Photoelectric sensor with dynamic teach mode

Abstract

Apparatus and associated methods relate to photoelectric sensor systems with dynamic light teaching capabilities. In an illustrative example, a photoelectric sensor system may include a dynamic teach module configured to automatically learn environmental noise. In some embodiments, the module may determine a minimum and an average signal values during a dynamic teach mode. For example, the system may calculate a difference between these values and adjust gain settings based on this difference and a sensitivity level. Various embodiments may advantageously improve detection accuracy by filtering out noise from sources like conveyor belt seams or flutter without manual intervention.

Description

TPL Docket No.: 128-78-WOPHOTOELECTRIC SENSOR WITH DYNAMIC TEACH MODECROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application also claims the benefit of U.S. Provisional Application Serial No.63 / 761,530, titled “PHOTOELECTRONIC SENSOR WITH DYNAMIC TEACH MODE,” filed by Begad Elnielligy, et al., on February 21, 2025.

[0002] This application incorporates the entire contents of the foregoing application(s) herein by reference.TECHNICAL FIELD

[0003] Various embodiments relate generally to operating and calibrating photoelectric sensors.BACKGROUND

[0004] Photoelectric sensors are often used in conveyor belt systems. For example, these sensors can be configured to detect the presence of objects placed on the conveyor. Additionally, these sensors play a role in a variety of industrial processes, including sorting and quality control. Despite their utility, the dynamic nature of conveyor belt systems can often present issues in the performance of photoelectric sensors. The movement of the belt, along with various environmental conditions (e.g., vibrations, lighting conditions, etc.), can introduce environmental noise that may disrupt sensor performance.

[0005] Photoelectric sensors may function by emitting light from a source. Thereafter, the sensors may receive the light and detect changes, which may occur when an object enters the sensing area. Additionally, the sensors can be configured in different arrangements, such as through-beam, retro-reflective, diffuse reflection, and / or the like. Signal processing techniques are often used to provide analysis on the received light in order to determine whether an object is present.

[0006] As mentioned above, photoelectric sensing often deals with how to handle environmental noise and other variations in conditions. These variations can involve lighting conditions, dust, vibrations, temperature fluctuations, etc. and can affect sensor reliability. Various techniques have been employed to improve sensor accuracy and may include automatic gain control and filtering techniques that compensate for environmental changes.SUMMARY

[0007] Apparatus and associated methods relate to photoelectric sensor systems with dynamic light teaching capabilities. In an illustrative example, a photoelectric sensor system may include a dynamic teach module configured to automatically learn environmental noise. In someTPL Docket No.: 128-78-WO embodiments, the module may determine a minimum and an average signal values during a dynamic teach mode. For example, the system may calculate a difference between these values and adjust gain settings based on this difference and a sensitivity level. Various embodiments may advantageously improve detection accuracy by filtering out noise from sources like conveyor belt seams or flutter without manual intervention.

[0008] Various embodiments may achieve one or more advantages. For example, some embodiments may reduce human error by automating the sensor calibration process. Some embodiments may, for example, dynamically adapt to environmental changes to improve detection performance. For example, some embodiments may reduce repetitive recalibrations due to drifting environmental conditions.

[0009] The details of various embodiments are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIG. 1 illustrates an exemplary Photoelectric Sensor With Dynamic Teach (PSWDT) employed in an illustrative use-case scenario.

[0011] FIG. 2 illustrates an exemplary block diagram of a photoelectric sensor system.

[0012] FIG. 3 illustrates an exemplary block diagram of another photoelectric sensor system.

[0013] FIG. 4 illustrates an exemplary block diagram of a dynamic light teach system.

[0014] FIG. 5 illustrates a flowchart of an exemplary dynamic light teaching method.

[0015] Like reference symbols in the various drawings indicate like elements.DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0016] FIG. 1 illustrates an exemplary Photoelectric Sensor With Dynamic Teach (PSWDT) employed in an illustrative use-case scenario. In this example, a conveyor belt system 100 includes a PSWDT 102 and a conveyor belt 108. For example, the PSWDT 102 may be configured to detect the presence of objects (e.g., for obtaining object count, for activating a packaging system). As shown, the PSWDT 102 may include emitting a light and receiving a reflection of the light (e.g., through a retroreflector) to detect an object on the conveyor belt 108 at a detection site 104. For example, when an object (e.g., a package) is transported through the detection site 104, the PSWDT 102 may generate a signal indicating a detection.

[0017] In some implementations, the PSWDT 102 may include a noise filtering engine 106 for accurately detecting objects on the conveyor belt 108 while filtering out noise caused by environmental features (e.g., feature on a belt surface, external reflection of light). In this example, the conveyor belt 108 includes a belt seam 110. As an illustrative example without limitation, theTPL Docket No.: 128-78-WO belt seam 110 may create a signal disturbance (e.g., a noise signal) when the belt seam 110 passes through the detection site 104. In some examples, the belt seam 110 may trigger the PSWDT 102 to generate a false detections (e.g., when the belt seam 110 is not filtered out) and / or missed objects (e.g., when the noise filtering engine 106 is erroneously adjusted to filter packages along with the belt seam 110).

[0018] In some examples, the PSWDT 102 may transmit a light beam 112towards a retro reflector positioned across a width of the conveyor belt 108. For example, the retro reflector may reflect this light back towards the PSWDT 102. The PSWDT 102 may detect the reflected light. When an object passes between the PSWDT 102 and the retro reflector, for example, the light beam 112 may be interrupted. This may indicate the presence of an object on the conveyor belt 108. In some examples, the belt seam 110 may cause false detections by, for example, introducing additional noise signals as it moves through the detection site 104.

[0019] In some implementations, the PSWDT 102 may operate in a dynamic teach mode to adjust the noise filtering engine 106. For example, after operating in the dynamic teach mode, the PSWDT 102 may be adaptively adjusted to the environmental noise of the conveyor belt system 100 (e.g., including the belt seam 110 of the conveyor belt 108). For example, the dynamic teach mode may advantageously improve detection accuracy.

