System, apparatus and method for compensating for field installation error of TARS device

Abstract

A system, apparatus, and method for compensating for field installation errors of a TARS device are disclosed. An exemplary system includes: at least one sensor configured to detect attitude data; and one or more processors having a memory and operably coupled with the at least one sensor. The one or more processors are configured to iteratively adjust an offset error present in the attitude data until the attitude data is within a linear region. The attitude data is received from the at least one sensor each iteration, and a plurality of calibration coefficients are determined based at least on the received attitude data using a piecewise linear equation. The plurality of calibration coefficients are applied to the attitude data. An offset error present in the attitude data is then adjusted based at least on the application of the plurality of calibration coefficients until the attitude data is within the linear region of the piecewise linear equation.

Description

Technical Field

[0001] Example embodiments of the present disclosure relate generally to sensors and, more particularly, to systems, apparatus, and methods for compensating for field installation errors of a Transportation Attitude Reference System (TARS) device. Background Art

[0002] The Transport Attitude Reference System (TARS) is a technology used in the field of transportation and vehicle systems to determine and monitor the attitude and / or orientation of a vehicle. For example, TARS devices provide key data about the pitch, roll, and yaw angles of a vehicle, which are necessary for various applications including navigation, stability control, and safety systems in aircraft, automobiles, marine vessels, and other modes of transportation. Typically, TARS devices include sensors such as accelerometers and gyroscopes to accurately measure and report the pitch, roll, and yaw angles of the vehicle. In the field of transportation and vehicle technology, integrating calibrated TARS devices into vehicles is a fundamental step to improve safety and accuracy. TARS devices are factory calibrated. However, when the user mounts the TARS device on the vehicle, an offset error is introduced into the TARS device, which is an installation error relative to the X-axis, Y-axis, and Z-axis. Recurring challenges arise due to the installation process, especially offset errors that may cause the TARS device to not transmit accurate data.

[0003] The inventors have identified many areas of improvement over the prior art and methods, which are the subject of the embodiments described herein. Through effort, ingenuity and innovation, many of these deficiencies, challenges and problems have been addressed by developing solutions, including in the embodiments of the present disclosure, some examples of which are described in detail herein. Summary of the invention

[0004] An overview of some example embodiments is given below in order to provide a basic understanding of some aspects of the present disclosure. This overview is not an exhaustive review and is neither intended to identify key elements or important elements nor to describe the scope of such elements. It should also be understood that the scope of the present disclosure encompasses many possible embodiments in addition to those summarized herein, some of which will be further described in the specific embodiments given later.

[0005] In an example embodiment, a system is disclosed. The system includes at least one sensor configured to detect gesture data. In addition, one or more processors with a memory are operably coupled to the at least one sensor. The one or more processors are configured to iteratively adjust the offset error present in the gesture data until the gesture data is within a linear region. Each iteration receives the gesture data via the at least one sensor, wherein the gesture data includes an offset error, and determines a plurality of calibration coefficients based at least on the received gesture data. The plurality of calibration coefficients are determined using a piecewise linear equation. In addition, in each iteration, the determined plurality of calibration coefficients are applied to the received gesture data. Then, in each iteration, the offset error present in the gesture data is adjusted based at least on the application of the plurality of calibration coefficients until the gesture data is within the linear region.

[0006] In some embodiments, the posture data includes at least one of an angular velocity, an acceleration, and an inclination of the vehicle. In addition, the posture data is configured to calculate a roll / pitch / yaw (RPY) value to adjust the offset error in the posture data. In some embodiments, the one or more processors are further configured to perform one or more functions based at least on one or more commands received from a user. In addition, the one or more functions include a reset all function, a set function, and an add function. In addition, the reset all function corresponds to clearing the determined multiple calibration coefficients to 0. In addition, the set function corresponds to replacing the RPY value with a PGN RPY value. In addition, the add function corresponds to updating the RPY value using multiple calibration coefficients determined according to the detected posture data.

[0007] In some embodiments, one or more processors are configured to receive a command from a user to switch from a normal mode to a compensation mode to adjust the offset error. In addition, the normal mode corresponds to detecting the gesture data via the at least one sensor, and the compensation mode corresponds to adjusting the offset error present in the gesture data.

