Method and system for detecting track defects

Abstract

A storage and fulfillment system is known in which a stack of storage containers is arranged within a grid framework structure. The containers are accessed from above by a load handling device operating on rails or tracks located on top of the grid framework structure. A method and system for detecting defects on the tracks or rails along which the load handling device travels is disclosed.

Description

[Technical Field]

[0001] The present invention relates to a method and system for detecting defects on tracks or rails on which load handling devices travel. [Background technology]

[0002] Some commercial and industrial activities require a system that allows for the storage and retrieval of a large number of different products. WO2015 / 185628A describes a storage and fulfillment system in which a stack of storage containers is placed within a grid storage structure. The containers are accessed by a load handling device that operates on rails or tracks located on top of the grid storage structure.

[0003] To ensure that the storage and fulfillment system operates reliably, it is important that the rails or tracks are free of defects, and it is against this background that the present invention has been devised. Summary of the Invention

[0004] There is a computer-implemented method for determining faults in a system, the system comprising a load handling device, a first set of tracks extending in a first direction, and a second set of tracks extending in a second direction transverse to the first direction, the load handling device configured to move on the tracks, wherein the load handling device comprises a sensor configured to monitor movement of the load handling device, and wherein the method includes: acquiring data generated by the sensors as the load handling device moves along the first and / or second set of tracks; determining that the data comprises anomalous data; and identifying defects in areas of the first and / or second set of trucks by mapping the anomaly data to location data of the load handling device, wherein a defect is identified if the number of times the anomaly data maps with the location data corresponding to the area meets a first threshold, meaning that a system defect can be identified with a high degree of confidence.

[0005] The sensors may detect vibrations of the load handling device as it moves along the first and / or second set of tracks, meaning that faults in the system are detected by the sensors.

[0006] The sensors may comprise accelerometers, inertial measurement units, gyroscopes, encoders or torque sensors coupled to at least one wheel or axle of the load handling device, meaning that data indicative of faults in the system may be obtained.

[0007] Determining that data is anomalous comprises detecting that the data deviates from the mode, median, or mean by a predetermined value or exceeds a reference value, meaning that data indicative of a system flaw can be identified.

[0008] When determining that the data contains anomalous data, data generated by the sensors during acceleration and deceleration of the load handling device may be excluded, as if the load handling device were moving at a constant speed, meaning that acceleration / deceleration due to system imperfections only would be detected by the sensors.

[0009] The sensors may generate data only in an axis transverse to the direction of movement of the load handling device, which reduces the data processing and / or storage burden when analyzing the data generated by the sensors.

[0010] The sensor may generate data in a first axis corresponding to the first direction, a second axis corresponding to the second direction, and a third axis transverse to the first and second directions, thereby improving accuracy in detecting defects in the system.

[0011] The first threshold may be a numerical value or a percentage value, and optionally the percentage value may comprise the ratio of the number of times the anomaly data maps to the location data corresponding to the region to the number of times the location data corresponds to the region, which means that different usage contexts can be taken into account when identifying defects in the system.

[0012] If the number of times the location data corresponds to the region meets a second threshold, the defect may be verified. This ensures that an area has been sampled a sufficient number of times before identifying a fault in the system.

[0013] The system may include multiple load handling devices, where a fault is verified if, for the multiple load handling devices, the number of times the anomaly data maps to location data corresponding to the region meets a third threshold, which increases confidence that a fault has been identified and rules out faulty sensors and / or errors when processing data from the sensors.

[0014] For multiple load handling devices, a fault may be verified if the number of times the location data corresponds to an area meets a fourth threshold, ensuring that an area has been sampled a sufficient number of times before identifying a fault in the system.

[0015] The system may include a plurality of load handling devices, wherein the method may further include determining a percentage of unique load handling devices for which data comprises anomalous data within a region to define a first value, and verifying that a defect is within the region if the first value exceeds a fifth threshold, thereby providing further confidence that a defect has been identified and ruling out faulty sensors and / or errors in processing data from the sensors.

[0016] The system may include a plurality of load handling devices, wherein the method may further include determining a total number of unique load handling devices with location data corresponding to the region to define a second value, and verifying that a fault is within the region if the second value exceeds a sixth threshold, thereby ensuring that the region has been sampled a sufficient number of times before identifying a fault in the system.

[0017] The second, third, fourth, fifth, and sixth thresholds may each be met to verify that the defect is within the region, maximizing the confidence that the defect has been identified.

[0018] The method may further comprise determining the type of defect by detecting a corresponding unique pattern in the data, which means that the nature of subsequent action and / or repair may be determined without first inspecting the system.

[0019] The types of defects may comprise at least one of debris on the first and / or second set of tracks, misalignment along and / or between the first and / or second set of tracks, damaged sections on the first and / or second set of tracks, and damaged intersections along and / or between the first and / or second set of tracks, meaning that the method is capable of detecting a wide variety of defects that adversely affect reliable operation of the system.

[0020] The first and second sets of tracks lie substantially in a horizontal plane and form a grid with a plurality of grid spaces, forming a plurality of vertical storage locations below the grid for containers stacked vertically between and guided by the uprights through the plurality of grid spaces, and optionally the or each load handling device is configured to lift containers from and / or lower containers into the vertical storage locations below the grid. This means that reliable operation of the system is guaranteed.

[0021] The region may comprise a grid space, meaning that operationally problematic regions within the system may be identified. The method may further comprise reconfiguring the system to avoid operating the load handling device within the grid space, meaning that the system may be performed in a way that minimizes the risk of failure, such as the load handling device tipping over, due to imperfections in the grid space.

[0022] The data may comprise multiple averages, each derived from the sensor as the load handling device moves across a respective grid space, which helps isolate grid spaces where system imperfections exist.

[0023] The method is carried out over a predetermined period of time, which means that the system can be periodically evaluated and compared for defects.

[0024] The method can further comprise generating a map, wherein the map shows areas in the system that have defects, meaning that defects can be easily found in the system.

[0025] There is a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to perform the method of the above aspects.

[0026] There is a data processing system comprising a processor configured to perform the method of the above aspect.

[0027] a system comprising a load handling device, a first set of tracks extending in a first direction, and a second set of tracks extending in a second direction transverse to the first direction, the load handling device configured to move on the tracks, wherein the load handling device comprises: a sensor configured to monitor movement of the load handling device; and the data processing system of the above aspect.

[0028] There is a map, wherein the map is generated using the method of the above aspect, the computer program of the above aspect, or the system of the above aspect, and the map indicates areas in the system that have defects.

[0029] The present invention will now be described with reference to one or more exemplary embodiments thereof as illustrated in the accompanying drawings. [Brief explanation of the drawings]

[0030] [Figure 1] FIG. 1 shows the storage structure and container. [Figure 2] FIG. 2 shows the track on top of the storage structure shown in FIG. [Figure 3] FIG. 3 shows a load handling device on top of the storage structure illustrated in FIG. [Figure 4] Figure 4 shows a single load handling device with the container lifting means in a lowered configuration. [Figure 5A] Figure 5A shows a cutaway view of a single load handling device with the container lifting means in a raised configuration. [Figure 5B] Figure 5B shows a cutaway view of a single load handling device with the container lifting means in a lowered configuration. [Figure 6] FIG. 6 shows a known load handling device, showing X and Y position sensors in the form of a "fifth" wheel mounted thereon. [Figure 7] FIG. 7 shows a diagram of a movement sensor and positioning system for a load handling device, according to one embodiment. [Figure 8] FIG. 8 illustrates communication between a load handling device and a master controller over a network, according to one embodiment. [Figure 9] FIG. 9 illustrates a method for determining faults in a system comprising a truck and a load handling device, according to one embodiment. [Figure 10] 1 illustrates a method for verifying defects in a system comprising a truck and a load handling device, according to one embodiment. [Figure 11] FIG. 11 illustrates a method for determining faults in a system comprising a truck and a load handling device, according to one embodiment. [Figure 12] FIG. 12 shows a "heat" map showing where defects are located in a system, according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0031] Online retail businesses that sell multiple product lines, such as online grocery stores and supermarkets, require systems that can store dozens, hundreds, or even thousands of different product lines. The use of a single product stack in such cases is impractical because a very large floor area would be required to accommodate all of the required stacks. Furthermore, it may be desirable to store smaller quantities of some items, such as perishable or infrequently ordered items, making a single product stack an inefficient solution.