[0020] As shown, the PSWDT 102 includes a dynamic teach engine (DTE 114). The DTE 114 includes an environmental noise learning module 116, a gain adjustment module 118, and a switch point module 120.

[0021] For example, the environmental noise learning module 116 may analyze the signal patterns received by the PSWDT 102 over a period of time. For example, the environmental noise learning module 116 may store historical signals received during a dynamic teach operation. For example, the environmental noise learning module 116 may generate various statistical indicators of the historical signal (e.g., a minimum, a maximum, an average, a mode, a standard deviation, a variance, a confidence interval). In some examples, the environmental noise learning module 116 may learn to recognize the periodic interruptions caused by the belt seam 110 as the conveyor belt 108 completes full rotations.

[0022] For example, the gain adjustment module 118 may receive computation results from the environmental noise learning module 116 to adaptively adjust a sensitivity of the PSWDT 102. In some examples, the gain adjustment module 118 may adjust gain settings of the PSWDT 102 to reduce an impact of one or more environmental noise in the conveyor belt system 100.

[0023] For example, the switch point module 120 may adjust an object detection threshold(s) (ODT(s)) as a function of the gain setting of the gain adjustment module 118 and / or noise patterns generated by the environmental noise learning module 116. For example, the ODT may include aTPL Docket No.: 128-78-WO switch point at which an output signal of the PSWD T 102 switches. For example, the OD T(s) may be set to differentiate between the signal interruption caused by the belt seam 110 and the interruptions caused by actual objects on the conveyor belt 108. As shown, the switch point module 120 may store the switch point measured from the dynamic teach operation in a switch point(s) data store (SPDS 122).

[0024] In an illustrative example without limitation, a user may present the PSWDT 102 within a target operating environment in the light state (e.g., when the conveyor belt 108 moves with no objects present). For example, the user may activate the DTE 114 to start the dynamic teach operation. In some implementations, the DTE 114 may sweep a sensor gain (e.g., up and down) in the dynamic teach operation. For example, the DTE 114 may measure the switch point of the PSWDT 102 in the target operating environment. For example, the DTE 114 may record signals received from the PSWDT 102 (e.g., a minimum signal and an average signal measured during the dynamic teach operation). In some implementations, the DTE 114 may dynamically update various statistical representations of the environment signal variation as a function of the recorded signals.

[0025] In the depicted example, the PSWDT 102 includes an environmental signal variation 124. For example, the environmental signal variation 124 may include the environmental noise of the conveyor belt system 100 (e.g., including conveyor belt seams, vibration). For example, the DTE 114 may generate the environmental signal variation 124 as a difference of the minimum signal and the average signal.

[0026] In some implementations, the dynamic teach operation may be terminated via manual user input and / or timeout. For example, the DTE 114 may add the difference between the average and min signals recorded on top of the switch point. In some implementations, the DTE 114 may add a user-configurable offset to the switch point. For example, a drift filter gain control of the PSWDT 102 may be configured to maintain an aggregate of the environment signal variation and the user-configurable offset. In some examples, the drift filter may be configured to maintain a stable switch point determined by the DTE 114.

[0027] In various embodiments, the DTE 114 may be configured to learn specific (e.g., environmental) characteristics of the conveyor belt system 100. In some implementations, the DTE 114 may be configured to run for an unspecified length of time in a predetermined operation state (e.g., a light state when no object is placed on the conveyor belt 108) until the dynamic teach mode is manually stopped. For example, the DTE 114 may adjust the noise filtering engine 106 without having any knowledge of the conveyor belt system 100 (e.g., a length of the conveyor belt 108). In some implementations, the DTE 114 may run through (e.g., a predetermined number, multiple belt rotations) several iterations during the dynamic teach mode. For example, the predetermined number of rotations may advantageously allow the environmental noise learning module 116 toTPL Docket No.: 128-78-WO capture a comprehensive noise profile of the belt seam 110 along with other recurring noise patterns.

[0028] Once the dynamic learning mode is completed, for example, the gain adjustment module 118 and switch point module 120 may collaboratively (e.g., interactively) adjust detection parameters to reduce false positive detection. In some examples, the PSWDT 102 may filter out periodic interruption caused by the belt seam 110 while remaining sensitive to other objects on the conveyor belt 108. For example, the DTE 114 may advantageously improve the reliability of automated processes that depend on accurate object detection (e.g., sorting or quality control operations).

[0029] FIG. 2 illustrates a block diagram of a PSWDT 200. The PSWDT 200 includes a light emitter 202, a light receiver 204, and (optionally) a retro reflector 206 in this example. In some examples, the PSWDT 200 may be used in a conveyor belt system (e.g., the conveyor belt system 100). For example, the PSWDT 200 may be configured to automatically adapt to the environmental noise present in conveyor belt environments during the dynamic teach operation described with reference to FIG. 1. For example, the PSWDT 200 may learn to filter out noise caused by belt seams passing through the sensor's field of view.

[0030] As shown, the PSWDT 200 includes a light emitter 202 and a light receiver 204. The retro reflector 206 may be positioned in proximity to the PSWDT 200, for example, across the width of the conveyor belt 108. In some examples, the light emitter 202 may be configured to transmit light. The light emitter 202 may direct the transmitted light towards the retro reflector 206. The retro reflector 206, for example, may be positioned to reflect the light transmitted by the light emitter 202 back towards the light receiver 204 of the PSWDT 200.

[0031] The light receiver 204 may be configured to receive reflected light. In some examples, the light receiver 204 may receive the light that has been reflected by the retro reflector 206. The arrangement of the light emitter 202, light receiver 204, and retro reflector 206 may allow the PSWDT 200 to detect objects that interrupt the light path between the PSWDT 200 and the retro reflector 206. In some examples, the PSWDT 200 may detect objects that reflect light while interrupting the light path between the PSWDT 200 and the retro reflector 206, such as objects entering the light path, having reflective surfaces.

[0032] For example, the retro reflector 206 may allow the light emitter 202 and light receiver 204 to be housed in the same unit. For example, the retro reflector 260 may advantageously simplify installation of the PSWDT 200.