[0008] In some embodiments, the at least one sensor comprises a 3-axis gyroscope or a 3-axis accelerometer. In some embodiments, the plurality of calibration coefficients comprises a linear equation from a piecewise linear equation The derived coefficients a0, a1, a2. In some embodiments, the system corresponds to at least one of the Transportation Attitude Reference System (TARS) devices mounted above the vehicle.

[0009] In another example embodiment, a method is disclosed. The method iteratively adjusts the offset error present in the posture data until the posture data is within the linear region. Each iteration includes the step of receiving posture data via at least one sensor, wherein the posture data includes the offset error. In addition, a plurality of calibration coefficients are determined based at least on the received posture data using a piecewise linear equation. In addition, the determined plurality of calibration coefficients are applied to the received posture data. Then, the offset error present in the posture data is adjusted based at least on the application of the plurality of calibration coefficients until the posture data is within the linear region.

[0010] The above-mentioned summary of the invention is provided only for the purpose of summarizing some exemplary embodiments, so as to provide a basic understanding of some aspects of the present disclosure. Therefore, it should be understood that the above-mentioned embodiments are only examples and should not be construed as narrowing the scope or essence of the present disclosure in any way. It should be understood that in addition to those summarized here, the scope of the present disclosure also covers many possible embodiments, some of which will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Having thus generally described certain example embodiments of the present disclosure, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and in which:

[0012] Figure 1 A Transportation Attitude Reference System (TARS) device mounted on a vehicle is shown according to an example embodiment of the present disclosure;

[0013] Figure 2 shows a perspective view of an exemplary TARS device according to an example embodiment of the present disclosure;

[0014] Figure 3 shows a block diagram of an exemplary TARS device according to an example embodiment of the present disclosure;

[0015] Figure 4 shows an exemplary table of a TARE PGN according to an example embodiment of the present disclosure;

[0016] Figure 5 shows an exemplary table of a TARE command according to an example embodiment of the present disclosure;

[0017] Figure 6 A flow chart showing an exemplary method for compensating for field installation errors of a TARS device according to an exemplary embodiment of the present disclosure;

[0018] Figure 7Another flow chart showing an exemplary method for compensating for field installation errors of a TARS device according to an exemplary embodiment of the present disclosure;

[0019] Figure 8 shows an exemplary graph associated with results of piecewise linear correction of a TARS device according to an example embodiment of the present disclosure; and

[0020] Fig. 9 Exemplary simulation results of a TARS device according to an example embodiment of the present disclosure are shown. DETAILED DESCRIPTION

[0021] Some embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments of the present disclosure are shown. Indeed, the various embodiments may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements.

[0022] The components shown in the drawings represent components that may or may not be present in various embodiments of the present disclosure described herein, such that an embodiment may include fewer or more components than those shown in the drawings without departing from the scope of the present disclosure. Some components may be omitted from one or more drawings, or shown in phantom to make the following components visible.

[0023] As used herein, the term "comprising" means including but not limited to, and should be interpreted in the manner in which it is typically used in a patent context. The use of broader terms such as "comprising," "including," and "having" should be understood to provide support for narrower terms such as "consisting of," "consisting essentially of," and "composed essentially of."

[0024] The phrases "in various embodiments," "in one embodiment," "according to one embodiment," "in some embodiments," and the like generally mean that the particular feature, structure, or characteristic following the phrase may be included in at least one embodiment of the present disclosure, and may be included in more than one embodiment of the present disclosure (importantly, such phrases are not necessarily referring to the same embodiment).

[0025] As used herein, the word “example” or “exemplary” means “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other implementations.

[0026] If the specification states that a component or feature "may", "can", "might", "should", "will", "preferably", "likely", "typically", "optionally", "for example", "usually" or "might" (or other such language) be included or have a property, the specific component or feature need not be included or have that property. Such components or features may be optionally included in some embodiments or may be excluded.

[0027] The present disclosure provides various embodiments of systems, devices, and methods for compensating for field installation errors of a transport attitude reference system (TARS) device. The embodiments can correct offset errors in the attitude data of a TARS device after the TARS device is installed on a vehicle. The embodiments can calculate the attitude data and determine the calibration coefficients using piecewise linear equations about the attitude data. Then, the embodiments can apply corrections to the calculated attitude data. The embodiments can repeatedly calculate the attitude data and determine the calibration coefficients until the final output is within a linear region, which can be one or more thresholds associated with specifications associated with an application using the TARS device 100. In addition, the embodiments can be integrated into a TARS device installed on a vehicle to provide a robust and reliable system for improving vehicle stability control.