[0032] International Patent Application WO98 / 049075A (Autostore), the contents of which are incorporated herein by reference, describes a system in which a multi-product stack of containers is arranged within a frame structure.

[0033] PCT Publication No. WO2015 / 185628A (Ocado) describes a further known storage and fulfilment system in which stacks of containers are arranged within a grid framework structure. The containers are accessed by load handling devices, otherwise known as "bots", which operate on trucks located on top of the grid framework structure. A system of this type is illustrated diagrammatically in Figures 1 to 3 of the accompanying drawings.

[0034] As shown in FIGS. 1 and 2, stackable containers 10, also known as "bins," are stacked on top of one another to form stacks 12. The stacks 12 are arranged within a grid framework structure 14 in a warehousing or manufacturing environment. The grid framework is made up of a plurality of storage columns, or grid columns. Each grid within the grid framework structure has at least one grid column for storing a stack of containers. FIG. 1 is a schematic perspective view of the grid framework structure 14, and FIG. 2 is an overhead view showing the stack 12 of bins 10 arranged within the framework structure 14. Each bin 10 typically holds multiple product items (not shown). The product items within the bins 10 may be the same product type or different product types, depending on the application.

[0035] The grid framework structure 14 includes a plurality of upright members 16 supporting horizontal members 18, 20. A first set of parallel horizontal members 18 are arranged perpendicular to a second set of parallel horizontal members 20 in a grid pattern, forming a plurality of horizontal grid structures supported by the upright members 16. The members 16, 18, 20 are typically fabricated from metal. The bins 10 are stacked between the members 16, 18, 20 of the grid framework structure 14 such that the grid framework structure 14 guards the stack 12 of bins 10 against horizontal movement and guides the vertical movement of the bins 10.

[0036] The top level of the grid framework structure 14 includes rails 22 arranged in a grid pattern across the top of the stacks 12. Referring to FIG. 3 , the rails or tracks 22 guide a plurality of load handling devices 30. A first set 22a of parallel rails 22 guides movement of the robotic load handling devices 30 in a first direction (e.g., X direction) across the top of the grid framework structure 14. A second set 22b of parallel rails 22, positioned perpendicular to the first set 22a, guides movement of the load handling devices 30 in a second direction (e.g., Y direction) perpendicular to the first direction. In this manner, the rails 22 allow the robotic load handling devices 30 to move laterally in two dimensions within the horizontal XY plane. The load handling devices 30 can be moved to a position above any of the stacks 12.

[0037] 4, 5A and 5B is described in PCT Patent Publication No. WO2015 / 019055 (Ocado), which is incorporated herein by reference, where each load handling device 30 covers a single grid space or grid cell of the grid framework structure 14. This arrangement allows for a higher density of load handlers and therefore a higher throughput for a system of a given size.

[0038] The load handling device 30 includes a vehicle 32 arranged to move on the rails 22 of the frame structure 14. A first set of wheels 34, consisting of a pair of wheels 34 at the front of the vehicle 32 and a pair of wheels 34 at the rear of the vehicle 32, are arranged to engage two adjacent rails of the first set 22a of rails 22. Similarly, a second set of wheels 36, consisting of a pair of wheels 36 on each side of the vehicle 32, are arranged to engage two adjacent rails of the second set 22b of rails 22. Each set of wheels 34, 36 can be raised and lowered so that either the first set of wheels 34 or the second set of wheels 36 is always engaged with the respective set of rails 22a, 22b. When the first set of wheels 34 is engaged with the first set of rails 22a and the second set of wheels 36 is fully lifted off the rails 22, the first set of wheels 34 can be driven to move the load handling device 30 in the X direction via a drive mechanism (not shown) housed in the vehicle 32. To achieve movement in the Y direction, the first set of wheels 34 is lifted off the rails 22 and the second set of wheels 36 is lowered into engagement with the second set 22b of the rails 22. The drive mechanism can then be used to drive the second set of wheels 36 to move the load handling device 30 in the Y direction.

[0039] The load handling device 30 is equipped with a lifting device, e.g., a crane mechanism, for lifting a storage container from above. The lifting device includes a winch tether or cable 38 wound on a spool or reel (not shown) and a gripper device 39. The lifting device shown in FIG. 4 includes a set of four vertically extending lifting tethers 38. The tethers 38 are connected to or near each of the four corners of the gripper device 39, e.g., a lifting frame, for releasable connection to the storage container 10. For example, each tether 38 is positioned at or near each of the four corners of the lifting frame. The gripper devices 39 are configured to releasably grip the top of the storage container 10 to lift the storage container 10 from a stack of containers in a storage system of the type shown in FIGS. 1 and 2. For example, the lifting frame 39 may include pins (not shown) that mate with corresponding holes (not shown) in a rim forming the top surface of the bin 10 and slide clips (not shown) engageable with the rim to grip the bin 10. The clips are driven into engagement with the bins 10 by a suitable drive mechanism housed within a lifting frame 39 that is powered and controlled by signals carried through the cable 38 itself or through a separate control cable (not shown).

[0040] To remove a bin 10 from the top of the stack 12, the load handling device 30 is moved in the X and Y directions so that the gripper device 39 is positioned above the stack 12. The gripper device 39 is then lowered vertically in the Z direction to engage the top bin 10 of the stack 12, as shown in FIGS. 4 and 5B. The gripper device 39 grasps the bin 10 and then pulls it, along with the attached bin 10, up onto the cable 38. At the top of its vertical movement, the bin 10 is held above the rails 22 housed within the vehicle body 32. In this manner, the load handling device 30 can be moved to different positions in the XY plane, carrying the bin 10 with it, to transport the bin 10 to another location. Upon reaching the target location (e.g., another stack 12, an access point in a storage system, or a conveyor belt), the bin or container 10 can be lowered from the container-receiving portion and released from the grabber device 39. The cable 38 is long enough to allow the load handling device 30 to pick and place bins from any level in the stack 12, including, for example, floor level.

[0041] As shown in Figure 4, multiple identical load handling devices 30 may be provided, each capable of operating simultaneously to increase system throughput. The system illustrated in Figure 4 may include specific locations known as ports, where bins 10 may be transported into or out of the system. Each port may have an additional conveyor system (not shown) associated with it, so that bins 10 transported to the port by the load handling device 30 may be transported by the conveyor system to another location, e.g., a picking station (not shown). Similarly, bins 10 may be moved by the conveyor system to the port from an external location, e.g., a bottling station (not shown), and transported by the load handling device 30 to a stack 12 to replenish stock in the system.

[0042] Each load handling device 30 can lift and move one bin 10 at a time. The load handling devices 30 have a container-receiving cavity or recess 40 in their lower part. The recess is sized to accommodate the container 10 when it is lifted by the crane mechanism, as shown in Figures 5A and 5B. When in the recess, the container 10 is lifted off the rails below so that the vehicle 32 can move laterally to a different grid location.