[0033] The PSWDT 200 may include a signal processing unit 208 configured to process signals from the light receiver 204. In some examples, the signal processing unit 208 may comprise an average value calculator 210, a minimum value detector 212, and a difference calculator 214.TPL Docket No.: 128-78-WO [0034| Fhe average value calculator 210 may be configured to calculate average signal values based on the signals received from the light receiver 204. In some examples, the average value calculator 210 may compute a running average of the signal values over a specified period of time.

[0035] The minimum value detector 212 may be configured to detect minimum signal values from the signals received by the light receiver 204. In some examples, the minimum value detector 212 may continuously monitor the incoming signals and update the minimum value when a lower signal value is detected.

[0036] The difference calculator 214 may be configured to determine differences between calculated values. In some examples, the difference calculator 214 may compute the difference between the average signal value (e.g., determined by the average value calculator 210) and the minimum signal value (e.g., detected by the minimum value detector 212).

[0037] The PSWDT 200 may also include a gain control module 216 configured to adjust gain settings. In some examples, the gain control module 216 may receive inputs from the signal processing unit 208 to determine appropriate gain adjustments. The gain control module 216 may use the calculated difference from the difference calculator 214 to fine-tune the gain settings of the PSWDT 200.

[0038] The PSWDT 200 may include a switch point detector 218. The switch point detector 218 may be configured to determine switching thresholds at which an output signal of the PSWDT 200 switches at an output port 224. In some examples, the switch point detector 218 may use information from the signal processing unit 208 and the gain control module 216 to set the switching thresholds used for object detection.

[0039] The components of the signal processing unit 208 may work in combination to process the received light signals and provide inputs for the gain control module 216 and switch point detector 218. For example, the average value calculator 210, minimum value detector 212, and difference calculator 214 may be used to provide analysis on the signals in order to determine detection thresholds. The gain control module 216 and switch point detector 218 may then use this processed infomiation to maintain system performance (e.g., to improve object detection accuracy).

[0040] In some examples, the incorporation of these various components may allow the PSWDT 200 to dynamically adjust to changing environmental conditions. The signal processing unit 208 may continuously update its calculations, enabling the gain control module 216 and switch point detector 218 to make real-time adjustments to the system’s sensitivity and detection thresholds.

[0041] The PSWDT 200 may include a user interface 220. In some examples, the user interface 220 may be configured to allow a user to select a time period for the dynamic teach module operation. The user interface 220 may provide a means for users to interact with the PSWDT 200. For example, a user may be enabled to customize various operational parameters of the system. InTPL Docket No.: 128-78-WO some examples, the user interface 220 may include preset configurations commonly used for different types of conveyor belt systems (e.g., food processing, automotive manufacturing, etc.). These configurations may adjust parameters like the sensitivity level and dynamic teach duration in anticipation of typical noise conditions encountered given the application.

[0042] In some examples, the user interface 220 may be further configured to receive system status information and display the information to a user. This functionality may allow users to monitor the performance of the PSWDT 200 in real-time (e.g., allowing for more efficient troubleshooting of the system's operation).

[0043] The PSWDT 200 may include a drift filter 222. In some examples, the drift filter 222 may be configured to maintain a target signal level by compensating for environmental changes that affect sensor readings. For example, the drift filter 222 may advantageously ensure reliable performance of the PSWDT 200 over time.

[0044] In some implementations, the drift filter 222 may be configured to maintain a consistent and / or environmentally stable switch point for the PSWDT 200. For example, the drift filter 222 may dynamically adjust a sensor gain to compensate environmental variations. For example, the environmental variations may include the environmental signal variation 124. For example, the drift filter 222 may retrieve the environmental signal variation 124 determined by the DTE 114 to compensate environmental variations (e.g., conveyor belt seams, vibrations, changes in reflectivity). In some embodiments, the photoelectric sensor is automatically calibrated according to environmental noise without prior knowledge of the operating environment.

[0045] For example, the SPDS 122 may be initially determined by sweeping the sensor gain up and down during the dynamic teach operation to find where the output switches. After teaching, for example, the drift filter maintains the SPDS 122 by continuously adjusting the sensor's gain.

[0046] In various embodiments, a stable switch point may include an aggregation of a difference between the average and minimum signals (which represents the natural fluctuation of the environment) obtained during the dynamic teach operation and a user-configurable offset. For example, the drift filter 222 may advantageously maintain a stable switch point despite gradual changes in environmental conditions.

[0047] In some examples, the drift filter 222 may be configured to receive inputs from the signal processing unit 208 and provide outputs to adjust system operation. This interaction between the drift filter 222 and the signal processing unit 208 may enable the PSWDT 200 to dynamically adapt to changing conditions without requiring manual intervention.

[0048] The drift filter 222 may work in combination with other components of the PSWDT 200 to maintain system performance. By continuously monitoring environmental changes, the driftTPL Docket No.: 128-78-WO filter 222 may help prevent false detections (e.g., and / or missed objects) that could result from changes in environmental conditions.

[0049] For example, the user interface 220 may provide a means for users to configure and monitor the system, while the drift filter 222 may help maintain performance over time. These features, along with the dynamic teach capabilities, may enable the PSWDT 200 to automatically learn and adjust to environmental noise without manual intervention. This approach may improve detection accuracy and reduce setup time compared to traditional manually adjusted photoelectric sensors. Various components may advantageously improve overall functionality and adaptability of the PSWDT 200.

[0050] The PSWDT 200 with dynamic teach mode described herein may automatically leam and adjust to environmental noise without manual intervention. In some examples, the dynamic teach module may learn minimum and average signal values during a user-selected time period, calculate the difference between these values, and adjust gain settings based on this difference and a sensitivity level.

[0001] This approach may enable the PSWDT 200 described herein to filter out noise from sources like conveyor belt seams and flutter, leading to improved detection accuracy. The PSWDT 200 may run for a user-selected time or until manually stopped. For example, the PSWDT 200 may advantageously allow a user to selectively adjust detection performance in response to environmental changes based on training duration.

