, any numerical examples in the following discussion are intended to be non-limiting, and thus additional numerical values, ranges, and percentages are within the scope of the disclosed embodiments. 
     As discussed herein, the present approach relates to human-assisted inspections of assets using robots, unmanned vehicles, or drones and/or inspections implemented by automated or computer-based routines. By way of example, such inspections may be performed using unmanned or robotic devices, such as ground-based mobile robots, including those with legs, wheels, tracks, etc., unmanned aerial vehicles (UAVs), including fixed wing and rotary wing vehicles, unmanned submersible vehicles (USVs), which may swim or move along the floor of the body of liquid, or other autonomously moving vehicles that may be characterized as drones or robots. For simplicity, to the extent the terms “drone” and “robot” are used herein, though it should be appreciated that this terminology is intended to encompass all variations, of UAVs, USVs, robotic devices, and so forth that are capable of programmable movement with limited human oversight. Such programmable movement can be based on either locally generated path waypoints or guidance or path guidance and waypoints generated by a remote system and communicated to the robot. Thus, as used herein, such devices move during an operational phase or period with limited human intervention or oversight. In accordance with present approaches, such devices may be operated to move along a flight plan, along which the devices acquire inspection data, such as video or still image data, LIDAR data, acoustic data, spectroscopic data, temperature or pressure data, chemical samples, smells, or other data that can be acquired by sensors or cameras that can be affixed to a device moving along the flight plan. In general, such inspections may be performed on one or more assets including, but not limited to, power generation assets, communication assets, transportation assets, mining or underground pumping assets, manufacture or construction assets and so forth. 
     Though the phrase “flight plan” is used generally herein, it should be appreciated that this phrase does not necessitate aerial movement, but instead relates to any one-dimensional (1D) (such as along a track), two-dimensional (2D) (such as along a defined or undefined planar route), or three-dimensional (3D) (such as movement in the air, under water, or on a structure in where depth or altitude is also traversable), or four-dimensional (4D) (such as where there are defined temporal aspects that may characterize a velocity, acceleration, or a time on station at a waypoint) path or route along which a drone moves as part of an inspection plan. Thus, a “flight plan” as used herein may be characterized as any 1D, 2D, 3D, or 4D route or path along which device such as a drone or robot is moved to perform a sensor-based inspection of an asset. Such a path may be adaptive, as discussed herein, and may consist of one or more waypoints along which the robot proceeds in an ordered fashion, with the sequence and location of the waypoints defining the path or route. It should be appreciated that such a flight plan may also incorporate not only temporal and/or spatial locations, but also orientation and/or alignment instructions for movement along the path and/or to exhibit at a given waypoint. Thus, the flight plan may also specify parameters such as roll, pitch, and yaw for the drone to exhibit at different points along the flight plan as well as two- or three-dimensional alignment characteristics that may relate to the direction in which a sensor or camera is pointing at a point along the flight plan. Thus, the flight plan may address not only where or when a robot is with respect to an inspection site but, at a given location or waypoint, the direction the robot is facing or otherwise oriented with respect to. Further, even at the same waypoint and orientation, images may be acquired at different magnifications, wavelengths, or other optical parameter such that effectively the image constitutes a different view. As discussed herein, the present approach facilitates the inspection of assets by acquired sensor data gathered during an inspection. 
     In addition, in accordance with certain aspects, prior knowledge may be leveraged in the inspection process. For example, prior knowledge may be used in generating or modifying an adaptive flight plan. In certain aspects, machine learning approaches may be employed to learn from human reviewer decisions (e.g., regarding asset condition, data sufficiency, decision oversight, mission planning, etc.), thereby creating a trained artificial neural network based on this prior knowledge that can facilitate future data sufficiency decisions. 