[0043] When a bin 10b that is not located at the top of a stack 12 (a "target bin") needs to be removed, the bins 10a above (a "non-target bin") must first be moved to allow access to the target bin 10b. This is accomplished in an operation hereinafter referred to as "digging." Referring to FIG. 3, during a digging operation, one of the load handling devices 30 sequentially lifts each non-target bin 10a from the stack 12 containing the target bin 10b and places it in an empty position in another stack 12. The target bin 10b can then be accessed by the load handling device 30 and moved to a port for further transportation.

[0044] Each of the load handling devices 30 is remotely operable under the control of a central computer. Each individual bin 10 in the system is tracked so that the appropriate bin 10 can be retrieved, transported, and replaced as needed. For example, the location of each non-target bin 10 is logged so that non-target bin 10a can be tracked during digging operations.

[0045] Wireless communications and networks can be used to provide a communications infrastructure from a master controller, e.g., via one or more base stations, to one or more load handling devices operating on the grid structure. In response to receiving commands from the master controller, a controller within the load handling device is configured to control various drive mechanisms and control the movement of the load handling device. For example, the load handling device can be instructed to retrieve a container from a target storage column at a specific location on the grid structure. This command can include various movements in the XY plane of the grid structure 15. As described above, upon reaching the target storage column, the lifting mechanism can be operated to grasp and lift the storage container 10. Once the container 10 is received within the container receiving space 40 of the load handling device 30, the container 10 is then transported to another location on the grid structure 15, e.g., a “drop-off port.” At the drop-off port, the container 10 is lowered to an appropriate picking station to enable retrieval of any items within the storage container. Movement of the load handling devices 30 on the grid structure 15 may also involve the load handling devices 30 being directed to move to charging stations typically located on the periphery of the grid structure 15 .

[0046] To operate the load handling devices 30 on the grid structure 15, each of the load handling devices 30 is equipped with a motor for driving the wheels 34, 36. The wheels 34, 36 may be driven via one or more belts connected to the wheels or may be individually driven by motors integrated into the wheels. In the case of a single-cell load handling device (where the footprint of the load handling device 30 occupies a single grid cell 17), the motors for driving the wheels may be integrated into the wheels due to the limited space available within the vehicle body. For example, the wheels of a single-cell load handling device are driven by respective hub motors. Each hub motor includes an outer rotor having multiple permanent magnets arranged to rotate around a wheel hub with coils forming an inner stator.

[0047] The system described with reference to Figures 1 to 4 has many advantages and is suitable for a wide range of storage and retrieval operations. In particular, it allows for very dense storage of products and provides a very economical way of storing a large range of different items in the bins 10, while still allowing reasonable and economical access to all of the bins 10 when required for picking.

[0048] In storage systems of the type shown in FIGS. 1-3 , it is useful to determine the precise location of a given load handling device 30 operating on the grid structure 15. Each load handling device 30 is sent control signals, e.g., in the form of a motion control profile, to move along a predetermined path from one location to another on the grid structure. For example, a given load handling device 30 may be commanded to move to a particular location within the grid structure 15 and lift a target container from a stack of containers at the particular location. When multiple such devices 30 move along respective trajectories on the grid structure 15, it is useful to know the precise location of each given load handling device 30, e.g., relative to the grid structure 15, so that the predetermined trajectories can be monitored in real time against the “actual” location of the corresponding load handling device 30. For example, one or more trajectories may be adjusted, and / or the control signals sent to the devices 30 may be adjusted to follow the corresponding trajectories, so that the devices 30 reach their target locations sufficiently accurately and / or to avoid other devices 30 following their respective trajectories.

[0049] 6 shows a known load handling device 30 with one or more position sensors 98a, 98b for measuring the position of the load handling device relative to the grid structure 15. The position sensors 98a, 98b each comprise a so-called "fifth" wheel, in the sense that there is an additional fifth wheel in each of the first and second sets of wheels for monitoring the position of the load handling device in a first direction and a second direction on the grid structure, respectively.

[0050] 6, a first "fifth" wheel 98a is mounted adjacent to one of the first set of wheels 34, and a second "fifth" wheel 98b is mounted adjacent to one of the second set of wheels 36. The first "fifth" wheel 98a corresponding to the first position sensor is configured to engage the rail (or "track") 22 when the load handling device 30 is traveling in a first direction, such that rotation of the first "fifth" wheel is an indication of the position and heading of the load handling device 30 with respect to time. Similarly, the second "fifth" wheel 98b corresponding to the second position sensor is configured to engage the track 22 when the load handling device 30 is traveling in a second direction, such that rotation of the second "fifth" wheel is an indication of the position and heading of the load handling device 30 in the second direction with respect to time. The first and second directions may be the X and Y directions, respectively, along the track 22 as described.

[0051] In FIG. 6 , each of the one or more position sensors 98 a, 98 b comprises an incremental encoder comprising a rotating electromechanical device that generates pulses as the respective “fifth” wheel rotates. For example, pulses are generated for a predetermined amount of angular rotation of the “fifth” wheel. Thus, the pulses indicate the position and direction of rotation of the “fifth” wheel 98 a, 98 b, which can be translated into displacement of the load handling device 30 relative to the grid structure 15. The “fifth” wheel is mounted on an arm and biased downward into engagement with the track 22 of the grid structure 15. As an alternative to the “fifth” wheel 98 a, 98 b, an incremental encoder could be used to generate pulses as any one wheel in the set of wheels 34, 36 rotates to determine the position of the load handling device 30.

[0052] Reliable operation of the storage and fulfillment system described above with reference to FIGS. 1 through 6 relies on load handling devices moving unimpeded along the tracks. In practice, the tracks can develop defects that impede the movement of the load handling devices. For example, debris such as metal, nut, or bolt chips may become lodged within the tracks. Alternatively, the first and / or second sets of tracks may become misaligned. In other words, one set of tracks in the X direction (or Y direction) may no longer be parallel to an adjacent set of tracks in the X direction (or Y direction), or the first and second sets of tracks may no longer be perpendicular. Similarly, sections of the tracks in the X direction (or Y direction) may become damaged. For example, guides on the tracks for the wheels of the load handling devices may be deformed or bent inward, such that the sides of the wheels of the load handling devices experience higher friction when passing over the deformed section. As another example, there may be damaged intersections, i.e., points where the X and Y direction tracks intersect and are joined by nuts / bolts, welding, or any suitable joining means. A section of track in the X (or Y) direction could become disconnected from the intersection (perhaps due to a joint failure), causing a drop in the load handling device moving towards the intersection, and then a steep tilt (or upward judder) as it moves onto the intersection. Similarly, a section of track in the X (or Y) direction could become disconnected from the intersection (perhaps due to a joint failure), causing a drop in the load handling device moving towards the intersection, and then a steep drop (or downward judder) as it moves onto the intersection.

[0053] Identifying such defects is important to ensure reliable operation of the storage and fulfillment system. Due to the significant size of the horizontal grid structure 15 and the significant number of load handling devices, it is difficult, if not impossible, to observe such defects directly via visual inspection or indirectly via a camera. Furthermore, defects may be subtle in nature and not easily identified, yet still adversely affect the reliable operation of the storage and fulfillment system. A method and system for detecting track defects is needed.

[0054] FIG. 7 shows a schematic diagram 200 of a load handling device 30 in accordance with the present invention. One or more encoders 210 are used for the wheels of the load handling device described above to detect the positioning of the load handling device on the horizontal grid structure 115. The one or more encoders are located within the load handling device in this example. In FIG. 7, only one pair of wheels 234 is shown for moving the load handling device 30 in the X direction on the grid structure 115. However, a complete set of wheels for each of the X and Y directions can be included, such as the first set of wheels 134 and the second set of wheels 136 described in the previous example.