[0052] The features of this PSWDT 200, including its ability to automatically learn and adapt to environmental noise, may provide a more robust implementation for object detection in industrial settings. Various embodiments may advantageously offer improved accuracy and reduced setup time compared to traditional photoelectric sensors.

[0053] FIG. 3 illustrates a block diagram of a PSWDT 300. The PSWDT 300 may include a dynamic teach module 302, a signal processing unit 304, a gain control module 306, and a switch point detection module 308. In some examples, the PSWDT 300 may be configured to synchronize its operation with the speed of the conveyor belt. For example, the environmental noise learning module 310 may adjust its sampling rate based on belt speed, which may in turn ensure accurate characterization of periodic noise.

[0054] In some examples, the dynamic teach module 302 may be configured to automatically learn and adjust to environmental noise. The dynamic teach module 302 may coordinate the operation of various components within the PSWDT 300 during the teaching phase.

[0055] The dynamic teach module 302 may interact with the signal processing unit 304, which may process signals received from a light receiver. In some examples, the signal processing unit 304 may provide processed sensor data to other modules within the PSWDT 300.TPL Docket No.: 128-78-WO [00561 I’he gain control module 306 may adjust gain settings based on inputs from other modules. In some examples, the gain control module 306 may receive inputs from the dynamic teach module 302 and the switch point detection module 308 to make gain adjustments.

[0057] The switch point detection module 308 may determine switch points during the teaching phase. In some examples, the switch point detection module 308 may work in combination with the gain control module 306 to set switching thresholds for the PSWDT 300.

[0058] The dynamic teach module 302 may be configured to run for a user-selected time period or until manually stopped. This feature may allow users to customize the duration of the teaching process, which may be useful depending on the specific application. In some examples, the user-selected time period may be set through a user interface of the PSWDT 300.

[0059] The interaction between the dynamic teach module 302 and other components of the PSWDT 300 may enable automatic learning and adjustment to environmental noise. For example, the dynamic teach module 302 may receive processed sensor data from the signal processing unit 304, use this data to determine gain adjustments through the gain control module 306, and set appropriate switch points via the switch point detection module 308.

[0060] By incorporating these various components, the dynamic teach module 302 may allow for automatic adaptation of the PSWDT 300 to environmental conditions without requiring manual intervention. This approach may improve the system's ability to filter out noise from sources such as conveyor belt seams or flutter, in turn enhancing detection accuracy.

[0061] The PSWDT 300 may include an environmental noise learning module 310. In some examples, the environmental noise learning module 310 may be configured to learn and process environmental noise characteristics, including average and minimum signal values. The environmental noise learning module 310 may work in combination with the dynamic teach module 302 to automatically adapt the PSWDT 300 to environmental conditions.

[0062] The PSWDT 300 may be configured to receive an external enable signal derived from the conveyor belt system being static or actively moving such that the dynamic teach module 302 may be automatically disabled or suspended from operation, for example, if the conveyor belt stopped moving during the teaching phase.

[0063] In some examples, the environmental noise learning module 310 may be configured to learn a minimum signal value and an average signal value during a uscr-sclcctcd time period. The environmental noise learning module 310 may provide these learned noise characteristics to the dynamic teach module 302 for use in adjusting system parameters.

[0064] The dynamic teach module 302 may be configured to calculate a difference between the average signal value and the minimum signal value learned by the environmental noise learning module 310. This calculated difference may be used to fine-tune the operation of the PSWDT 300.TPL Docket No.: 128-78-WO [0065| In some examples, the dynamic teach module 302 may be configured to adjust the gain settings based on the calculated difference and a sensitivity level. The dynamic teach module 302 may communicate with the gain control module 306 to implement these adjustments, thereby improving the system’s ability to filter out environmental noise.

[0066] The PSWDT 300 may include a user interface 312. In some examples, the user interface 312 may allow user interaction with the system. The user may select the teaching duration and additionally receive system status information. The user interface 312 may provide a means for users to configure the user-selected time period for the dynamic teach process.

[0067] The user interface 312 may also display system status information that allows users to monitor the performance of the PSWDT 300 in real-time. This functionality may be used for efficient troubleshooting of the system's operation.

[0068] The combination of the environmental noise learning module 310 and the user interface 312 with the aforementioned components may contribute to the overall functionality and adaptability of the PSWDT 300. The environmental noise learning module 310 may enable the system to automatically learn and adjust to environmental noise, while the user interface 312 may provide a means for users to customize and monitor the system's operation. These features, along with the dynamic teach capabilities provided by the dynamic teach module 302, may enable the PSWDT 300 to automatically learn and adjust to environmental noise without manual intervention, thereby improving detection accuracy and reducing setup time compared to traditional manually adjusted photoelectric sensors.

[0069] FIG. 4 illustrates a block diagram of a dynamic teach system (DTS 400). As shown in this example, the DTS 400 includes an environmental noise learning module 402. As shown, the environmental noise learning module 402 includes a minimum value learner 404 and an average value learner 406.

[0070] In some examples, the environmental noise learning module 402 may be configured to learn and process environmental noise characteristics. The environmental noise learning module 402 may receive input signals and process them through the minimum value learner 404 and the average value learner 406.

[0071] The minimum value learner 404 may be configured to determine minimum signal values during operation of the DTS 400. In some examples, the minimum value learner 404 may continuously monitor incoming signals and update the minimum value when a lower signal value is detected.

[0072] The average value learner 406 may be configured to calculate average signal values during operation of the DTS 400. In some examples, the average value learner 406 may compute a running average of the signal values over a specified time period.TPL Docket No.: 128-78-WO [0073| Fhe minimum value learner 404 and the average value learner 406 may be configured to operate during a user-selected time period. This user-selected time period may be set through the user interface of the photoelectric sensor system. By operating during a user-selected time period, the environmental noise learning module 402 may allow for customization of the learning process.