     To facilitate explanation and provide useful real-world context, various examples such as wind turbine generators, radio transmission towers, smokestacks, and so forth are provided herein. It should be appreciated however that such examples are provided merely to facilitate explanation, and the present approach is suitable for use with a wide range of other assets and at various other types of sites. Thus, the present approach is not intended to be limited to the context of the present examples. 
     With the preceding in mind, and turning to the figures,  FIG. 1  depicts aspects of an inspection system  10  employing one or more robots  12  suitable for inspecting one or more assets  14 , such as a wind turbine generator, radio tower, smokestack, or other suitable asset. 
       FIG. 1  also depicts a remote server  16 , accessible via a cloud  18  (e.g., a network interface for accessing one or more remote servers, virtual machines, etc. for storage, computing, or other functionality), which may communicate with the one or more robots  12  to coordinate operation of one or more robots  12 , such as for inspection of an asset  14 . In one embodiment, the robot(s)  12  have onboard cellular or network connectivity and can communicate with the remote server  16  at least prior to beginning an inspection. In certain implementations the cellular or network connectivity of the robot(s)  12  allow communication during an inspection, allowing inspection data to be communicated to the remote server  16  and/or allowing the remote server  16  to communicate feedback to a given robot  12 . 
     As shown, in some embodiments, the system  10  may also include a docking station  20  (e.g., robot garage), disposed on or near the asset  14 , for short term or long term storage of the robot  12  before and/or after inspection. In some embodiments, the docking station  20  may be in communication with the remote server  16  via the cloud  18 . If the robot  12  relies on a battery for power, the docking station  20  may also include a power source for charging the robot&#39;s  12  battery. 
     In the depicted example, the remote server  16  is a remote computing device accessible by the robot(s)  12  via the cloud  18 . Though only a single remote server  16  is shown in  FIG. 1 , it should be understood that the functions performed by the remote server  16  may be performed by multiple remote servers  16  and/or by virtualized instances of a server environment. In the instant embodiment, the remote server  16  includes a data processing system  22 , which may include a memory component  24  and a processor  26 , for processing data received from the robot  12 . As is described in more detail below, in some embodiments, the robot  12  may provide raw data to the remote server  16  for processing. In other embodiments, the robot  12  may pre-process or partially process the data before passing it to the remote server  16 . In further embodiments, all of the data processing may be performed by the robot  12 . 
     The remote server  16  also includes a searching/parsing component  28 , which may also include a memory  24  and a processor  26 , for searching, parsing, and otherwise interacting with data stored on the remote server  16 . A user interface  30  may receive inputs from a user. For example, the data processing system  22  may utilize machine learning (e.g., a trained artificial neural network) that uses inputs from a user provided via the user interface  30 . The user inputs may be supervisory in nature (e.g., the user monitors the robot and provides guidance as the robot performs the inspection), corrective (e.g., the user corrects the robot when the robot does something incorrectly), reinforcing (e.g., the user tells the robot when the robot does something correctly), decisive (e.g., the user makes a decision for the robot when prompted), instructive (e.g., identifying features in the asset), etc. In some embodiments, the user may interrupt the robot&#39;s inspection with feedback. In other embodiments, the robot may prompt the user by requesting feedback. A network interface  32  facilitates communication between the robot(s)  12  via the cloud  18 . As shown, the remote server  16  may store and maintain one or more databases  34 . These databases  34  may include inspection data, configuration files, models of assets and/or areas surrounding assets, task files, algorithms, etc. 
     Following an inspection, or during the inspection, the robot  12  may send inspection data to the remote server  16  for processing, analysis, and/or storage. By way of example, videos, images, LIDAR data, depth sensor data, acoustic data, spectroscopic data, or other relevant sensor or camera data acquired by the one or more robots  12  during an inspection may be uploaded to the database  34  as acquired or as a batch after an inspection flight plan is completed. Alternatively, in other implementations, the inspection data may be provided to the database  34  by other means or channels, such as via direct transmission from the robot  12  and/or via other intermediary communication structures, such as a dedicated inspection data communication circuit. 