[0028] Figure 1 A Transportation Attitude Reference System (TARS) device 100 is shown mounted on a vehicle 102 according to an example embodiment of the present disclosure. Figure 2 A perspective view of an exemplary TARS device 100 is shown according to an exemplary embodiment of the present disclosure. Figure 2 right Figure 1 Give a description.

[0029] In some embodiments, the TARS device 100 may be a packaged sensor array for reporting posture data of the vehicle 102. The posture data may include at least the angular velocity, acceleration, and inclination of the vehicle 102. In addition, the TARS device 100 may be mounted on, in, or above the vehicle 102. In some embodiments, the vehicle 102 may be, for example, a forest vehicle, a construction vehicle, a pile driver, a skid loader, a mobile crane, an intelligent leveling traction device, a wheel loader, an excavator vehicle, or any other vehicle known in the art. In one example embodiment, the TARS device 100 may be mounted above the left front tire of a construction vehicle. In another example embodiment, the TARS device 100 may be mounted on the hood of a forest vehicle. Without departing from the scope of the present disclosure, the TARS device 100 may be mounted above any fixed component of the vehicle 102, for example.

[0030] In addition, the TARS device 100 can be configured to determine the pitch, roll, and yaw angles of the vehicle. When the user mounts the TARS device 100 on the vehicle 102, an offset error may be introduced into the TARS device 100. The offset error may be an installation error relative to the X-axis, Y-axis, and Z-axis. In some embodiments, the TARS device 100 can be calibrated after being mounted on the vehicle 102 to compensate for and / or reduce the offset error in the posture data of the TARS device 100, which is described in more detail herein.

[0031] Figure 3 A block diagram of an exemplary TARS device 100 is shown according to an example embodiment of the present disclosure. Figure 4 An exemplary table 400 of TARE PGNs is shown according to an example embodiment of the present disclosure. Figure 5 FIG. 1 shows an exemplary table of a TARE command according to an example embodiment of the present disclosure. Figures 4 to 5 right Figure 3 Give a description.

[0032] The TARS device 100 may include at least one sensor 302, one or more processors 304, a memory 306, input / output circuitry 308, and / or communication circuitry 310. In an example embodiment, a system or device system may exist external to the TARS device 100 and be communicatively coupled to the TARS device 100 to perform one or more operations described herein to compensate for field installation errors of the TARS device 100.

[0033] At least one sensor 302 may be configured to detect posture data of the vehicle 102. For example, and in some embodiments, at least one sensor 302 may be mounted above the vehicle 102. At least one sensor 302 may include, for example, a 3-axis gyroscope and / or a 3-axis accelerometer. In some embodiments, the posture data may include the angular velocity, acceleration, and inclination of the vehicle.

[0034] The angular velocity of the vehicle 102 may represent how the angular position or orientation of the vehicle 102 changes over time. The angular position or orientation may include the speed at which the vehicle 102 rotates around the axis of rotation. The angular position or orientation may also include the speed at which the axis of rotation itself changes direction. In an example embodiment, a 3-axis gyroscope may be used to detect the angular velocity of the vehicle 102.

[0035] In some embodiments, the acceleration of the vehicle 102 may represent the rate at which the vehicle 102 may increase in speed. In one example embodiment, a 3-axis accelerometer may be used to detect the acceleration of the vehicle 102. In some embodiments, the inclination of the vehicle 102 may represent the angle between a horizontal plane and a line connecting the center of gravity of the vehicle and the point where the tires of the vehicle 102 contact the ground. In one example embodiment, a combination of a 3-axis gyroscope sensor and a 3-axis accelerometer may be used to detect the inclination of the vehicle 102.