[0055] Additionally, at least one movement sensor 220 is used to monitor the movement of the load handling device. The output of the movement sensor 220 indicates whether the load handling device is moving unobstructed and therefore “normally” along the horizontal grid structure 15. The movement sensor 220 also indicates whether the movement of the load handling device is “abnormal” due to being obstructed by an imperfection on the horizontal grid structure 15. More generally, the movement sensor is configured to monitor the movement of the load handling device as it moves along the first and / or second set of tracks and generate data that can be used to distinguish between “normal” and “abnormal” movement of the load handling device. How the data from the movement sensor 220 is used to distinguish between “normal” and “abnormal” movement is described in more detail below. The movement sensor 220 can take the form of an accelerometer, an inertial measurement unit, a gyroscope, an encoder, or a torque sensor coupled to at least one wheel or axle of the load handling device.

[0056] 7, the encoder 210 and the movement sensor 220 used to determine position are shown as separate elements, but it should be understood that the encoder can also be configured to monitor movement. For example, the encoder can detect a pattern associated with "normal" movement of the load handling device, and deviations from the expected pattern can indicate "abnormal" movement of the load handling device. In other words, a single sensor can be used to detect the positioning of the load handling device and monitor its movement. The load handling device 200 can process data from the encoder 210 and the movement sensor using the processor 240 and store this data in the storage device 230. The data in the storage device 230 can be periodically transmitted over one or more networks, such as a base station, for further processing.

[0057] 8 shows that each load handling device transmits data to a computing device 174 via a network 176. Thus, data relating to the monitored movement and positioning of each load handling device 30 on the horizontal grid structure 15 is obtained by the computing device 174.

[0058] FIG. 9 illustrates steps of a method 900 for determining a fault in a system. The system includes a load handling device 30, a first set of tracks 17 extending in a first direction, and a second set of tracks 19 extending in a second direction transverse to the first direction, the load handling device configured to move on the tracks, wherein the load handling device includes a sensor configured to monitor movement of the load handling device (such as that shown in FIG. 7). In step 910, data generated by the sensor as the load handling device moves along the first and / or second set of tracks is acquired. The data may be acquired by a computing device 174, which then performs the method of FIG. 9. In step 920, the data is analyzed and it is determined that the data comprises anomalous data. In other words, at least a subset or portion of the data indicates that a section of the first and / or second set of tracks impedes movement of the load handling device. How to determine from the data that movement of the load handling device is impeded is described in more detail below. In step 930, defects in a region (e.g., grid space) of the first and / or second set of tracks are identified by mapping the anomalous data to position data of the load handling device, where a defect is identified if the number of times the anomalous data maps with position data (e.g., generated as described above in connection with FIG. 6) corresponding to the region meets a first threshold. In other words, a defect exists in a region of the horizontal grid structure if the region results in the load handling device experiencing anomalous movement a significant number of times. The mapping process may be accomplished by using timestamps for the data and position data generated by the movement sensor 910. Alternatively, the data and position data generated by the movement sensor 210 may be periodically combined as they are generated.

[0059] The first threshold can be set taking into account various factors, such as the period over which the data is analyzed, the number and length of trips / strokes made by the load handling device, and the size of the horizontal grid structure 15. For example, if the load handling device covers a large area of ​​the horizontal grid structure 15 a significant number of times per day, the threshold can be set higher because it can be assumed that it will visit the area several times and therefore should detect a defect at each visit. The first threshold can be a number or a percentage value, where the percentage value comprises the ratio of the number of times that anomalous data maps with the location data corresponding to the area. The first threshold can be set appropriately and is used to indicate the confidence level that a defect actually exists. If the first threshold is not used and only the mapping of anomalous data to location data is used, other reasons for the anomalous data cannot be excluded. Other reasons include normal operation when lifting / dropping a container with a large force in an adjacent area causes an anomalous reading or when the movement sensor 220 inaccurately records data. Of course, it will be appreciated that the use of a first threshold is not required and that a simple mapping of anomaly data to position data may be used to flag any potential defects.

[0060] Assuming that either the first and / or second set of tracks has a defect-free section, the movement sensor 210 of the load handling device moving along that section will record a range of data that falls within a normal distribution. In an example where the movement sensor 210 is an accelerometer, the acceleration data recorded along each axis (along the X direction, the Y direction, and the Z direction transverse to both the X and Y directions) will fall within a normal distribution. Theoretically, when the load handling device is moving at a constant speed, the accelerometer should record zero acceleration along the X and Y directions. Similarly, the Z axis should experience a constant acceleration due to gravity, which can also be corrected to zero. For this reason, the reference data used to derive the normal distribution can also exclude the acceleration and deceleration phases of the load handling device and include only data generated when the load handling device is moving at a constant speed. This means that any detected vibrations or accelerations are due to defects. It will be understood that a normal distribution can be derived by using multiple different sections of the track. It will also be understood that a normal distribution can be generated using any one of the acceleration axes. Alternatively, all axes may be summed or averaged to produce a normal distribution.

[0061] After the normal distribution is generated, it can be compared with data obtained from the motion sensors 210 on any section of the first and / or second set of tracks. Given that the normal distribution can be derived to include only data generated when the load handling device is moving at a constant velocity, the data obtained from the motion sensors 210 can be filtered to include only data generated when the load handling device is moving at a constant velocity (on any section of the first and / or second set of tracks). The comparison can be based on average readings from the motion sensors derived over a period of time, such as would typically be required to move across one grid space. Similarly, the comparison can be based only on an axis transverse to the direction of motion, where the axis corresponds to one of the X, Y, or Z directions. This can reduce the amount of data that must be accumulated and compared while minimizing the impact on accuracy. For example, if the load handling device is moving in the Y direction, the X-direction sensor axis (and vice versa) should detect lateral movement due to deformation or bending, or rocking motion due to debris. Similarly, using only the Z-direction sensor axis will detect bumps in the track caused by debris or a sudden incline / descent. Therefore, specific axes can be analyzed individually to determine the nature of the defect. Alternatively, data from any two or all axes can be compared to maximize accuracy, while still retaining data from individual axes to determine the nature of the defect. More generally, any axis capable of detecting vibrations in the load handling device can be used.

[0062] Thus, any data acquired from the motion sensors 210 on any section of the first and / or second set of tracks that falls outside the normal distribution can be classified as anomalous. What is considered outside the normal distribution can be freely selected, but may be, for example, a value that exceeds an integral number of standard deviations. In other words, determining that data is anomalous comprises detecting that the data deviates from a mode, median, or mean by a predetermined value. Similarly, determining that data is anomalous comprises detecting that the data exceeds an absolute value (or its distance from 0, which is the mode, median, or mean of the normal distribution). Similarly, the normal distribution provides a reference value from which anomalous data can be determined. While the specific example of an accelerometer has been described, it will be understood that a normal distribution or reference value can also be generated using an inertial measurement unit, a gyroscope, an encoder (described above in connection with FIG. 7), or a torque sensor coupled to at least one wheel or axle of the load handling device. In general, any sensor capable of detecting vibrations of the load handling device is contemplated.

[0063] It is further envisioned that each defect can produce a “signature” vibration pattern, and thus, by analyzing the data for the presence of the “signature,” the type of defect can be identified. In other words, a particular defect is reflected in a corresponding unique pattern in the data. For example, once enough data is collected and analyzed, it can be inferred that debris on the first and / or second set of tracks produces a first unique pattern in the data, misalignment along and / or between the first and / or second set of tracks produces a second unique pattern in the data, damaged sections on the first and / or second set of tracks produces a third unique pattern in the data, and damaged intersections along and / or between the first and / or second set of tracks produces a fourth unique pattern in the data. This means that the type of defect can be determined without first inspecting the system.