[0074] In some examples, the environmental noise learning module 402 may provide learned noise characteristics to other components of the DTS 400. These learned characteristics may be used to adjust system parameters and improve the ability of the photoelectric sensor system to filter out environmental noise.

[0075] The DTS 400 may work in combination with other components of the photoelectric sensor system (e.g., the signal processing unit, the gain control module, and the switch point detection module). By integrating the learned noise characteristics from the environmental noise learning module 402, the DTS 400 may enable automatic adaptation to environmental conditions without requiring manual intervention.

[0076] The DTS 400 may include a target signal calculator 408. In some examples, the target signal calculator 408 may be configured to determine target signal levels based on inputs from other components of the DTS 400. The target signal calculator 408 may receive data from the environmental noise learning module 402, which may include the minimum and average signal values learned during the dynamic teach process.

[0077] A gain adjustment module 410 may be included in the DTS 400. The gain adjustment module 410 may be configured to modify signal gain levels based on inputs from other system components. In some examples, the gain adjustment module 410 may contain a first gain adjuster 412 and a second gain adjuster 414.

[0078] The first gain adjuster 412 may be configured to perform an initial gain adjustment. In some examples, the first gain adjuster 412 may apply a first adjustment to a gain stage to search for a switch point. For example, this process may involve incrementally adjusting the gain until a predetermined threshold or condition is met. Once met, this may indicate the location of the switch point.

[0079] The second gain adjuster 414 may be configured to perform a subsequent gain adjustment. In some examples, the second gain adjuster 414 may apply a second adjustment to the gain stage based on the calculated difference between the average and minimum signal values, in addition with a sensitivity level. This second adjustment may fine-tune the gain settings in order to improve (e.g., optimize) the system's performance in the presence of environmental noise.

[0080] The dynamic teach module 302 may coordinate the operations of the target signal calculator 408 and the gain adjustment module 410. In some examples, the dynamic teach module 302 may be configured to set a target signal based on the switch point identified by the first gainTPL Docket No.: 128-78-WO adjuster 412, the calculated difference between average and minimum signal values, and the sensitivity level.

[0081] The interaction between these components may enable the DTS 400 to automatically adapt to environmental conditions. For example, the target signal calculator 408 may use the learned noise characteristics from the environmental noise learning module 402 to determine a target signal level. The gain adjustment module 410 may then adjust the gain settings to achieve this target signal level, which may lead to improving the system's ability to filter out environmental noise and maintain reliable detection performance.

[0082] The DTS 400 may include a switch point module 416. In some examples, the switch point module 416 may interact with the gain adjustment module 41 to set switching thresholds for the photoelectric sensor system. The switch point module 416 may use information from other components of the DTS 400 to determine appropriate switching thresholds for object detection.

[0083] In some examples, the switch point module 416 may work in combination with the first gain adjuster 412 during the process of searching for a switch point. The switch point module 416 may analyze the signals processed by the first gain adjuster 412 to identify the point at which the photoelectric sensor system transitions between detecting and not detecting an object.

[0084] The switch point module 416 may interact with the target signal calculator 408. In some examples, the switch point module 416 may provide information about the identified switch point to the target signal calculator 408, which may use this information along with data from the environmental noise learning module 402 to determine a target signal level.

[0085] The DTS 400 may include a user input module 418. In some examples, the user input module 418 may allow for a user to set a configuration of system parameters. The user input module 418 may provide a means for users to interact with the DTS 400 and enable customization of operational parameters.

[0086] In some examples, the user input module 418 may be configured to allow a user to select a time period for the dynamic teach mode. Users may be able to specify the duration of the learning process through the user input module 418, which may allow for customization of the teaching process based on the application.

[0087] The user input module 418 may also be configured to receive and display system status information. In some examples, the user input module 418 may provide real-time feedback about the operation of the DTS 400, which may help with efficient troubleshooting and performance monitoring.

[0088] The user input module 418 may interact with other components of the DTS 400. For example, the user input module 418 may communicate user-selected parameters to the environmental noise learning module 402, which in turn may adjust the duration of the minimumTPL Docket No.: 128-78-WO value learner 404 and average value learner 406 operations. The user input module 418 may provide input to the gain adjustment module 410 and allow users to adjust sensitivity levels (e.g., or other gain-related parameters).

[0089] In some examples, the user input module 418 may work in combination with the switch point module 416 to allow users to manually set switching thresholds. This functionality may provide additional flexibility in adapting the DTS 400 to specific applications.

[0090] The combination of the switch point module 416 and the user input module 418 with the described components may contribute to the overall functionality and adaptability of the DTS 400. These modules may enable automatic determination of switching thresholds while also providing user customization options.

[0091] FIG. 5 illustrates a flowchart of an exemplary dynamic teaching method. For example, the method 500 may be performed by the PSWDT 102. For example, the method 500 may be performed by the PSWDT 200. For example, the method 500 may be performed by the PSWDT 300. For example, the method 500 may be performed by the DTS 400. For example, the method 500 may be performed by a processor to cause sensor calibration operations to be performed to automatically to calibrate a photoelectric sensor with respect to an operating environment.

[0092] In this example, the method 500 begins in step 502 when a signal is received to initiate a dynamic teach mode. In some examples, the dynamic teach mode may be initiated through the user interface 220 of the PSWDT 200. For example, a teach initiation signal may activate a photoelectric sensor in a dynamic teach mode.

[0093] After entering the dynamic teach mode, a photoelectric sensor system is activated in step 504. For example, the PSWDT 102 may be activated for object detection in the light state (e.g., without any object placed on the conveyor belt 108). In step 506, measurement data is collected. Collected measurement data, for example, may include light intensity. In some examples, the DTE 114 may receive (e.g., continuously) intensity measurement signals of reflected light from the conveyor belt 108. For example, the signal processing unit 304 may process the intensity measurement signals during this period. In some examples, the minimum value learner 404 and the average value learner 406 may continuously update their respective values based on the incoming signals from the light receiver.

