System and method for interactive cognitive task assistance

A cognitive assistant that allows a maintainer to speak to an application using natural language is disclosed. The maintainer can quickly interact with an application hands-free without the need to use complex user interfaces or memorized voice commands. The assistant provides instructions to the maintainer using augmented reality audio and visual cues. The assistant will walk the maintainer through maintenance tasks and verify proper execution using IoT sensors. If after completing a step, the IoT sensors are not as expected, the maintainer is notified on how to resolve the situation.

BACKGROUND

The present disclosure relates to systems and methods for producing and maintaining physical structures, and in particular to a system and method for providing users guidance to perform tasks on physical structures.

2. Description of the Related Art

Traditional maintenance activity uses paper maintenance manuals that the user (maintainer) references in order to look up the proper maintenance tasks and to follow each step of those tasks. As a consequence, the maintainer must locate the paper maintenance manual, find the appropriate portion of the manual related to the desired maintenance task, determine which tools and other resources are required (often located elsewhere) and obtain those tools and resources. The maintainer must then reference the paper maintenance manual for each step, perform the step, then re-reference the paper maintenance manual to determine the next step. That the maintainer must constantly stop work to reference the manual instructions for each step extends the time require to perform the task and increases the chances of errors on the part of the maintainer. In sum, paper maintenance manuals must be stored and retrieved, and cannot easily be searched and queried, and requires the maintainer to move between the maintenance task and the associated manual, slowing down the maintenance process.

Although digital maintenance manuals exist and alleviate some of these drawbacks by making the manual portable and searchable, the maintainer must still have free hands to interact with the digital maintenance manual. A digital maintenance manual may include a video, but such videos typically run either faster or slower than the maintainer's ability to perform the steps, hence the maintainer must constantly start and restart (or rewind) the playback of the video.

Systems are available that improve upon such digital maintenance manuals. For example, augmented reality has been used to guide maintainers through maintenance steps, using augmented reality glasses. One such system has been proposed by Bavarian Motor Works, as described in “End of the Mechanic? BMW Smart Glasses Make it Possible for Anyone to Spot and Fix a Car Engine Fault Just by Looking at It,” by Victoria Woollaston, Daily Mail, published Jan. 21, 2014, which is hereby incorporated by reference herein. An example of a similar system is described in “Augmented Reality for Maintenance and Repair (ARMAR),” by Steve Henderson and Steven Feiner, Columbia University Computer Graphics and User Interfaces Lab, 2016, also incorporated by reference herein. This discloses the use of real time computer graphics, overlaid on and registered with the actual repaired equipment to improve the productivity, accuracy, and safety of maintenance personnel by use of head-worn, motion-tracked displays augment the user's physical view of the system with information such as sub-component labeling, guided maintenance steps, real time diagnostic data, and safety warnings. Such systems may use smart helmets such as those available from DAQRI, described in “Daqri Smart Helmet,” by Brian Barrett, Wired, Jan. 7, 2016, also incorporated by reference herein.

Unfortunately, such existing systems do not resolve many maintenance issues. For example, user interaction with the BMW system involves a voice interaction limited to the maintainer providing verbal commands such as “next step.” The system does not determine the task to be performed nor the steps of that task using ordinary language requests from the user. Consequently, for a given problem, the maintainer must still determine which task to perform (e.g. there is no diagnosis nor any feature that allows the user to a goal to be achieved in ordinary language to find the proper task or task steps. The ARMAR and DAQRI systems are similarly deficient in this respect.

The foregoing systems also fail to monitor the performance of the steps (to provide feedback confirming that the step has been properly performed or indicating that it has not, with corrective action provided) or to provide data logging the performance of the step. For example, the step to be performed may be to tighten a nut to a bolt to a particular torque.

With respect to monitoring the performance of the steps to provide feedback, none of the foregoing systems sense whether maintainer has failed to tighten the nut to the proper specification, whether the maintainer is using the proper tool, whether the user has failed to align the nut on the threads of the bolt before tightening it. Such errors are unlikely to be timely discovered, and if discovered, would waste time, as they may require disassembly or performing the task steps in reverse order to allow the error to be corrected. Also, none of the foregoing systems can sense whether the step is being performed properly while the user is performing the step, and thus prevent damage to either the tools used, or the structure being worked on.

With respect to logging, none of the foregoing systems record any data regarding performance of the step. Such data can be used to further refine maintenance procedures, or estimate of how long such procedures should take. The data may also be used to identify causes of subsequent failures (e.g. subsequent failures were more common when a nut from a particular vendor was torqued to the upper end of a torque specification).

SUMMARY

To address the requirements described above, this document discloses a system and method for providing guidance to perform a task having at least one step performed on a physical structure at a station. In one embodiment, the method comprises receiving, in a guidance processing unit, a command from a performance entity, the command invoking the task, determining, in the guidance processing unit, the at least one step from the command, transmitting, from the guidance processing unit to the performance entity, instruction data illustrating performance of the at least one step, receiving, in the guidance processing unit, real time sensor data generated by a sensor proximate the physical structure sensing performance of the step, and computing a performance measure of the step according to the sensor data.