[0064] FIG. 10 shows a method 1000 for verifying defects determined by the method of FIG. 9. In step 1010, the method of FIG. 9 can be improved by ensuring that the load handling device “samples” the area a sufficient number of times. Even if the first threshold is met, it may be based on an insufficient sample size. A second threshold can be used to ensure that the area has been sufficiently sampled. The second threshold can be set taking into account various factors, such as the period over which the data is analyzed and the size of the horizontal grid structure 15. Thus, if the number of times the location data corresponds to the area meets the second threshold, the defect is verified. It will be appreciated that instead of using a second threshold to verify the defect, both the first and second thresholds must be met before the defect is identified.

[0065] The fault may be further verified by acquiring 1020 data from all load handling devices on the horizontal grid structure 15 to verify the fault.

[0066] In step 1030, a defect is verified if, for a plurality of load handling devices, the number of times that the anomalous data maps with location data corresponding to the region meets a third threshold. The third threshold can be set taking into account various factors, such as the period over which the data is analyzed, the number of load handling devices, and the size of the horizontal grid structure 15. The third threshold may be a numerical value or a percentage value, where the percentage value comprises the ratio of the number of times that the anomalous data maps with location data corresponding to the region. The third threshold can be set appropriately and is used to indicate a confidence level that a defect actually exists. In step 1040, a fourth threshold can be used to ensure that the region has been adequately sampled. The fourth threshold can be set taking into account various factors, such as the period over which the data is analyzed, the number of load handling devices, and the size of the horizontal grid structure 15. Thus, a defect is verified if, for a plurality of load handling devices, the number of times that the location data corresponds to the region meets the fourth threshold. It will be appreciated that instead of using either or both of the third and fourth thresholds to verify a defect, all of the first, second, third, and / or fourth thresholds must be met before a defect is identified.

[0067] In step 1050, the percentage of unique load handling devices whose data comprise anomalous data in the region is used to define a first value. That is, for all of the different load handling devices in operation, it is determined how many of them generated anomalous data in that region. The greater the number of unique load handling devices that detected anomalous movement, the more likely a defect exists. Other explanations, such as a faulty motion sensor 210 or erroneous data transmission, are unlikely to occur across multiple unique load handling devices. If the first value meets a fifth threshold, a defect can be verified. The fifth threshold can be set taking into account various factors, such as the time period over which the data is analyzed, the number of unique load handling devices, and the size of the horizontal grid structure 15. The fifth threshold may be a number or a percentage value, where the percentage value comprises the ratio of the number of times the anomalous data maps to location data corresponding to the region (unique load handling devices) to the number of times the location data corresponds to the region. The fifth threshold can be set as appropriate and is used to indicate a confidence level that a defect actually exists. In step 1060, a sixth threshold may be used to ensure that the region has been sufficiently sampled by unique load handling devices. The sixth threshold may be set taking into account various factors, such as the period over which data is analyzed, the number of unique load handling devices, and the size of the horizontal grid structure 15. Thus, if, for multiple unique load handling devices, the number of times the location data corresponds to the region meets the sixth threshold, a defect is verified. It will be appreciated that instead of using either or both of the fifth and sixth thresholds to verify a defect, all of the first, second, third, fourth, fifth, and / or sixth thresholds must be met before a defect is identified.

[0068] With respect to Figure 10, it will be appreciated that any combination of thresholds may be selected to verify or identify defects, i.e., any one or combination of the first, second, third, fourth, fifth, and sixth thresholds may be selected as appropriate.

[0069] While the method of FIG. 10 is used to complement data derived from a single load handling device, as in FIG. 9, it should be understood that data from all of the load handling devices may be required before a fault can be detected. For example, the process of FIG. 9 can be modified to use data generated by each motion sensor 210 of each load handling device. Thus, any instance of anomalous data from any load handling device is mapped to location data. A fault is then identified in a region if the number of times the mapping is performed for that region meets a first threshold. The aforementioned first threshold (and the fourth, fifth, and sixth thresholds) can, of course, be scaled according to the number of load handling devices in use. In this embodiment, the first threshold may be substantially equal to the aforementioned third threshold. Thus, FIG. 11 illustrates steps of a method 1000 for determining faults in a system. The system includes a plurality of load handling devices 30, a first set of tracks 17 extending in a first direction, and a second set of tracks 19 extending in a second direction transverse to the first direction, each load handling device configured to move on the tracks, wherein each load handling device includes a sensor configured to monitor movement of the load handling device. In step 1110, data generated by the sensors as the load handling device moves along the first and / or second set of tracks is acquired. The data may be acquired by a computing device 174, which then performs the method of FIG. 11. In step 1120, the data is analyzed and it is determined that the data comprises anomalous data, as described above. In step 1130, defects in regions (e.g., grid spaces) on the first and / or second sets of tracks are identified by mapping the anomalous data to position data of the load handling devices, wherein a defect is identified if the number of times the anomalous data maps to position data (e.g., generated as described above) corresponding to the region meets a first threshold. In other words, if an area of ​​the horizontal grid structure causes the load handling device to experience abnormal movement a significant number of times, then a defect exists in that area.

[0070] Defects determined by the method of Figure 11 can be further verified by using steps 1040, 1050, and 1060 of Figure 10. Again, it will be appreciated that instead of using any of the fourth, fifth, and sixth thresholds to verify a defect, all of the fourth, fifth, and sixth thresholds must be met before a defect is identified. It will also be appreciated that any combination of thresholds can be selected to verify or identify a defect. That is, any one or combination of the first, fourth, fifth, and sixth thresholds can be selected as appropriate.

[0071] The above method can be performed on data collected over a period of time, such as hours, days, weeks, and months. Performing the above method can identify areas of the horizontal grid structure that have defects. Both the location and severity (i.e., persistent anomalous data) can be presented on a map (or "heat" map). A heat map or map is a 2D representation of the horizontal grid structure 15, where the color of the defective area can be used to indicate the severity of the defect. An exemplary heat map 1200 is shown in FIG. 12, where an area 1210 is shown along a scale 1220 as experiencing highly anomalous data (e.g., a load handling device experiences significant vibration when moving through this area). The two axes of the heat map can be used to indicate the exact area / location of the defect. Daily snapshots of the heat map can be used to construct a video or time lapse of the horizontal grid structure 15 to see how the defect developed over time.

[0072] While the above method is described in the context of detecting defects, it will be appreciated that the same method can easily detect the correct operation of a system. That is, if the movement sensors produce only normal data, the grid can be assumed to be free of defects. This is particularly useful when verifying whether newly constructed tracks and / or repairs are robust.

[0073] In this document, the phrase "movement in the n-direction," where n is one of x, y, and z (and related phrases) is intended to mean movement substantially along or parallel to the n-axis in either direction (i.e., toward the positive end of the n-axis or toward the negative end of the n-axis).

[0074] In this document, the term "connect" and its derivatives are intended to encompass the possibilities of direct and indirect connections. For example, "x is connected to y" is intended to encompass the possibilities of x being directly connected to y with no intervening components, and the possibilities of x being indirectly connected to y with one or more intervening components. When a direct connection is intended, "directly connected," "directly connected," or similar terms are used. Similarly, the term "support" and its derivatives are intended to encompass the possibilities of direct and indirect contact. For example, "x supports y" is intended to encompass the possibilities of x directly supporting and directly contacting y with no intervening components, and the possibilities of x indirectly supporting y with one or more intervening components that contact x and / or y. The term "mount" and its derivatives are intended to encompass the possibilities of direct and indirect attachment. For example, "x is attached to y" is intended to include the possibility that x is directly attached to y with no intervening components, and the possibility that x is indirectly attached to y with one or more intervening components.