     In the depicted example, the data processing system  22 , the database  34 , and the searching/parsing component  28  are depicted as part of a single or common processor-based system. However, the depicted functionalities may be implemented in a distributed or dispersed manner, with certain aspects being local to the asset  14 , to the robot  12 , to an operational facility and/or other locations remote from the asset  14 . In such distributed implementations, the depicted aspects may still be communicatively linked, such as over one or more network connections. 
     In the illustrated embodiment, the data processing system  22  may utilize machine learning to train an algorithm or an artificial neural network based on user feedback provided via the user interface  30 . That is, as feedback is provided via the user interface, the data processing system  22  learns how to conduct inspections and process data such that less and less human supervision is needed over time. 
       FIG. 2  is a schematic of an embodiment of the robot  12  shown in  FIG. 1 . It should be understood, however, that other embodiments of the robot  12  are envisaged having additional components, fewer components, and/or different combinations of components. As shown, the robot  12  includes a power supply  100  to provide power for the operation of the robot  12 . The power supply  100  may include a replaceable or rechargeable battery, a combustion engine, a generator, and electric motor, a solar panel, a chemical-reaction based power generation system, etc., or some combination thereof. 
     The robot may include a user interface  102 , by which a user may set up or adjust various settings of the robot  12 . The user interface may include one or more input devices (e.g., knobs, buttons, switches, dials, etc.) and in some cases may include a display (e.g., a screen, array of LEDs, etc.) for providing feedback to the user. Though previously discussed embodiments receive user feedback via the user interface of the remote server, embodiments in which user feedback is provided via the user interface  102  of the robot  12  are also envisaged. 
     A network interface  104  enables communication with the remote server via the cloud, or other devices (e.g., the docking station, a remote controller, a smart phone, a computing device, a tablet, etc.). For example, the network interface  104  may enable communication via a wireless network connection, a wired network connection, cellular data service, Bluetooth, Near Field Communication (NFC), ZigBee, ANT+, or some other communication protocol. In some embodiments, data sent or received via the network interface may be encrypted. For example, standard data encryption techniques may be utilized, such as hashing (e.g., MD5, RIPEMD-160, RTRO, SHA-1, SHA-2, Tiger, WHIRLPOOL, RNGss, Blum Blum Shub, Yarrowm etc.), key exchange encryption (e.g., Diffie-Hellman key exchange), symmetric encryption methods (e.g., Advanced Encryption Standard (AES), Blowfish, Data Encryption Standard (DES), Twofish, Threefish, IDEA, RC4, Tiny Encryption algorithm, etc.), asymmetric encryption methods (e.g., Rivest-Shamir-Adlemen (RSA), DAS, ElGamal, Elliptic curve cryptography, NTRUEncrypt, etc.), or a combination thereof. 
     A sensing system  106  may include one or more sensors  107  (e.g., tactile, chemical, ultrasound, temperature, laser, sonar, camera, a red, blue, green, depth (RGB-D) camera, etc.) configured to sense various qualities and collect data corresponding to the asset during inspections. 
     A drive system  108  may actuate movement of the robot  12  through the air, through a liquid, along a surface, or some combination thereof. As shown, the drive system  108  may include one or more motors  110  and one or more encoders  112 . The one or more motors  110  may drive propellers, legs, wheels, tracks, etc. The one or more encoders  112  may sense one or more parameters of the one or more motors  110  (e.g., rotational speed) and provide data to a control system  114 . 
     The control system  114  may include one or more memory components  116  and one or more processors  118 . A motion control system  120  may receive a signal from the one or more encoders  112  of the drive system  108  and output a control signal to the one or more motors  110  to control the movement of the robot  12 . Similarly, a data collection control system  122  may control the operation of the sensing system  106  and receive data from the sensing system  106 . A data processing and analysis system  124  may receive data collected by the sensing system  106  and process or analyze the collected data. In some embodiments, the data processing and analysis system  124  may completely process and analyze the data and make a determination as to the condition of the asset. In other embodiments, the data processing and analysis system  124  may perform pre-processing of the data or a partial processing and analysis of the data and then send the data to the remote server for the remainder of processing and analysis. In further embodiments, the robot may send raw data to the remote server. When user feedback is provided, the data processing and analysis system may take the user inputs into account when processing and/or analyzing the inspection data. In some embodiments, user feedback may be communicated back to the robot  12 . 