Another embodiment is evidenced by a system for providing guidance to perform a task having at least one step performed on a physical structure at a station, in which the system comprises a sensor proximate the physical structure, a presentation device, and a guidance processing unit comprising a processor communicatively coupled to a memory storing instructions comprising instructions. The instructions include instructions for receiving a command from a performance entity, the command invoking the task, determining the at least one step from the command, transmitting, instruction data illustrating performance of the at least one step to the performance entity for presentation by the presentation device, receiving real time sensor data generated by the sensor proximate the physical structure sensing performance of the step, and computing a performance measure of the step according to the sensor data.

DESCRIPTION

In the following description, reference is made to the accompanying drawings which form a part hereof, and which is shown, by way of illustration, several embodiments. It is understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present disclosure.

Overview

The system and method described herein improves the performance and efficiency of a maintainer of a platform or other physical structure by assisting the maintainer in locating correct maintenance procedures, guiding the maintainer through each maintenance step using audio and visual queues, and validating the correctness of each step through the use of sensor feedback. In one embodiment, the sensors are integrated via the Internet of Things (IoT). The system provides the capability to (1) use visual and other IoT sensors to perform initial diagnostics to select the appropriate work order for the maintainer (2) automatically collect images and data associated with maintenance operations to provide a sensor validated audit trail rather than relying on maintainer manual input (3) automatically collect performance and cycle time metrics showing how long maintenance steps take to perform to identify process improvement opportunities, including maintainer training opportunities. In one embodiment, the system operates under voice control using natural language to issue commands and to control the maintenance environment. For example, the operator can audibly request that a light be turned on, rather than reaching for a light switch.

The system and method adds several cognitive capabilities to a new maintenance tool including speech-to-text, text-to-speech, natural language processing, machine learning and augmented reality. These capabilities allow the maintainer to interact with the maintenance tool using natural spoken commands without the need to memorize exact voice commands. The tool also leverages natural language processing and machine learning to determine the intent of voice commands and react accordingly to such commands. Feedback from the tool is presented to the maintainer using hands-free augmented reality providing natural language audio commands and 3D visual information overlaid onto real-world objects.

The tool aggregates of several distinct capabilities to make an end system more powerful than each individual component. In one embodiment, the tool combines IoT, Cognitive Natural Language Processing, and advanced document indexing and querying. This allows the maintainer to easily access all knowledge required to perform maintenance quickly and effectively. The tool also makes the maintenance operations completely hands-free by conveying information as a combination of audio and 3D visual cues through an augmented reality environment. The tool also adds cognitive capabilities and IoT feedback to existing maintenance tools that otherwise requires mostly manual and unverified maintenance steps. The addition of the cognitive capabilities allows the maintainer to locate relevant maintenance information in a fast and efficient manner, and adding IoT feedback verifies the proper completion of each step reducing rework.

Although described primarily in terms of performing maintenance tasks, the tool is equally at home in production applications, or anywhere where tasks are performed on physical structures, including manufacturing and quality control. For example, the techniques described below are applicable to the assembly and testing of physical structures including automobiles, aircraft, spacecraft and water vessels.

FIG. 1is a diagram depicting an exemplary maintenance/assembly/test (MAT) facility100(hereinafter simply referred to as a facility or MAT100. The MAT100has one or more stations102A-102N (hereinafter alternatively referred to as stations102) at which tasks are performed. Each station102includes a physical structure106upon which maintenance is performed, parts are assembled/disassembled, or tests are being performed). The stations102may also comprise one or more tools110A-110N (alternatively referred to hereinafter as tool(s)110) that are used to perform the tasks on the physical structure106. Such tasks can be performed by one or more users such as person(s)108P or robot(s)108R (hereinafter alternatively referred to as user(s)108).

One or more of the physical structure106, tools110and user108include one or more sensors (collectively referred to as sensors112) which measure or monitor a characteristic of the associated physical structure106, tools110, or user108, respectively. For example, the physical structure106may include one or more physical structure sensors112B which sense a characteristic of the physical structure106. This characteristic may include a physical characteristic, such as the position or angle of an appendage relative to another portion of the physical structure106, an electrical characteristic, such as a voltage or current measurement on a conductor or the physical structure106, or any other quality measurable by a sensor of any type. The physical structure sensors112B may include sensors that are part of the completed assembly of the physical structure106, or physical structure sensors112B that are affixed to the physical structure106for maintenance or production purposes and later removed before assembly or maintenance is completed. For example, the physical structure106may comprise a flight control surface such a rudder that includes an integral potentiometer that measures the position of the rudder for purposes of navigation and control. In this example, this potentiometer may be used as one of the physical structure sensors112B of the physical structure106not only for the operational assembly, but for testing purposes as well. In other embodiments, another physical structure sensors112B may be attached to the physical structure for performing the MAT operation. For example, a separate potentiometer may be affixed to the rudder, and rudder position measurements with this sensor may be compared to the measured position of the rudder by the integral potentiometer.

Similarly, one or more of the tools110each may include one or more sensors112DA-112DN that are used to sense or measure a characteristic of the associated tool110A-110N, respectively. That characteristic may likewise include one or more of a physical characteristic, electrical characteristic or any other quality measured by the sensors112. For example, tool110A may comprise a torque wrench and the sensor112DA may measure the torque being imparted upon a portion of the physical structure106such as a bolt or nut by the torque wrench. Such sensors112D may also include temperature sensors (to monitor the temperature of the tool110during use).