[0094] In step 508, a sensor gain is swept across a range (e.g., up and down) to measure a switch point at a range of the sensor gain. For example, the switch point detection module 308 may control the gain control module 306 to adjust the sensor gain and record switch point at each gain. In some embodiments, the sensor gain is swept across a predetermined gain range.TPL Docket No.: 128-78-WO [0095| In step 510, an environmental variation offset (EVO) is generated. For example, the DTE 114 may generate the environmental signal variation 124 as a function of the minimum measured signal and average measured signal measured.

[0096] In a decision point 512, it is determined whether the teach mode is ended. For example, the teach mode may end when a user manually terminate the dynamic teach mode using the user interface 220.

[0097] If it is determined that the teach mode is not ended, the step 504 is repeated. If it is determined that the teach mode is ended, in step 514, a gain sensitivity is adjusted based on the EVO, the switch point, and user input. For example, the DTE 114 may adjust the noise filtering engine 106. For example, the user input may include a user-configurable offset received from the user interface 220. For example, the drift filter 222 may be configured to maintain the gain sensitivity.

[0098] Although various embodiments have been described with reference to the figures, other embodiments are possible. In some examples, the photoelectric sensor system may incorporate multiple dynamic teach modes configured for different types of environmental noise. For example, one mode may be designed to handle noise from conveyor belt seams, while another mode may be designed for noise caused by vibrations. In such a case, users may select the mode based on their specific application.

[0099] The PSWDT 200 may include advanced signal processing algorithms to supplement its noise filtering function. In some examples, these algorithms may utilize techniques including but not limited to adaptive filtering, frequency domain analysis, or machine learning approaches to distinguish between object detections and environmental noise.

[0100] In some variations, the PSWDT 200 may incorporate wireless connectivity features. This may allow for remote monitoring and configuration of the sensor's performance. In some examples, multiple photoelectric sensors may be networked together.

[0101] The dynamic teach process may include learning of temporal patterns in environmental noise. In some examples, the system may analyze the timing and frequency of noise events, which in turn may aid in filtering.

[0102] In some embodiments, the PSWDT 200 may incorporate adaptive sensitivity modes. These modes may automatically adjust the sensor's sensitivity based on the learned environmental noise characteristics. For example, the system may increase sensitivity during periods of low noise and decrease sensitivity when environmental noise levels are higher.

[0103] The user interface of the photoelectric sensor system may provide more detailed feedback on the dynamic teach process. In some examples, this may include graphical representations of learned noise patterns, and suggested parameter configurations based on the system's analysis.TPL Docket No.: 128-78-WO [0104| In some variations, the photoelectric sensor system may incorporate multiple light emitters and receivers, where each emitter / receiver may be configured at a different wavelengths (e.g., multi-spectral). This approach may allow for more robust object detection in certain environments.

[0105] In some embodiments, the PSWDT 200 may be automatically disabled when the conveyor belt is static. This variant may prevent the collection of invalid environmental noise.

[0106] The dynamic teach process may include learning of spatial noise patterns. In some examples, the system may use a scanner to map out spatial variations in environmental noise.

[0107] The photoelectric sensor system may also be adapted for use in non-industrial applications, such as security systems.

[0108] In some variations, the PSWDT 200 may adjust its power consumption based on the learned environmental characteristics, preserving energy of the of system. For example, the dynamic teach mode may end when the PSWDT 200 may have determined to have sufficient data to calibrate the photoelectric sensor at a predetermined accuracy level.

[0109] In some examples, the minimum value learner 404 of the environmental noise learning module 402 may determine the minimum signal value during the dynamic teach mode operation. The minimum value learner 404 may continuously monitor the incoming signals from the light receiver 204 and update the minimum value when a lower signal value is detected.

[0110] In some examples, the average value learner 406 of the environmental noise learning module 402 may calculate the average signal value during the dynamic teach mode operation. The average value learner 406 may compute a running average of the signal values over the selected period.

[0111] The dynamic teach module 302 may coordinate the activities of the minimum value learner 404 and the average value learner 406 in performing the method 500. In some examples, the dynamic teach module 302 may ensure that both the average and minimum signal values are learned in the light state. This state may occur when no object is blocking the light path between the light emitter 202 and the light receiver 204, for example.

[0112] By learning both the average and minimum signal values, in some implementations, the PSWDT 102 may be able to interpret and recognize the environmental noise characteristics. In some examples, this information may be used by other components of the photoelectric sensor system, such as the gain control module 216 and the switch point detector 218, to improve the system's performance in the presence of environmental noise.

[0113] The signal processing unit 208 may process the signals from the light receiver 204 during these learning steps. In some examples, the signal processing unit 208 may apply filtering or other signal processing techniques to the incoming signals before they are analyzed by the minimum value learner 404 and the average value learner 406.TPL Docket No.: 128-78-WO [0114| The learning of minimum, maximum, and / or average signal values may allow the photoelectric sensor system to capture a sample of the environmental noise. In some examples, this may include noise variations caused by factors such as conveyor belt seams or flutter. This approach may enable the system to automatically adapt to these conditions without requiring manual intervention.

[0115] The user interface 312, for example, may receive user-selected thresholds and / or sensitivity levels that, for example, affect how the difference is calculated and / or how the switch point search is performed.

[0116] In some examples, the gain control module 306 may incrementally adjust the gain settings while monitoring the output of the photoelectric sensor system. The switch point detector may analyze the sensor output to identify the point at which the system transitions between detecting and not detecting an object.

[0117] For example, adjustment to a gain stage may include incrementally modifying the gain until a predetermined threshold or condition is met, which may indicate the location of the switch point. The dynamic teach module 302 may coordinate this process.

[0118] In some examples, the target signal calculator may determine a target signal level based on the switch point, the calculated difference, and the sensitivity level. The gain adjustment module may then make additional adjustments to the gain settings to hit this target signal level.

[0119] For example, the dynamic teach module 302 may coordinate the activities of the gain adjustment module and the target signal calculator. The environmental noise learning module may provide inputs to the target signal calculator, which may lead to the learned noise characteristics being considered when determining the target signal level.