     The control system  114  may also include a mission planning component  126 . The mission planning component  126  generates a mission plan and executes the mission plan by coordinating the various other components of the control system  114  and the robot  12 . In some embodiments, the mission planning component  126  may request additional files or data from the remote server to complete mission plan. 
       FIG. 3  generally illustrates a block diagram of example components of a computing device  200  that could be used as the remote server. As used herein, a computing device  200  may be implemented as one or more computing systems including laptop, notebook, desktop, tablet, or workstation computers, as well as server type devices or portable, communication type devices, such a cellular telephones, and/or other suitable computing devices. 
     As illustrated, the computing device  200  may include various hardware components, such as one or more processors  202 , one or more busses  204 , memory  206 , input structures  208 , a power source  210 , a network interface  212 , a user interface  214 , and/or other computer components useful in performing the functions described herein. 
     The one or more processors  202  are, in certain implementations, microprocessors configured to execute instructions stored in the memory  206  or other accessible locations. Alternatively, the one or more processors  202  may be implemented as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and/or other devices designed to perform functions discussed herein in a dedicated manner. As will be appreciated, multiple processors  202  or processing components may be used to perform functions discussed herein in a distributed or parallel manner. 
     The memory  206  may encompass any tangible, non-transitory medium for storing data or executable routines, including volatile memory, non-volatile memory, or any combination thereof. Although shown for convenience as a single block in  FIG. 3 , the memory  206  may actually encompass various discrete media in the same or different physical locations. The one or more processors  202  may access data in the memory  206  via one or more busses  204 . 
     The input structures  208  are used to allow a user to input data and/or commands to the device  200  and may include mice, touchpads, touchscreens, keyboards, and so forth. The power source  210  can be any suitable source for providing power to the various components of the computing device  200 , including line and battery power. In the depicted example, the device  200  includes a network interface  212 . Such a network interface  212  may allow communication with other devices on a network using one or more communication protocols. In the depicted example, the device  200  includes a user interface  214 , such as a display configured to display images or date provided by the one or more processors  202 . As will be appreciated, in a real-world context a processor-based systems, such as the computing device  200  of  FIG. 3 , may be employed to implement some or all of the present approach, such as performing the functions of the remote server shown in  FIG. 1 . 
       FIG. 4  is a schematic illustrating interactions between the robot  12  and the remote server  16 . As previously described, the robot  12  and the remote server  16  communicate with one another via respective network interfaces (e.g., communication servers). As previously described, the robot  12  conducts the inspection according to the mission plan. Data  300  is then output. Though  FIG. 4  shows raw data  300  being output by the robot. In some embodiments, the robot  12  may perform some conditioning, pre-processing, or processing before transmitting the data  300 . As previous described, the data  300  may be output while the inspection is ongoing (e.g., continuously or in batches), or after the inspection is complete. The data  300  is dispatched via two channels: to the user interface  30  for human review, and to a robotic inspection analytics component  302  of the data processing system  22  for automated inspection analysis. In some embodiments, the data  300  may also be copied to an inspection data database  34  for storage. 