Likewise, one or more of the users108may comprise or use one or more sensors112. The user(s)108may comprise a person108P or a robot108R, for example. The robot108R may include one or more robot sensors112F to sense a characteristic of the robot108R one or more characteristics of the other elements of the station102A (including the physical structure106tools110, or person108P). In one embodiment, the robot108R includes a plurality of potentiometers, which provide an indication of the relative position of the structures of the robot108R, and from which the position of the head or working surface of the robot108R may be determined. This can be used, for example, to determine the position of the working end of the robot108as well as any of its structures as a function of time. In another embodiment, the robot108R includes a camera or other visual sensor dispose at or near the working end, so that visual representations of the region surrounding the working end may be obtained. Sensors112F may be integrated with the robot108R (e.g. with sensor measurements being used by the robot108R to control robot responses to commands) or may be added to the robot108R only for use at the station102to perform MAT operations. Such robot sensors112F may also include temperature sensors (to monitor the temperature of the robot108R or portions of the robot108R during use).

As another example, the person108P may wear one or more sensors112E. Such sensors112E may include, for example, an augmented reality headset. Such headsets typically comprise a stereoscopic head-mounted display (providing separate images for each of the person's eyes), and head motion tracking sensors. Such motion tracking sensors may include, for example, inertial sensors such as accelerometers and gyroscopes, structured light systems, and eye-tracking sensors. When the augmented reality headset is worn by the person108P, the person can view their surroundings, but stereoscopic images are imposed upon those surroundings. Such stereoscopic images can include, for example, portions of the physical structure106or the changes to the physical structure106called for by steps of the task. The inertial sensors and eye sensors can be used to determine the direction the user is looking in inertial space, and images of the physical structure superimposed on those images.

Because the augmented reality headsets not only record video images, but also present video images superimposed on real images, such headsets can be regarded not only as sensors, but also presentation elements of the augmented reality headset114B which present information to the user108. The station102A may also include a more conventional presentation device such as a display114A, for displaying instruction information.

The sensors112E may also include other sensors112E such as appendage mounted inertial sensors such as accelerometers or gyros, which can measure the inertial state of the person's appendages. In some embodiments, the sensors may include sensors to monitor the person108P, such as sensors that measure temperature or heart rate. The information provided by such sensors are useful in determining if the tasks being performed by the person108P are particularly difficult.

The station102A may also include environmental sensors112A. Environmental sensors112A are sensors that measure characteristic of the environment of the station102. This may include, for example, the sensors that measure ambient temperature or humidity (e.g. using a thermometer and hygrometer), visible sensors that determine the physical location or proximity of any of the elements of the station102to each other including elements of the physical structure106, the tools110, the user108or guidance processing unit104. Environmental sensors112A may include elements that are disposed on other elements of the station102A. The environmental sensors112A may comprise passive, active, or semi-active systems. For example, one embodiment of an active environmental sensor may comprise a reflector positioned on another element of the station102(e.g. an arm of the robot108R), an illuminator that illuminates the reflector, and a visual sensor that measures the position of the illuminated sensor. An example of a passive environmental sensor is a visual sensor such as a video or still camera, which may be sensitive to visible, ultraviolet, or infrared wavelengths. Environmental sensors112A may also include radio frequency identification (RFID) systems, which can be used to identify the physical structure106and its characteristics.

Any or all of the sensors112are communicatively coupled to a guidance processing unit104(indicated by the Ⓢ symbols), permitting the guidance processing unit104to receive the data from the sensors. Further, the guidance processing unit104may be communicatively coupled to the guidance processing units of other stations102B-102N and to a central processor120(as indicated by the {circle around (B)} symbols).

FIG. 2is a diagram of one embodiment of the station102. The station102includes the guidance processing unit104, one or more sensors112, effectors202, presentation devices114(which include the display114A and presentation elements of the augmented reality headset114B), and the physical structure106.

The guidance processing unit104receives sensor data from the sensors112and in some embodiments, provides sensor commands to the sensors112as well. Such commands may include, for example, commands regarding the resolution or active range of the sensors112. The guidance processing unit104also sends commands and receives data from effectors. Such effectors might include, for example, a stepper motor that controls one of the sensors112or the robot108R.

In the illustrated embodiment, the guidance processing unit104includes an interface206communicatively coupled to a processor208. Sensors112provide sensor data to the processor208via interface206. Further, sensors112may receive commands from the processor via interface206. Similarly, the processor208, through the interface206, provides commands to effectors202and may received data from effectors202as well.

The guidance processing unit104provides instruction data illustrating performance of the steps performed on the physical structure to complete tasks to the presentation devices114, and may also provide commands to control the sensors112via the interface206. Likewise, presentation devices114may provide commands or information to the guidance processing unit104.

The guidance processing unit104comprises a processor208is communicatively coupled to one or more memories storing processor instructions, which when executed, cause the guidance processing unit104to perform the operations described below. The processor208may include multiple processors208and such processors208may be located remotely from one another. In an embodiment described further below, processor208comprises distributed processing elements.

FIGS. 3A-3Bare diagrams depicting one embodiment of exemplary process steps that can be used to guide the user108in the completion of tasks involving one or more steps on physical structures106. In block302, the user108or performance entity transmits a command invoking a task to be performed on the physical structure106. In block304, the command is received by the guidance processing unit104.