[0120] In some examples, the dynamic teach module may set the target signal level based on the information gathered during the aforementioned steps. For example, this target signal may act as a reference point for the photoelectric sensor system's ongoing operation, which in turn may help maintain consistent detection performance.

[0121] The user interface 220 may display information about the target signal setting process during the dynamic teach mode. In some examples, this may allow users to monitor the performance of the dynamic teach process and ensure that the system has adapted to the environmental conditions. In some embodiments, the user interface 220 may provide a termination signal.

[0122] In some examples, the photoelectric sensor system may include additional features or variations to enhance its functionality and adaptability. These embodiments may provide further improvements in detection accuracy and system flexibility across various industrial applications.TPL Docket No.: 128-78-WO [0123| Fhe conveyor belt system 100 may include a drift filter that utilizes the output from the dynamic teach process as a target. This drift filter may help maintain performance over time by compensating for changes in environmental conditions. In some examples, the drift filter may continuously monitor the sensor output and make small adjustments to maintain appropriate alignment with the target signal set during the dynamic teach process.

[0124] For example, the drift filter 222 may work in combination with the gain control module to make fine adjustments to the gain settings. In some examples, these adjustments may be made in real-time.

[0125] Although an exemplary system has been described with reference to FIGS. 1-5, other implementations may be deployed in other industrial, scientific, medical, commercial, and / or residential applications.

[0126] In various embodiments, some bypass circuits implementations may be controlled in response to signals from analog or digital components, which may be discrete, integrated, or a combination of each. Some embodiments may include programmed, programmable devices, or some combination thereof (e.g., PLAs, PLDs, ASICs, microcontroller, microprocessor), and may include one or more data stores (e.g., cell, register, block, page) that provide single or multi-level digital data storage capability, and which may be volatile, non-volatile, or some combination thereof. Some control functions may be implemented in hardware, software, firmware, or a combination of any of them.

[0127] Computer program products may contain a set of instructions that, when executed by a processor device, cause the processor to perform prescribed functions. These functions may be performed in conjunction with controlled devices in operable communication with the processor. Computer program products, which may include software, may be stored in a data store tangibly embedded on a storage medium, such as an electronic, magnetic, or rotating storage device, and may be fixed or removable (e.g., hard disk, floppy disk, thumb drive, CD, DVD).

[0128] Although an example of a system, which may be portable, has been described with reference to the above figures, other implementations may be deployed in other processing applications, such as desktop and networked environments.

[0129] Temporary auxiliary energy inputs may be received, for example, from chargeable or single use batteries, which may enable use in portable or remote applications. Some embodiments may operate with other DC voltage sources, such as a 9V (nominal) batteries, for example. Alternating current (AC) inputs, which may be provided, for example from a 50 / 60 Hz power port, or from a portable electric generator, may be received via a rectifier and appropriate scaling. Provision for AC (e.g., sine wave, square wave, triangular wave) inputs may include a line frequency transformer to provide voltage step-up, voltage step-down, and / or isolation.TPL Docket No.: 128-78-WO [01301 Although particular features of an architecture have been described, other features may be incorporated to improve performance. For example, caching (e.g., LI, L2, ...) techniques may be used. Random access memory may be included, for example, to provide scratch pad memory and or to load executable code or parameter information stored for use during runtime operations. Other hardware and software may be provided to perform operations, such as network or other communications using one or more protocols, wireless (e.g., infrared) communications, stored operational energy and power supplies (e.g., batteries), switching and / or linear power supply circuits, software maintenance (e.g., self-test, upgrades), and the like. One or more communication interfaces may be provided in support of data storage and related operations.

[0131] Some systems may be implemented as a computer system that can be used with various implementations. For example, various implementations may include digital circuitry, analog circuitry, computer hardware, firmware, software, or combinations thereof. Apparatus can be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device, for execution by a programmable processor; and methods can be performed by a programmable processor executing a program of instructions to perform functions of various embodiments by operating on input data and generating an output. Various embodiments can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and / or at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0132] Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, which may include a single processor or one of multiple processors of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random-access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including, by way of example, semiconductor memoryTPL Docket No.: 128-78-WO devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, ASICs (applicationspecific integrated circuits).

[0133] In some implementations, each system may be programmed with the same or similar information and / or initialized with substantially identical information stored in volatile and / or nonvolatile memory. For example, one data interface may be configured to perform auto configuration, auto download, and / or auto update functions when coupled to an appropriate host device, such as a desktop computer or a server.

[0134] In some implementations, one or more user-interface features may be custom configured to perform specific functions. Various embodiments may be implemented in a computer system that includes a graphical user interface and / or an Internet browser. To provide for interaction with a user, some implementations may be implemented on a computer having a display device. The display device may, for example, include an LED (light-emitting diode) display. In some implementations, a display device may, for example, include a CRT (cathode ray tube). In some implementations, a display device may include, for example, an LCD (liquid crystal display). A display device (e.g., monitor) may, for example, be used for displaying information to the user. Some implementations may, for example, include a keyboard and / or pointing device (e.g., mouse, trackpad, trackball, joystick), such as by which the user can provide input to the computer.

[0135] In various implementations, the system may communicate using suitable communication methods, equipment, and techniques. For example, the system may communicate with compatible devices (e.g., devices capable of transferring data to and / or from the system) using point-to-point communication in which a message is transported directly from the source to the receiver over a dedicated physical link (e.g., fiber optic link, point-to-point wiring, daisy-chain). The components of the system may exchange information by any form or medium of analog or digital data communication, including packet-based messages on a communication network. Examples of communication networks include, e.g., a LAN (local area network), a WAN (wide area network), MAN (metropolitan area network), wireless and / or optical networks, the computers and networks forming the Internet, or some combination thereof. Other implementations may transport messages by broadcasting to all or substantially all devices that arc coupled together by a communication network, for example, by using omni-directional radio frequency (RF) signals. Still other implementations may transport messages characterized by high directivity, such as RF signals transmitted using directional (i.e., narrow beam) antennas or infrared signals that may optionally be used with focusing optics. Still other implementations are possible using appropriate interfaces and protocols such as, by way of example and not intended to be limiting, USB 2.0, Firewire,TPL Docket No.: 128-78-WO ATA / IDE, RS-232, RS-422, RS-485, 802.11 a / b / g, Wi-Fi, Ethernet, IrDA, FDDI (fiber distributed data interface), token-ring networks, multiplexing techniques based on frequency, time, or code division, or some combination thereof. Some implementations may optionally incoiporate features such as error checking and correction (ECC) for data integrity, or security measures, such as encryption (e.g., WEP) and password protection.