     For human inspection, data  300  (e.g., raw data, pre-processed data, processed data, etc.) are displayed to the user via the user interface  30 . For example, the user may use the user interface  30  to review images, video, plots, graphs, spreadsheets, scores, etc. The user may provide feedback via the user interface  30 . For example, the feedback may be concerned with the condition of the asset, the sufficiency of the collected data, how the inspection was performed, whether more inspection is needed, etc. The human inspection may be performed at the beginning of an asset inspection, continuously throughout inspection, of following inspection. For example, in some embodiments, the user may provide feedback at the beginning of the inspection. A trained model  306  of the data processing system  22  may then be trained based on the inputs to improve the model. Such that less and less human input is needed as the inspection goes on. The human assisted inspection results are packaged into a standard description file and provided to a fusion component  308  for combination with the automated inspection results. 
     For automated inspection analysis, data  300  is received by the robotic inspection analytics component  302  and analyzed using the trained model  306 . Analysis of the data  300  may be with regard to, for example, the condition of the asset (e.g., detecting corrosion, recognizing an anomaly, etc.), the sufficiency of the collected data, how the inspection was performed, whether more inspection is needed, etc. The inspection algorithms used by the robotic inspection analytics component  302  may be fixed or flexible, and may include deep learning, cascade, DPM, intensity, etc. The trained model  306  may be pre-trained using pre-collected training data and runs continuously without human intervention. The automated inspection results are packaged into a standard description file and provided to the fusion component  308  for combination with the human-assisted inspection results. The automated inspection results may be provided to the fusion component  308  continuously, when each processing cycle is done, periodically in batches, at the end of inspection, etc. 
     The fusion component  308  combines the human-assisted inspection results with the automated inspection results and generates an inspection report  310 . In one embodiment, the human-assisted inspection results and the automated inspection results are labeled as either bounding boxes or pixels. The two sets of results may be quickly combined via the bitwise logic operation AND and OR. Wherein AND is focused on the common recognition results between robots and humans and OR is focused on unifying the recognition results from robots and humans. The bitwise logic operation provides fast and efficient processing for some use cases. In another embodiment, a weighted operation may be utilized wherein the human-assisted inspection results and the automated inspection results are be given weights and combined to determine confidence scores. For example, the regions and pixels may be labeled as anomalies with corresponding confidence scores. The confidence scores for each of the human-assisted inspection results and the automated inspection results may be multiplied by the respective weights and added together to determine the overall confidence score. In some embodiments, a clustering based post processing may be performed following the weighted operation to avoid small and/or noisy regions. The weighted operation helps to adaptively incorporate results from humans and robots. The weights may reinforce the regions with high confidence and inhibit the regions with low confidence. In another embodiment, a Kalman-filter may be utilized to probabilistically combine the recognition results from humans and robots. The Kalman-filter is normally used to deal with very noisy and uncertain environments in an on-line estimation process. In a further embodiment, a decision-making operation may be utilized wherein rules are pre-stored on the system. IF, AND/OR, THEN are then used to assign each region a result of inspection. The decision-making operation is based on a knowledge base where the robot can retrieve rules from. The rules can be updated when humans&#39; intervention is given. 
     The human assisted inspection  304  acts as a supplement to the automated inspection prepared by the robotic inspection analytics component  302 . Early on, the feedback provided by the user via the user interface  30  that constitutes the human assisted inspection  304  may be substantial. The feedback may include identification features (e.g., corrosion, cracks, decay, damage, etc.) in the asset, feedback as to the sufficiency of the collected data (e.g., lighting, ambient noise, wind, malfunctioning sensors, etc. may affect inspection data collection), feedback as to how the robot conducted the inspection, etc. The feedback provided is used to periodically retrain the trained model  306  such that a training data set is developed and modified over time as inspections are performed. As the size of the training data set increases, and as the trained model  306  learns more and more, less and less human feedback is provided during inspection. Accordingly, in some embodiments, inspections may be performed without any user feedback. That is, the human assisted inspection  304  may not contain any modifications to the automated inspection. In such cases, the fusion component  308  may just convert the automated inspection into the inspection report  310 . As such, as time progresses, the automated inspection may account for more and more of the inspection report until, for some inspections, the automated inspection may account for the entirety of the inspection report  310 . 