In one embodiment, this command is a hands-free (e.g. voice) command that is sensed by an audio sensor and provided to the guidance processing unit104, where the audio command is recognized by a speech recognition module and translated into text. Such voice commands may be in a fixed command language (where the onus is on the user108to learn the command syntax and phrasing required by the guidance processing unit104) and natural language (where the onus is on the guidance processing unit104to interpret the voice commands and translate them into a syntax and phrasing needed to search for the appropriate task and steps. Fixed command languages can include domain-specific training accomplished by training software components that translate the user's speech to text.

In other embodiments, the command comprises a digital command via a controller device communicatively coupled to the guidance processing unit104, such as a remote control, computer keyboard, mouse, game controller, or touch screen display. In another embodiment, the command is sensed by a monitoring system, and translated into a digital command. For example, the command may be wholly or partially implemented using gestures performed by the user108, sensed by an imaging sensor (for example, the environment sensor112A) and provided to the guidance processing unit, where such gestures are analyzed, interpreted, and translated into digital commands.

In still other embodiments, the command is a digital command received via a system-to-system message from control system for the robot108R or other robots at other stations102or the central processor120.

Next, the guidance processing unit104determines one or more steps to be performed from the received command. The received commands may be in many different forms. In one embodiment, the command comprises a generalized goal rather than a specific task. For example, the user108may issue a command “air conditioning is not functional.” Given this command, the guidance processing unit104determines what problems with the physical structure may be the cause of non-functional air conditioning. In making this determination, the guidance processing unit104may accept input from an on-board diagnostic (OBD)-type sensor. The guidance processing unit104then determines one or more tasks responsive to the command, and determines at least one step from the determined task. For example, in the case of the failed air conditioning example, the guidance processing unit may generate a plurality of tasks, each for checking each component of the air conditioning system as well as a task for diagnosing which of the components is defective. Each task may have one or more subtasks hierarchically below each task. At the bottom of the hierarchy of tasks and subtasks are the steps, which represent a unit of activity suitable for specific instructions to the user108. In the foregoing example, the step may be to remove a single screw of the air conditioning compressor, or to remove a subsystem of the air conditioning compressor. The hierarchical level at which steps are defined may depend on the complexity of the step and the experience of the user108. For example, the voice command may include an indication of how experienced the user108is, and the hierarchical level of steps defined according to this level of experience. As defined further herein, the guidance processing unit104may store performance data indicating how well the user108has performed steps or tasks, and this information can be used to determine the level of experience of the user108, resulting in steps or instructions suitable for the user's level of experience. In this case, the user108may be determined by user input (e.g. typing or speaking the user's name), via RFID technologies, or other means.

In one embodiment, determining the task from the received command comprises generating a database query from the received command using a natural language interpreter. Such interpreters allow users to issue commands in plain conversational language. The words in the command are parsed, and the parsed words are analyzed for syntax, semantics, discourse and speech. The result is a database query in the appropriate language and syntax. This query is provided to a database communicatively coupled to the guidance processing unit (e.g. the central processor120), and the task is determined from the result of the database query. Once the task is identified, a query may be generated based on the task to retrieve the appropriate steps to perform, subject to the user's experience and abilities.

In one embodiment, the task is one of a plurality of tasks to be performed on the physical structure106, and the database query is further determined according to current context data. Such context data comprises, for example, information about other of the plurality of tasks performed on the physical structure106and constraints on the task imposed by the physical structure itself, the environment (e.g. the position of other elements or physical structures, temperature, humidity, performance limitations of the tools110). For example, a task may have been performed on the physical structure106that changes the steps needed to perform another particular task. Returning to the example of the air conditioning system, another task may have resulted in the removal of a subsystem or part, making the removal of that subsystem or part in the current task unnecessary. Conversely, a step or task may have been performed that will make additional steps necessary to perform the current task. For example, if a previous task involved gluing a component, the current task may require waiting a period of time for that glue to set. Performance measures from previously completed tasks may also be used to alter or modify the definition of the steps to be performed on the current task. For example, if a previous task included the step of tightening a nut on a bolt to a particular torque, the measured torque applied to the bolt (performance measure of that previous step) could be used to estimate the torque require to remove that nut for a subsequent task.

In other embodiments, the task may be determined using stored information relevant to the job being performed, such as a work order or other information. The task (and steps) may also be determined based on constraints imposed by the state of the physical structure106or the environment. For example, a previous task may have disassembled at least a portion of the physical structure106, in which case, the steps needed to disassemble the portion of the physical structure are not required. Conversely, a previous task may have modified the physical structure106in such a way that additional steps need to be performed. As another example, a step may be required to be performed at a given ambient temperature. If the ambient temperature is too low, the task may include the step of increasing the ambient temperature to a higher value, a step that would not be required if the ambient temperature were sufficient. As another example, a voltage measured on a potentiometer of the physical structure may depend on the ambient temperature. The environment sensors112A may be used to sense this temperature, and determine the proper setting of the potentiometer based on such ambient temperature. Other environmental constraints may include, for example, the location of other elements of the station102, such as the arm of the robot108R, tools110or other physical structures, because the location of these structures may prevent disassembly of the physical structure106. In this case, the environmental sensors may include visible light sensors that sense the location of the physical structure106and nearby elements. Also, the environment may include which tools110are available at the station102, and which ones must be retrieved from other locations for the task to be completed. In this case, the environmental sensors112A may include RFID tags on the tools.