[0075] As used herein, the term "comprises" and its derivatives are intended to have an inclusive, rather than exclusive, meaning. For example, "x comprises y" is intended to include the possibility that x includes one and only one y, multiple ys, or one or more ys and one or more other elements. When an exclusive meaning is intended, the phrase "x is composed of y" is used to mean that x includes only y and nothing else.

[0076] As used herein, a "controller" is intended to include any hardware suitable for controlling (e.g., providing instructions to) one or more other components. For example, a processor with one or more memories and appropriate software may process data for one or more components and send appropriate instructions to the components to enable them to perform their / their intended function.

[0077] As used in this disclosure, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0078] The present invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In a preferred embodiment, the present invention is implemented in software.

[0079] Furthermore, the present invention may take the form of a computer program product embodied as a computer-readable medium having computer-executable code thereon for use by or in connection with a computer. In the context of this document, a computer-readable medium may be any tangible apparatus that can contain, store, communicate, propagate, or transfer a program for use by or in connection with a computer. Furthermore, a computer-readable medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of computer-readable media include semiconductor or solid-state memory, magnetic tape, removable computer diskettes, random access memory (RAM), read-only memory (ROM), rigid magnetic disks, and optical disks. Current examples of optical disks include compact disc-read-only memory (CD-ROM), compact disc-read / write (CD-R / W), and DVD.

[0080] The flow diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods according to various embodiments of the present invention. In this regard, each block in the flow diagrams may represent a portion, segment, or module of code, comprising one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions shown in the blocks may occur out of the order shown in the figures. For example, depending on the functionality involved, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may be executed in the reverse order. It should also be noted that each block of the flow diagrams, and combinations of blocks in the flow diagrams, may be implemented by a special-purpose hardware-based system that performs the specified functions or operations, or that executes a combination of special-purpose hardware and computer instructions.

[0081] It will be understood that the above description is given by way of example only, and that various modifications may be made by those skilled in the art. Although various embodiments have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous changes to the disclosed embodiments without departing from the scope of the invention.

[0082] The following is a non-exhaustive list of embodiments that can be or are claimed.

[0083] 1. A computer-implemented method for determining faults in a system, the system comprising a load handling device (30), a first set of tracks (17) extending in a first direction, and a second set of tracks (19) extending in a second direction transverse to the first direction, the load handling device configured to move on the tracks, wherein the load handling device comprises a sensor configured to monitor movement of the load handling device, and wherein the method comprises: acquiring data generated by the sensors as the load handling device moves along the first and / or second set of tracks; determining that the data comprises anomalous data; and identifying defects in areas of the first and / or second set of trucks by mapping the anomaly data to position data of the load handling device.

[0084] 2. The method of embodiment 1, wherein a defect is identified when the number of times the anomaly data maps with the location data corresponding to the region meets a first threshold.

[0085] 3. The method of any preceding embodiment, wherein the sensor detects vibrations of the load handling device as it moves along the first and / or second set of tracks.

[0086] 4. The method of any preceding embodiment, wherein the sensor comprises an accelerometer, an inertial measurement unit, a gyroscope, an encoder, or a torque sensor coupled to at least one wheel or axle of the load handling device.

[0087] 5. Determining that data is anomalous means that the data, deviates from the mode, median, or mean by a predetermined value, or 10. The method of any preceding embodiment, comprising detecting that a reference value is exceeded.

[0088] 6. The method of any preceding embodiment, wherein data generated by the sensors during acceleration and deceleration of the load handling device, such that the load handling device is moving at a constant speed, is excluded when the data is determined to comprise anomalous data.

[0089] 7. The method of any preceding embodiment, wherein the sensor generates data only in an axis transverse to the direction of movement of the load handling device.

[0090] 8. A method as described in embodiments 1 to 6, wherein the sensor generates data in a first axis corresponding to a first direction, a second axis corresponding to a second direction, and a third axis transverse to the first and second directions.

[0091] 9. A method according to any of the preceding embodiments, wherein the first threshold is a number or a percentage value, and optionally the percentage value comprises a ratio of the number of times that anomaly data maps with location data corresponding to the region to the number of times that the location data corresponds to the region.

[0092] 10. The method of any preceding embodiment, wherein a defect is verified if the number of times the location data corresponds to the region meets a second threshold.

[0093] 11. A method according to any of the preceding embodiments, wherein the system comprises a plurality of cargo handling devices, and wherein, for the plurality of cargo handling devices, a defect is verified if the number of times that the anomaly data maps with the location data corresponding to the region meets a third threshold.

[0094] 12. The method of embodiment 11, wherein for multiple load handling devices, a defect is verified if the number of times the location data corresponds to an area meets a fourth threshold.

[0095] 13. The system comprises a plurality of load handling devices, wherein the method comprises: determining a percentage of unique load handling devices with anomalous data in the data region to define a first value; 11. A method as recited in any preceding embodiment, comprising verifying that the defect is within the region if the first value exceeds a fifth threshold.

[0096] 14. The system comprises a plurality of load handling devices, wherein the method comprises: determining a total number of unique load handling devices having location data corresponding to the region to define a second value; 11. The method of embodiment 1-10, further comprising: if the second value exceeds a sixth threshold, verifying that there is a defect in the region.

[0097] 15. The method of any one of embodiments 10 to 14, wherein the first, second, third, fourth, fifth, and sixth thresholds are met, respectively, or any combination of the first, second, third, fourth, fifth, and sixth thresholds is met, to verify that the defect is within the region.

[0098] 16. The method of any preceding embodiment, further comprising determining the type of defect by detecting a corresponding unique pattern in the data.

[0099] 17. The type of defect is: debris on the first and / or second set of tracks; misalignment along and / or between the first and / or second sets of tracks; a damaged section on the first and / or second set of tracks;

[0013] 3. The method of any preceding embodiment, comprising at least one of: damaged intersections along and / or between the first and / or second sets of tracks.

[0100] 18. A method according to any preceding embodiment, wherein the first and second sets of tracks are in a substantially horizontal plane and form a grid with a plurality of grid spaces, forming a plurality of vertical storage locations below the grid for containers stacked between and guided by the uprights vertically through the plurality of grid spaces, and optionally the or each load handling device is configured to lift containers from and / or lower containers into the vertical storage locations below the grid.

[0101] 19. The method of embodiment 18, wherein the region comprises a grid space, and the method optionally further comprises reconfiguring the system to avoid operating the or each load handling device within the grid space.

[0102] 20. The method of embodiment 19, wherein the data comprises a plurality of average values, wherein each average value is derived from a sensor as the load handling device moves across a respective grid space.

[0103] 21. The method of any preceding embodiment, wherein the method is carried out for a predetermined period of time.

[0104] 22. The method of any preceding embodiment, further comprising generating a map, wherein the map indicates areas in the system having defects.

[0105] 23. A computer program comprising instructions, the instructions causing the computer to perform the method of any one of embodiments 1 to 22 when the program is executed by a computer.

[0106] 24. A data processing system comprising a processor configured to perform the method of any one of embodiments 1 to 22.

[0107] 25. A system comprising: a load handling device (30); a first set of tracks (17) extending in a first direction; and a second set of tracks (19) extending in a second direction transverse to the first direction, the load handling device configured to move on the tracks, wherein the load handling device comprises: a sensor configured to monitor movement of the load handling device; A system comprising the data processing system of embodiment 24.

[0108] 26. A map generated using the method of any one of embodiments 1 to 22, the computer program of embodiment 23, or the system of embodiment 24 or 25, wherein the map indicates areas in the system that have defects.