     The inspection report  310  generated by the fusion component  308  may be saved in a reports database  34  and passed to a data streaming server  32  (e.g., network interface). The data streaming server  32  then provides the inspection report  310  to one or more users, customers, or clients for review. 
       FIG. 5  schematically depicts an example of the trained model  306  (e.g., artificial neural network) that may be trained as a deep learning model as discussed herein. In this example, the network  306  is multi-layered, with a training input  400  and multiple layers including an input layer  402 , hidden layers  404 ,  406 , and so forth, and an output layer  408  and the training target  410  present in the network  306 . Each layer, in this example, is composed of a plurality of “neurons” or nodes  412 . The number of neurons  412  may be constant between layers or, as depicted, may vary from layer to layer. Neurons  412  at each layer generate respective outputs that serve as inputs to the neurons  412  of the next hierarchical layer. In practice, a weighted sum of the inputs with an added bias is computed to “excite” or “activate” each respective neuron of the layers according to an activation function, such as rectified linear unit (ReLU), sigmoid function, hyperbolic tangent function, or otherwise specified or programmed. The outputs of the final layer constitute the network output  408  which, in conjunction with a target value or construct  410 , are used to compute some loss or error function  414 , which will be backpropagated to guide the network training. 
     The loss or error function  414  measures the difference between the network output  408  and the training target  410 . In certain implementations, the loss function may be a mean squared error (MSE). Alternatively, the loss function  414  could be defined by other metrics associated with the particular task in question, such as a softmax function. 
     With the preceding in mind, the neural network  306  may be trained for use in the analysis of data in a manner that facilitates ranking or identification of relevant features based on their relevance to a given task(s) and, potentially, selection of some or all of these features for a given application or use. In particular, the present disclosure describes an approach to process or analyze asset inspection data. 
       FIG. 6  is a flow chart of a process  500  for conducting a human-assisted robotic inspection of an asset. In block  502 , one or more robots are used to collect inspection data. The inspection data may include, for example, video or still image data, LIDAR data, acoustic data, spectroscopic data, temperature or pressure data, chemical samples, smells, or other data that can be acquired by sensors or cameras that can be affixed to a device moving along the flight plan. 
     At block  504 , the collected data is transmitted (e.g., via the cloud) to a remote server. The data received by the remote server may be raw, conditioned, pre-processed, partially processed, or processed, etc. At block  506 , the received data is directed to an automated inspection channel. At block  508  the inspection data may be analyzed using a trained model. At block  510 , the analyzed data is incorporated into an automatic inspection report. 
     At block  512  the received data is directed to a human inspection channel. At block  514 , the user reviews the data via a user interface and provides feedback. The provided inputs may include, for example, decisions, positive reinforcement, negative reinforcement, instructions, etc. At block  516  a human-assisted inspection is generated. In some embodiments, the user inputs may be used to train or retrain the model (block  518 ). 
     At block  520 , the human-assisted inspection and the automated inspection are combined to create an inspection report. As previously described, this combination may be accomplished via bitwise logic operation, weighted operation, a decision making operation, or a combination thereof. At block  522 , the inspection report is sent out to one or more users, customers, or clients for review. 
     The presently disclosed techniques utilize one or more robots to perform a human-assisted inspection of an asset. The one or more robots may be in communication with a remote server. The robot transmits inspection data to the remote server for analysis. Automated analysis of the inspection data is performed using a trained model, resulting in an automated inspection. The inspection data is also displayed to a user via a user interface. The user may provide feedback via the user interface. Based on the feedback received, a human-assisted inspection is generated. The user feedback may also be used to periodically train or retrain the trained model. The automated inspection and the human-assisted inspection are then combined to generate an inspection report, which may be transmitted for review by the user, customer, or client. 
     This written description uses examples to disclose the claimed subject matter, including the best mode, and also to enable any person skilled in the art to practice the disclosed subject matter, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the claimed subject matter is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.