Returning toFIG. 3A, the guidance processing unit then transmits instruction data illustrating the step(s), as shown in block308. As shown in block310, the instruction data is received by one or more presentation devices114(which may include display114A and/or the presentation elements of an augmented reality headset114B, or a speaker or other audio sensor (not illustrated)). In the case of visual presentation devices, a visual representation of the step is presented. In one example, the visual representation of the step is presented on the display114A. In another example, the visual representation of the step is presented in augmented reality via the presentation elements of the augmented reality headset114B.

In one embodiment, the instruction data illustrating the step(s) comprises a visual representation of the step for presentation in augmented reality via the augmented reality headset. Augmented reality headsets typically comprise a stereoscopic head-mounted display (providing separate images for each eye), two loudspeakers for stereo sound, and head motion tracking sensors (which may include gyroscopes, accelerometers and structured light systems). Some augmented reality headsets also have eye tracking sensors. By use of the head (and optionally, eye) tracking sensors, the augmented reality headset is aware of its location and orientation in inertial space, and provides this information to the guidance processing unit104. The guidance processing unit104uses this information to determine what the user108should be viewing, and can super-impose other images on the image presented to the user108. For example, if the user108is looking at the physical structure106, the guidance processing unit104can highlight a particular part that must be physically manipulated to perform the instruction on the displays provided in the augmented reality headset114B The user108can therefore be made aware of specifically which actions must be completed for each part of the physical assembly, and is particularly useful, as the guidance processing unit104does the work of matching the illustrated step with background images seen by the user108. It also eliminates errors, as the user108is less likely to mistake a portion of the physical structure106for another (e.g. the user will not loosen the incorrect bolt). The instruction data also typically comprises audio information (e.g. a verbal description of the steps to be performed or aural representation of what the physical structure should sound like during or after performing the step), and presentation elements of the augmented reality headset114B typically presents this information using the loudspeakers in the augmented reality headset114B. In one embodiment, the verbal instructions are provided in natural language (e.g. ordinary human conversational speech). Such natural language instructions may be in any language (e.g. English, German, Chinese, etc.). Video and/or audio instructions may be provided on a variety of devices, including mobile computing devices such as cellphones or tablet computers, as well as desktop computing devices.

The user108receives the presented instruction data illustrating the step, and commences performing the step, as shown in block312. This is typically accomplished by a person108P, but may also be accomplished by the robot108R, or with the person108P working in conjunction with the robot108R, with the person108P and the robot108R each performing their subset of the steps, or with the person108P and the robot108R working together on one or more of the steps.

While the step is being performed, sensor data that senses performance of the step is generated, as shown in block314. The sensor data is transmitted, as shown in block316and received by the guidance processing unit104, as shown in block318. The sensor data is used to monitor the performance of the step, for example to determine progress of the step and when and if the step has been completed. The sensor data may also be used to determine when the user108has begun actually performing the step (useful later in computing the time it took the user108to complete the step). The sensor data may also be used to store data indicating the performance of the step over time. Such data may be useful in diagnosing failures at a later time.

This sensor data may be generated by any one or combination of sensors112at the station102. Such sensors112can observe:

One or more states of the physical structure106upon which the task is being performed: This can be accomplished using physical structure sensors112B integral or attached to the physical structure itself or environmental sensors112A. The physical structure sensors112B and environmental sensors112A may include visual and/or non-visual sensors. For example, in one embodiment, the environmental sensors112A include visual sensors that visually observe the state of the physical structure106, using object and pattern recognition techniques similar to those used in self-driving automobiles. Such sensors112B may include embedded sensors and RFID tags.

One or more states of the performance entity or user108performing the task: Sensor(s)112E for measuring such states may include head-worn devices including audio sensors, imaging and video sensors, inertial measurement sensors such as gyros and accelerometers, and personal sensors such as heart rate monitors;

One or more states of devices (e.g. the tools110, test equipment and parts) used to perform the task: This can be accomplished using sensors112DA-112DN mounted on or integrated with the tools110, or the environmental sensors112A, in the same way as the environmental sensors may observe the physical structure. Such tools can include RFID tags, or embedded tool sensors;

One or more states of devices that are collaborating on the task: This may include, for example, the state(s) of the robot108R, as measured by robot sensor(s)112F; or

One or more states of the surrounding environment in which the task is being performed: This may include, for example, environmental sensors112A sensing the temperature of the station102or any element thereof, the humidity of the station, power consumption of the station, or the location of the station102elements as a function of time. Such environmental sensors may include imaging sensors, audio sensors, temperature sensors, and humidity sensors.

In one example, the step is for the user108to tighten a nut on a bolt using a tool110A that is a torque wrench. The torque wrench includes a torque sensor112DA that senses the amount of torque being exerted by the tool110A. In this embodiment, the torque sensor112DA measures the torque being applied to the physical structure106, and transmits sensor data including the measured torque to the guidance processing unit104. Such transmission may be accomplished either using wires or by wireless means. In another example, the step is for the user to turn a screw until a micro switch is activated (e.g. switched from the off position to an on position). In this case, the sensor transmits a voltage associated with the off position while the screw is turned, and when the screw is turned to the proper position, the switch is activated, and a voltage associated with the on position is transmitted. In this case, real-time sensor data consisting of either one voltage or another voltage is transmitted.