[0109] 27. A computer-implemented method for determining faults in a system, the system comprising a plurality of load handling devices (30), a first set of tracks (17) extending in a first direction, and a second set of tracks (19) extending in a second direction transverse to the first direction, the load handling devices configured to move on the tracks, wherein each load handling device comprises a sensor configured to monitor movement of the load handling device, and wherein the method comprises: acquiring data generated by the sensors as the load handling device moves along the first and / or second set of tracks; determining that the data comprises anomalous data; and identifying defects in areas of the first and / or second set of trucks by mapping the anomaly data to position data of the load handling device.

[0110] 28. The method of embodiment 27, wherein a defect is identified when the number of times the anomaly data maps with the location data corresponding to the region meets a first threshold.

[0111] 29. The method of embodiment 27 or 28, wherein the sensor detects vibrations of the load handling device as it moves along the first and / or second set of tracks.

[0112] 30. The method of embodiments 27-29, wherein the sensor comprises an accelerometer, an inertial measurement unit, a gyroscope, an encoder, or a torque sensor coupled to at least one wheel or axle of the load handling device.

[0113] 31. Determining that data is anomalous means that the data: deviates from the mode, median, or mean by a predetermined value, or 31. The method of embodiment 27 to 30, comprising detecting that the reference value is exceeded.

[0114] 32. The method of embodiments 27 to 31, wherein data generated by the sensor during acceleration and deceleration of the load handling device, such that the load handling device is moving at a constant speed, is excluded when it is determined that the data comprises anomalous data.

[0115] 33. The method of embodiments 27 to 32, wherein the sensor generates data only in an axis transverse to the direction of movement of the load handling device.

[0116] 34. A method according to any one of embodiments 27 to 33, wherein the sensor generates data in a first axis corresponding to a first direction, a second axis corresponding to a second direction, and a third axis transverse to the first and second directions.

[0117] 35. A method as described in embodiments 27 to 34, wherein the first threshold is a number or a percentage value, and optionally the percentage value comprises a ratio of the number of times that abnormal data maps with location data corresponding to the region to the number of times that location data corresponds to the region.

[0118] 36. The method of embodiments 27 to 35, wherein for a plurality of load handling devices, a defect is verified if the number of times the location data corresponds to an area meets a second threshold.

[0119] 37. The method is determining a percentage of unique load handling devices whose data has anomalous data in the region to define a first value; 37. The method of embodiment 27 to 36, further comprising verifying that the defect is within the region if the first value exceeds a fifth threshold.

[0120] 38. The method is determining a total number of unique load handling devices having location data corresponding to the region to define a second value; 38. The method of embodiment 26 to 37, further comprising verifying that there is a defect in the region if the second value exceeds a sixth threshold.

[0121] 39. The method of any one of embodiments 35 to 38, wherein the first, second, fifth, and sixth thresholds are met, respectively, or any combination of the first, second, fifth, and sixth thresholds is met to verify that the defect is within the region.

[0122] 40. The method of any one of embodiments 26 to 39, further comprising determining the type of defect by detecting a corresponding unique pattern in the data.

[0123] 41. The type of defect is: debris on the first and / or second set of tracks; misalignment along and / or between the first and / or second sets of tracks; a damaged section on the first and / or second set of tracks;

[0013] 3. The method of any preceding embodiment, comprising at least one of: a damaged intersection along and / or between the first and / or second sets of tracks.

[0124] 42. The method of any one of embodiments 27 to 41, wherein the first and second sets of tracks are in a substantially horizontal plane and form a grid with a plurality of grid spaces, forming a plurality of vertical storage locations below the grid for containers stacked between and guided by the uprights vertically through the plurality of grid spaces, and optionally the or each load handling device is configured to lift containers from and / or lower containers into the vertical storage locations below the grid.

[0125] 43. The method of embodiment 42, wherein the region comprises a grid space, and the method optionally further comprises reconfiguring the system to avoid operation of the or each load handling device within the grid space.

[0126] 44. The method of embodiment 43, wherein the data comprises a plurality of average values, wherein each average value is derived from a sensor as the load handling device moves across a respective grid space.

[0127] 45. The method of any one of embodiments 27 to 44, wherein the method is carried out for a predetermined period of time.

[0128] 46. ​​The method of any one of embodiments 27 to 45, further comprising generating a map, the map indicating areas in the system having defects.

[0129] 47. A computer program comprising instructions, the instructions causing the computer to perform the method of any one of embodiments 27 to 46 when the program is executed by a computer.

[0130] 48. A data processing system comprising a processor configured to perform the method of any one of embodiments 27 to 47.

[0131] 49. A system comprising a plurality of load handling devices (30), a first set of tracks (17) extending in a first direction, and a second set of tracks (19) extending in a second direction transverse to the first direction, the load handling devices configured to move on the tracks, wherein each load handling device comprises a sensor configured to monitor movement of the load handling device; A system comprising: a data processing system as described in embodiment 48.

[0132] 50. A map, the map being generated using the method of any one of embodiments 27 to 46, the computer program of embodiment 47, or the system of embodiment 48 or 49, the map indicating areas in the system having defects.

[0133] 51. A computer-implemented method for verifying system installation and / or repair, the system comprising a load handling device (30), a first set of tracks (17) extending in a first direction, and a second set of tracks (19) extending in a second direction transverse to the first direction, the load handling device configured to move on the tracks, wherein the load handling device comprises a sensor configured to monitor movement of the load handling device, and wherein the method comprises: acquiring data generated by the sensors as the load handling device moves along the first and / or second set of tracks; determining that the data comprises normal data; and verifying system installation and / or repair in the area of ​​the first and / or second set of trucks by mapping normality data to location data of the load handling device.

[0134] 52. The method of embodiment 51, wherein the construction and / or repair is verified if the number of times that normal data maps with location data corresponding to the region meets a first threshold.

[0135] 53. The method of embodiment 51 or 52, wherein the sensor detects vibrations of the load handling device as it moves along the first and / or second set of tracks.

[0136] 54. The method of embodiments 51 to 53, wherein the sensor comprises an accelerometer, an inertial measurement unit, a gyroscope, an encoder, or a torque sensor coupled to at least one wheel or axle of the load handling device.

[0137] 55. A method as described in embodiments 51 to 54, wherein determining that the data is normal comprises detecting that the data deviates from the mode, median, or mean by less than a predetermined value, or is less than a reference value.

[0138] 56. The method of embodiments 51 to 55, wherein when determining that the data comprises normal data, data generated by the sensor during acceleration and deceleration of the load handling device is excluded, such that the load handling device is moving at a constant speed.

[0139] 57. The method of embodiments 51 to 56, wherein the sensor generates data only in an axis transverse to the direction of movement of the load handling device.

[0140] 58. A method according to any one of embodiments 51 to 57, wherein the sensor generates data in a first axis corresponding to a first direction, a second axis corresponding to a second direction, and a third axis transverse to the first and second directions.

[0141] 59. The method of any one of embodiments 51 to 58, wherein the first threshold is a number or a percentage value, and optionally the percentage value comprises a ratio of the number of times that normal data maps with location data corresponding to the region to the number of times that location data corresponds to the region.

[0142] 60. The method of any one of embodiments 51 to 59, wherein the construction and / or repair is verified if the number of times the location data corresponds to the region meets a second threshold.

[0143] 61. The method of embodiments 51 to 60, wherein the system comprises a plurality of cargo handling devices, and wherein construction and / or repair is verified if, for the plurality of cargo handling devices, the number of times normal data maps with location data corresponding to the area meets a third threshold.

[0144] 62. The method of embodiment 61, wherein for a plurality of cargo handling devices, construction and / or repair is verified if the number of times the location data corresponds to the area meets a fourth threshold.