[0136] In various embodiments, the computer system may include Internet of Things (loT) devices. loT devices may include objects embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to collect and exchange data. loT devices may be in-use with wired or wireless devices by sending data through an interface to another device. loT devices may collect useful data and then autonomously flow the data between other devices.

[0137] Various examples of modules may be implemented using circuitry, including various electronic hardware. By way of example and not limitation, the hardware may include transistors, resistors, capacitors, switches, integrated circuits, other modules, or some combination thereof. In various examples, the modules may include analog logic, digital logic, discrete components, traces and / or memory circuits fabricated on a silicon substrate including various integrated circuits (e.g., FPGAs, ASICs), or some combination thereof. In some embodiments, the module(s) may involve execution of preprogrammed instructions, software executed by a processor, or some combination thereof. For example, various modules may involve both hardware and software.

[0138] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. For example, advantageous results may be achieved if the steps of the disclosed techniques were performed in a different sequence, or if components of the disclosed systems were combined in a different manner, or if the components were supplemented with other components. Accordingly, other implementations are contemplated.

Claims

TPL Docket No.: 128-78-WO CLAIMSWhat is claimed is:

1. A system comprising:a data store comprising a program of instructions; and,a processor operably coupled to the data store such that, when the processor executes the program of instructions, the processor causes operations to be performed to automatically calibrate a photoelectric sensor (102) with respect to an operating environment, the operations comprising:in response to a teach initiation signal, activate the photoelectric sensor (102) in a dynamic teach mode;collect measurement data comprising a light intensity received by the photoelectric sensor (102);generate an environmental variation offset (EVO) based on the collected measurement data;sweep a sensor gain across a predetermined gain range;at each of the sensor gain, measure a switch point; andwhen the dynamic teach mode is terminated upon receiving a termination signal from a user interface (220), generate an environmentally stable switch point based on the EVO and the switch point, such that the photoelectric sensor (102) is automatically calibrated according to environmental noise without prior knowledge of the operating environment.

2. The system of claim 1, wherein the operations further comprise configure a drift filter (222) to maintain the environmentally stable switch point.

3. The system of claim 1, wherein, in response to the teach initiation signal, the photoelectric sensor (102) is operated in a light state.

4. The system of claim 1, wherein the collected measurement data comprises recording a minimum measured signal and an average measured signal.

5. The system of claim 4, wherein the EVO is generated as a difference between the minimum measured signal and the average measured signal.

6. The system of claim 5, wherein the environmentally stable switch point is generated based on an aggregation of the EVO, the switch point, and a user-configurable offset.TPL Docket No.: 128-78-WO 7. A computer-implemented method performed by at least one processor to automatically calibrate a photoelectric sensor with respect to an operating environment, the method comprising:in response to a teach initiation signal, activate the photoelectric sensor (102) in a dynamic teach mode;collect measurement data comprising a light intensity received by the photoelectric sensor (102);generate an environmental variation offset (EVO) based on the collected measurement data;sweep a sensor gain across a predetermined gain range;at each of the sensor gain, measure a switch point; andwhen the dynamic teach mode is terminated, generate an environmentally stable switch point based on the EVO and the switch point, such that the photoelectric sensor is automatically calibrated according to environmental noise without prior knowledge of the operating environment.

8. The computer-implemented method of claim 7, further comprise configures maintain the environmentally stable switch point by a drift filter (222).

9. The computer-implemented method of claim 7, wherein the collected measurement data comprises recording a minimum measured signal and an average measured signal.

10. The computer-implemented method of claim 9, wherein the EVO is generated as a difference between the minimum measured signal and the average measured signal.

11. The computer-implemented method of claim 10, wherein the environmentally stable switch point is generated based on an aggregation of the EVO, the switch point, and a user- configurable offset.

12. The computer-implemented method of claim 7, wherein the dynamic teach mode is terminated after a user-selected time period.

13. The computer-implemented method of claim 7, wherein the dynamic teach mode is terminated when a user input is received from a user interface (220).TPL Docket No.: 128-78-WO 14. A computer program product comprising a program of instructions tangibly embodied on a non-transitory computer readable medium wherein, when the instractions are executed on a processor, the processor causes sensor calibration operations to be performed to automatically calibrate a photoelectric sensor (102) with respect to an operating environment, the operations comprising:in response to a teach initiation signal, activate the photoelectric sensor (102) in a dynamic teach mode;collect measurement data comprising a light intensity received by the photoelectric sensor (102);generate an environmental variation offset (EVO) based on the collected measurement data;sweep a sensor gain across a predetermined gain range;at each of the sensor gain, measure a switch point; andwhen the dynamic teach mode is terminated, generate an environmentally stable switch point based on the EVO and the switch point, such that the photoelectric sensor is automatically calibrated according to environmental noise without prior knowledge of the operating environment.

15. The computer program product of claim 14, wherein the sensor calibration operations further comprise configure a drift filter (222) to maintain the environmentally stable switch point.

16. The computer program product of claim 14, wherein, in response to the teach initiation signal, the photoelectric sensor is operated in a light state.

17. The computer program product of claim 14, wherein the collected measurement data comprises recording a minimum measured signal and an average measured signal.

18. The computer program product of claim 17, wherein the EVO is generated as a difference between the minimum measured signal and the average measured signal.

19. The computer program product of claim 18, wherein the environmentally stable switch point is generated based on an aggregation of the EVO, the switch point, and a user-configurable offset.

20. The computer program product of claim 14, wherein the dynamic teach mode is terminated after a user-selected time period.

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