Returning toFIG. 3A, the guidance processing unit104computes a performance measure from the sensor data, as shown in block320. This can be accomplished, for example, by comparing the received sensor data with a threshold value and computing a performance measure from the comparison of the received sensor data and threshold value. The performance measure can be used to monitor performance of the step and/or to verify performance of the step, as described further below. In the example of the torque wrench being used to tighten a nut on a bolt to a particular torque, the received real-time sensor data is compared to a threshold torque value (for example 10 newton meters), and a performance measure is computed from difference between the sensed torque and the threshold torque value.

In an embodiment, the guidance processing unit104optionally provides real time feedback about the progress of the task to the user108. This is illustrated in the dashed blocks ofFIG. 3B. Block322optionally generates and transmits feedback data according to the comparison of the sensor data and threshold value. This feedback data is optionally received by the presentation devices114and presented to the user108, as shown in blocks324and326. The generation of performance data and transmission of feedback allows the user to receive information regarding the progress of the step while performing the step itself. For example, in the case of the user108tightening a nut on a bolt, blocks322-326can compute a performance measure comprising a difference between the measured torque and a torque requirement or threshold, and present this difference to the user in terms of a gauge, digital display or other means. The feedback may also be aural (e.g. beeping when the proper torque value has been achieved) or both aural and visual (e.g. showing a visual depiction a comparison between the measured torque and the required torque in a bullseye graph and aural feedback with the tone either occurring when the proper torque has been achieved) or changing pitch, allowing the user to adjust the torque without looking at visual presentations.

The feedback may also provide an environmental state comparison with threshold values in support of physical or safety considerations. For example, such data and related threshold comparisons may include temperature, humidity, the location and/or movement of hazardous devices (e.g. a fork lift is approaching the user and/or is within a distance or threshold of the where an appendage of the user108may be during the performance of one or more of the steps of the task), and enable or lock out safety devices. Similarly, the sensor data collected may be provided to other elements at other stations102or between stations102in the MAT100to control those other elements to prevent such hazards (e.g. transmitting the sensor data to the fork lift to warn the operator that a step is going to be performed and that the fork lift should remain a safe distance away.

In block328, the guidance processing unit104determines whether the step has been completed. This can be accomplished by determining if the performance measure computed from the threshold value and the sensor data is within specified tolerances. In one embodiment, this is accomplished by comparing the state of the physical structure106, elements of the station102or MAT100against an expected state of the physical structure106, station102elements such as tools110or MAT100elements if the step was properly completed against the measured or actual state. Turning again to the example of the user108tightening a nut on a bolt, block328would indicate that the step of tightening the bolt is completed when the performance measure (the difference between the measured torque and the specified required torque, which represents the required value) is within a tolerance (e.g. 0.1 Nm) of the required torque.

If the step is not complete, processing is routed back to block318to receive and process further sensor data. The illustrated embodiment also includes an optional step failure test330which determines if the performance of the step has failed (e.g. a problem has arisen that prevents performance of the step). If the performance of the step has failed, processing is passed to block306to determine another step, with the context that the previously specified step has failed. If the performance of the step has not failed, processing is routed to block318to receive and process additional sensor data as before. The failure of a step can be determined using a comparison between a timer begun when the instructions for the step are sent (e.g. block308) and the time expected to complete the step. Alternatively, the timer could be started by using sensor data to indicate when the actual step was begun on the physical structure106. Failure of a step may also be determined according to the failure of a tool110required to perform the step, or the failure of the environment of the station102to attain a state required for performance of the step. For example, if the step requires an ambient temperature of 23 degrees Celsius, and the air conditioning or heating in the facility housing the station102is incapable of reaching that value.

If block328determines that the step is complete, processing is passed to block332, which generates and transmits feedback data regarding the performance of the completed step. That feedback data is received and presented to the user108by the presentation devices114or other means, as shown in blocks334-336. This feedback data can be used to send a confirmation to the user108that the step has been successfully (or unsuccessfully) completed, and is presented after performance of the step.

Finally, referring toFIG. 3C, block338stores the information gathered or computed with respect to the performance of the step. This information can include the sensor data from some or all of the sensors112and performance measures. Other information may also be stored, including the time that was required for the user108to perform the required step or task.

Such data can be used to determine how effective and economical the determined steps are in performing the required task, and can be compared to other possible steps in performing the task. For example, initially, a first set of stations102may be provided with a particular step sequence to accomplish a task, while another set of stations102may be provided with a different test sequence. The time required to perform the test can be determined for each set of stations, and compared to a quality measure in terms of how well the steps were performed, using the sensor data. In this way, two possible step sequences can be compared with real world results, with the most effective of the two possible step sequences selected for future activity.

The sensor data and performance measures may be used to improve on the process of determining the step illustrated in block306. For example, experienced users108may know that it isn't just performing the step, but how the step is performed that allows them to do the job more accurately and in less time. Using sensor data, such techniques and additional steps undertaken by the user108, even if not in the original definition of the required step, can be identified and integrated into how the step(s) are to be performed in the future. In early production, for example, a general outline of steps may be provided to experienced users, and the additional steps or skipped steps of those users can be used to optimize the process of determining the steps required to perform the task.