[0145] 63. The system comprises a plurality of load handling devices, wherein the method comprises: determining a percentage of unique load handling devices whose data has anomalous data in the region to define a first value; 61. The method of embodiment 51 to 60, further comprising verifying the construction and / or repair if the first value exceeds a fifth threshold.

[0146] 64. The system comprises a plurality of load handling devices, wherein the method comprises: determining a total number of unique load handling devices having location data corresponding to the region to define a second value; 61. A method as described in embodiments 51 to 60, further comprising verifying that there is a defect in the region if the second value exceeds a sixth threshold.

[0147] 65. The method of any one of embodiments 60 to 64, wherein the first, second, third, fourth, fifth, and sixth thresholds are met, respectively, or any combination of the first, second, third, fourth, fifth, and sixth thresholds is met, to verify that the defect is within the region.

[0148] 66. Verifying construction and / or repairs an absence of debris on the first and / or second set of tracks; alignment along and / or between the first and / or second sets of tracks; an undamaged section on the first and / or second set of tracks; 66. The method of embodiment 51 to 65, comprising determining at least one of: undamaged intersections along and / or between the first and / or second sets of tracks.

[0149] 67. The method of any one of embodiments 51 to 66, wherein the first and second sets of tracks are in a substantially horizontal plane and form a grid with a plurality of grid spaces, forming a plurality of vertical storage locations below the grid for containers stacked between and guided by the uprights vertically through the plurality of grid spaces, and optionally the or each load handling device is configured to lift containers from and / or lower containers into the vertical storage locations below the grid.

[0150] 68. The method of embodiment 67, wherein the area comprises a grid space, and the method optionally further comprises reconfiguring the system to avoid operating the or each load handling device on the grid space unless construction and / or repair has been verified.

[0151] 69. The method of embodiment 68, wherein the data includes a plurality of average values, wherein each average value is derived from a sensor as the load handling device moves across a respective grid space.

[0152] 70. The method of any one of embodiments 51 to 69, wherein the method is carried out for a predetermined period of time.

[0153] 71. The method of any one of embodiments 51 to 70, further comprising generating a map, wherein the map indicates areas within the system where construction and / or repair has been verified.

[0154] 72. A computer program comprising instructions, which when the program is executed by a computer, cause the computer to perform the method of any one of embodiments 51 to 71.

[0155] 73. A data processing system comprising a processor configured to perform the method of any one of embodiments 51 to 71.

[0156] 74. A system comprising: a load handling device (30); a first set of tracks (17) extending in a first direction; and a second set of tracks (19) extending in a second direction transverse to the first direction, the load handling device configured to move on the tracks, wherein the load handling device comprises: a sensor configured to monitor movement of the load handling device; A system comprising: a data processing system as described in embodiment 73.

[0157] 75. A map generated using the method of embodiments 51 to 71, the computer program of embodiment 72, or the system of embodiment 73 or 74, wherein the map indicates areas within the system where construction and / or repair has been verified.

Claims

1. 1. A computer-implemented method for determining faults in a system, the system comprising a load handling device (30), a first set of tracks (17) extending in a first direction, and a second set of tracks (19) extending in a second direction transverse to the first direction, the load handling device configured to move on the tracks, the load handling device comprising a sensor configured to monitor movement of the load handling device, the method comprising: acquiring data generated by the sensors as the load handling device moves along the first and / or second set of tracks; determining that the data comprises anomalous data; and identifying defects in regions of the first and / or second set of trucks by mapping the anomaly data to position data of the load handling device, wherein the defects are identified if the number of times the anomaly data maps to position data corresponding to the region meets a first threshold.

2. The method of claim 1 , wherein the sensor detects vibrations of the load handling device as it moves along the first and / or second set of tracks.

3. The method of claim 1 or 2, wherein the sensor comprises an accelerometer, an inertial measurement unit, a gyroscope, an encoder, or a torque sensor coupled to at least one wheel or axle of the load handling device.

4. Determining that data is anomalous may be performed by determining whether the data is: deviates from the mode, median, or mean by a predetermined value, or 4. The method of claim 1, further comprising detecting that a reference value is exceeded.

5. 5. The method of claim 1, wherein data generated by the sensor during acceleration and deceleration of the load handling device is excluded when the data is determined to comprise anomalous data, such that the load handling device moves at a constant speed.

6. 6. A method according to any one of claims 1 to 5, wherein the sensors generate data only in axes transverse to the direction of movement of the load handling device.

7. 7. The method of claim 1, wherein the sensor generates data in a first axis corresponding to the first direction, a second axis corresponding to the second direction, and a third axis transverse to the first and second directions.

8. 8. The method of claim 1, wherein the first threshold is a number or a percentage value, and optionally the percentage value comprises a ratio of the number of times that anomalous data maps with location data corresponding to the region to the number of times that location data corresponds to the region.

9. The method of claim 1 , wherein the defect is verified if the number of times that the location data corresponds to the region meets a second threshold.

10. 10. The method of claim 1, wherein the system comprises a plurality of cargo handling devices, and wherein the defect is verified if, for the plurality of cargo handling devices, the number of anomaly data maps with location data corresponding to the area meets a third threshold.

11. The method of claim 10 , wherein the fault is verified if, for the plurality of load handling devices, the number of times that location data corresponds to the region meets a fourth threshold.

12. The system comprises a plurality of load handling devices, wherein the method comprises: determining a percentage of unique load handling devices for which the data comprises anomalous data in the region to define a first value; The method of claim 1 , further comprising verifying that a defect is in the region if the first value exceeds a fifth threshold.

13. The system comprises a plurality of load handling devices, wherein the method comprises: determining a total number of unique load handling devices having location data corresponding to said region to define a second value; 10. The method of claim 1, further comprising verifying that there is a defect in the region if the second value exceeds a sixth threshold.

14. 12. The method of claim 9, wherein the second, third, fourth, fifth, and sixth thresholds are respectively met to verify that a defect is within the region.

15. The method of claim 1 , further comprising determining the type of defect by detecting a corresponding unique pattern in the data.

16. The type of defect is: debris on the first and / or second set of tracks; misalignment along and / or between the first and / or second sets of tracks; damaged sections on the first and / or second set of tracks; and damaged intersections along and / or between the first and / or second sets of tracks.

17. 17. A method according to any one of claims 1 to 16, wherein the first and second sets of tracks lie substantially in a horizontal plane and form a grid comprising a plurality of grid spaces, forming a plurality of vertical storage locations below the grid for containers stacked between and guided by uprights vertically through the plurality of grid spaces, and optionally the or each load handling device is configured to lift containers from and / or lower containers into the vertical storage locations below the grid.

18. 20. The method of claim 17, wherein the region comprises a grid space, and wherein the method optionally further comprises reconfiguring the system to avoid operating the or each load handling device within the grid space.

19. 20. The method of claim 18, wherein the data comprises a plurality of average values, each average value being derived from the sensor as the load handling device moves across a respective grid space.

20. 20. The method of any one of claims 1 to 19, wherein the method is carried out for a predetermined period of time.

21. 21. The method of claim 1, further comprising generating a map, wherein the map indicates areas in the system that have defects.

22. A computer program comprising instructions which, when executed by a computer, cause the computer to carry out the method of any one of claims 1 to 21.

23. 22. A data processing system comprising a processor configured to perform the method of any one of claims 1 to 21.

24. 1. A system comprising: a load handling device (30), a first set of tracks (17) extending in a first direction, and a second set of tracks (19) extending in a second direction transverse to the first direction, the load handling device configured to move on the tracks, wherein the load handling device comprises: a sensor configured to monitor movement of the load handling device; 24. A system comprising: a data processing system according to claim 23.

25. 25. A map, the map being generated using the method of claims 1 to 21, the computer program of claim 22, or the system of claim 23 or 24, the map indicating areas in the system having defects.

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