This can be accomplished by use of machine learning techniques. For example, the MAT100provides instructions to the user108for performing a particular procedure via a series of steps, and the user108performs the indicated steps. The MAT100can then use the sensor data of the user108performing the steps as well as other performance measures to “learn” (e.g. via machine learning) which instructions (or kind of instructions) confused the user, and to revise the instructions as required to make the instructions more understandable.

This can be determined, for example from the elapsed time between when the step was presented to the user108, and the user began performing the step and/or the elapsed time it took the user108to complete performance of the step (with excessive time indicative of confusion on the part of the user108). The MAT100may further use machine learning techniques to modify the steps for additional clarity or other improvements. Sensor data can be used to determine the source of the user confusion. For example, sensor data regarding the tools selected for the particular step can confirm or refute the notion that the user108used the proper tools in attempting to perform the step. The user108may also provide direct input (statements that they are confused or questions posed by the user108to clarify instructions that they do not find clear).

Procedures/steps may be modified for all users108based on the aggregate of those user's previous performance in performing steps, or may be modified on a user-by-user basis, so that the steps generated and presented to each user108by the MAT100are customized for each particular user108based upon that user's previous performance in previously performed steps or procedures. For example, the MAT100may generate a set of baseline steps that are to be performed to complete a particular task. More experienced users or those who have completed those tasks rapidly may be presented abbreviated versions of the instructions, while less experienced users or those who have taken longer to complete those task may be presented with versions of the instructions suggesting how the step might be better or more rapidly completed. Such versions may be based, for example, on sensor data complied from other users108who more rapidly completed the assigned steps or tasks. This allows the experience of all users108performing the task to be rapidly shared with more inexperienced users. Further, machine learning techniques and sensor data may be used to estimate the experience and expertise of the user, and the MAT100may present instructions commensurate with that experience. For example, different instruction sets of steps may be generated for performing the same tasks, and the MAT100may decide which instruction set to provide to the user108depending on an the user's professed or estimated experience level.

Machine learning techniques can also be used to diagnose and troubleshoot problems using sensor data collected in the performance of the steps. Sensor data from the production or maintenance of the physical structure106may be examined to attempt to correlate these failures with how one or more of the steps performed in assembling or maintaining the product using data mining or machine learning techniques. For example, a set of products may be found to have a particular failure (e.g. failure of a bolt). Data mining techniques can be used to analyze the sensor data collected in the production or maintenance of the physical structure106and attempt to correlate patterns with those physical structures that have failed. In one example, this analysis could conclude that each of the failed bolts were torqued higher than bolts that did not fail, raising the possibility that the torque specification is incorrect and should be changed. In a more complex example, the analysis may reveal that it was not the torque applied to the bolt, but rather, a tightening pattern or a flaw in a related part.

Finally, block340determines if the task is complete (e.g. additional steps are needed to complete the task). In one embodiment, this is accomplished by comparing the state of the physical structure106, elements of the station102or MAT100against an expected state of the physical structure106, station102elements such as tools110or MAT100elements if the task was properly completed against the measured or actual state. If additional steps are required, processing is routed to block306, which determines the next step, and processing continues as described above. If additional steps are not required, block344directs processing to the next task (if any). In one embodiment, this is accomplished by comparing the state of the elements of the station102or MAT100against an expected state of the station102or MAT100if the steps and tasks were properly completed.

Data regarding the performance of the task (e.g. the elapsed time or time duration to perform the entire task, or other task performance measures) may optionally be generated and stored, as shown in block342.

Task or step performance data, along with the sensor data used to generate the task or step performance data may be generated in real time and transmitted to other elements of the MAT100for purposes of real time digital documentation of the performance of steps, and used for archival and/or optimization purposes, parts usage for supply chain updates, audit logs and maintenance records.

FIG. 4is a diagram illustrating operation of the guidance processing unit104with other elements of the stations102and central processor120. In this embodiment, the guidance processing unit104is implemented using an Internet of Things (IoT) gateway402communicatively coupled to the interface206to receive data from and transmit data to sensors112and effectors (e.g. tools110).

Using the sensor interface206, the gateway402collects data from the sensors112and tools110and provides this information for processing and/or storage in one or more public servers404A-404N (hereinafter public server(s)404) and/or one or more private servers406at the MAT100. The public servers404are cloud-based processing and storage devices located “in the cloud” (e.g. remote from the station102or the MAT100and typically managed by another entity). The private server406is a data processing and storage device that is typically disposed with the station102and/or MAT100and is managed by the same entity.

The gateway402also receives commands and data from the public server(s)404and private server406and provides those commands to the interface206and thence to the sensor(s)112and tool(s)110as required. The public servers404and private server406also provide the instruction data illustrating the step(s) to presentation devices204such as display114A or an audio reproduction device. Machine learning/processing module408accesses the instruction data and may modify the instruction data based on previous instruction data, as well as sensor112and tool110data.

FIGS. 5A and 5Bare diagrams illustrating an exemplary test bed implementation of the guidance processing unit104and related elements of the station102. In this embodiment, the sensors112include a push button502(for example a GROVE button, which responds to a momentary push by outputting a digital voltage signal having a logical high signal and outputs a logical low signal when released) and a potentiometer504(providing an analog voltage signal). The effectors or tools110include for example, a light emitting diode506that receives a digital command, a stepper motor508receiving a digital command and/or a liquid crystal display510receiving an inter-integrated circuit (I2C) command. The interface206may be implemented by a modular processor device514(for example, an ARDUINO processor) communicatively coupled to an input/output (I/O) board such as a GROVE base shield512.

Further, in this embodiment, the IoT gateway402is implemented by a gateway processor519operating with an operating system (OS)518such as RASPBIAN executing an IoT programming tool516such as NODE-RED. The IoT gateway402implements open source real time messaging (MQTT) over WiFi and can send information from and to any combination of other IoT gateways402.

The IoT gateway402communicates with one of more public servers404A-404N (collectively referred to hereinafter as public server(s)404and/or one or more private server(s)406. Each public server404A-404N include a respective cognitive system520A-520N, respectively as well as a respective cloud platform528A-528N each implementing a software integration tool providing intercommunication of hardware devices. Each cognitive system520combines artificial intelligence (AI) and analytical software to produce a system that can answer questions.

In one embodiment, each public server404may be implemented using different software constructs by different vendors. For example, cognitive system520A may comprise IBM's WATSON IoT platform operating with a BLUEMIX cloud platform528A running a NODE-RED software integration tool522A, while cognitive system520N may comprise an MICROSOFT's AZURE IoT hub, operating with an AZURE application service528N running a JAVA application integration tool522N.

The servers404and406securely communicate with a visualization tool536executing an augmented reality (AR) application534via a representational state transfer (REST) compliant application program interface (API)532, and provides API security to ensure only authorized entities can access the system. Compliance with REST architectural constraints place limits on interaction between the elements, standardizing the syntax and means by which the elements communicate with one another. The result is that the architectural elements are essentially “pluggable,” allowing one version of an architectural element to be substituted from another version without any significant change in the operation or change in the other architectural elements. In one embodiment, the visualization tool is implemented by an AR authoring tool such as HOLOLENS, available from the MICROSOFT CORPORATION. Using this visualization tool536, IoT data can be viewed on mobile devices, by using REST based APIs to provide web-based access to the public server(s)404and/or private server(s)406. In one embodiment, private server406may be implemented using Message Queue Telemetry Transport (MQTT) Broker524and operating system530running a NODE-RED software integration tool526.

Hardware Environment

FIG. 6illustrates an exemplary computer system600that could be used to implement processing elements of the above disclosure, including the guidance processing unit104central processor120, presentation devices114, and/or sensors112. A computer602comprises one or more processors604A and604B and a memory, such as random access memory (RAM)606. The processor(s) may include a general purpose processor604A and/or a special purpose processor604B. General purpose processors604A typically do not require any particular computer language or software, and designed to perform general processing operations, but can be programmed for special applications. Special purpose processors604B may require a particular computer language or software, may implement some functions in hardware, and are typically optimized for a specific application. The computer602is operatively coupled to a display622, which presents images such as windows to the user on a graphical user interface (GUI). The computer602may be coupled to other devices, such as a keyboard614, a mouse device616, a printer628, etc. Of course, those skilled in the art will recognize that any combination of the above components, or any number of different components, peripherals, and other devices, may be used with the computer602.

Generally, the computer602operates under control of an operating system608stored in the memory606, and interfaces with the user to accept inputs and commands and to present results through a GUI module618A. Although the GUI module618B is depicted as a separate module, the instructions performing the GUI functions can be resident or distributed in the operating system608, the computer program610, or implemented with special purpose memory and processors. The computer602also implements a compiler612which allows a computer application program610written in a programming language such as C++, C#, Java, Python or other language to be translated into processor604readable code. After completion, the computer program610accesses and manipulates data stored in the memory606of the computer602using the relationships and logic that was generated using the compiler612. The computer602also optionally comprises an external communication device such as a modem, satellite link, Ethernet card, or other device for communicating with other computers.

In one embodiment, instructions implementing the operating system608, the computer program610, and the compiler612are tangibly embodied in a computer-readable medium, e.g., data storage device624, which could include one or more fixed or removable data storage devices, such as a hard drive, CD-ROM drive, flash drive, etc. Further, the operating system608and the computer program610are comprised of instructions which, when read and executed by the computer602, causes the computer602to perform the operations herein described. Computer program610and/or operating instructions may also be tangibly embodied in memory606and/or data communications devices630, thereby making a computer program product or article of manufacture. As such, the terms “article of manufacture,” “program storage device” and “computer program product” as used herein are intended to encompass a computer program accessible from any computer readable device or media.

Those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope of the present disclosure. For example, those skilled in the art will recognize that any combination of the above components, or any number of different components, peripherals, and other devices, may be used.

CONCLUSION

This concludes the description of the preferred embodiments of the present disclosure.

The foregoing discloses a cognitive assistant that allows a maintainer to speak to an application using Natural Language. The maintainer can quickly interact with an application hands-free without the need to use complex user interfaces or memorized voice commands. The assistant provides instructions to the maintainer using augmented reality audio and visual cues. The assistant will walk the maintainer through maintenance tasks and verify proper execution using IoT sensors. If after completing a step, the IoT sensors are not as expected, the maintainer is notified on how to resolve the situation. The foregoing description of the preferred embodiment has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the precise form disclosed. Many modifications and variations are possible in light of the above teaching. It is intended that the scope of rights be limited not by this detailed description, but rather by the claims appended hereto.