[0036] In addition, the detected attitude data may include information about the orientation of the vehicle 102 in three-dimensional space based on the angular velocity, acceleration, and inclination of the vehicle 102. The orientation of the vehicle may also include measurements related to the roll, pitch, and yaw values ​​(RPY) of the vehicle. In addition, roll may include the rotation of the vehicle 102 around the longitudinal axis of the vehicle 102. The longitudinal axis may include an imaginary line extending from the front of the vehicle 102 to the rear of the vehicle 102. In addition, roll may include the tilting of the vehicle 102 from one side to the other. In addition, pitch may include the rotation of the vehicle 102 around the transverse axis of the vehicle 102, which extends from one side to the other. Pitch may include the movement of the vehicle 102 tilting forward and backward. In addition, yaw may include the rotation of the vehicle 102 around the vertical axis of the vehicle 102 (usually considered to be the "upward" axis). In some embodiments, yaw may involve a left or right turn of the vehicle 102 without tilting.

[0037] In some embodiments, at least one sensor 302 can detect the posture data of the vehicle 102 in real time. In some embodiments, the posture data can be calculated at a frequency of 100 Hz (Hz). The posture data can be detected at periodic intervals. For example, the periodic interval can be 1 minute. In some embodiments, a low-pass filter can be used to remove noise from the detected posture data. For example, a low-pass filter with a 10 Hz and a 3 dB cutoff frequency can be used.

[0038] In some embodiments, the TARS device 100 may include one or more processors 304. The one or more processors 304 may be operably coupled to at least one sensor 302. In various examples, the one or more processors 304 may include suitable logic components, circuits, and / or interfaces that are operable to execute one or more instructions stored in the memory 306 in order to perform predetermined operations. In some embodiments, the one or more processors 304 may be configured to decode and execute any instructions received from one or more other electronic devices or servers. The one or more processors 304 may be configured to execute one or more computer-readable program instructions, such as program instructions that perform any of the functions described in this specification. In addition, the one or more processors 304 may be implemented using one or more processor technologies known in the art. Examples of processors include, but are not limited to, one or more general-purpose processors (e.g., or Advanced Micro (AMD) microprocessors) and / or one or more special purpose processors (e.g., digital signal processors or System on Chip (SOC) Field Programmable Gate Array (FPGA) Processor).

[0039] The memory 306 can store instruction sets and data. In some embodiments, the memory 306 may include one or more instructions that can be executed by one or more processors 304 to perform specific operations. It is obvious to those of ordinary skill in the art that the one or more instructions stored in the memory 306 enable the hardware of the system 100 to perform predetermined operations. Some well-known memory implementations include, but are not limited to, fixed (hard) drives, tapes, floppy disks, optical disks, compact disk read-only memories (CD-ROMs) and magneto-optical disks, semiconductor memories such as ROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other types of media / machine-readable media suitable for storing electronic instructions.

[0040] The TARS device 100 may include input / output circuitry 308, which in turn may communicate with the processor 304 to receive input from a user and / or provide output to a user. In some embodiments, the input / output circuitry 308 may also include additional functionality including a keyboard, mouse, joystick, touch screen, touch area, soft keys, microphone, speaker, or other input / output mechanisms.

[0041] The TARS device 100 may include a communication circuit 310 to communicate with one or more other devices and / or systems. In some embodiments, the communication circuit 310 may be a device or circuit embodied in hardware or a combination of hardware and software, which is configured to receive data from and / or transmit data to a network and / or any other device, circuit or module that communicates with the TARS device 100. In some embodiments, the communication circuit 310 may include a plug having multiple terminals that can be connected to another device, apparatus and / or system. In some embodiments, the communication circuit 310 may include, for example, a network interface for implementing communication with a wired or wireless communication network. For example, the communication circuit 310 may include one or more network interface cards, antennas, buses, switches, routers, modems, and supporting hardware and / or software, or any other device suitable for implementing communication via a network. For example, one or more processors 304 may communicate with a remote device (not shown) via a network interface (not shown) of the communication circuit 310. The network interface may facilitate a communication link between one or more processors 304 and a remote device. It should be noted that the network interface may further facilitate a communication link between other components of the TARS device 100. In addition, the network interface can be a wireless network and / or a wired network. The network interface can be implemented using one or more communication technologies. The one or more communication technologies can be radio waves, Wi-Fi, Bluetooth, ZigBee, Z-waves, visible light communication (VLC), World Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE), wireless local area network (WLAN), infrared (IR) communication, public switched telephone network (PSTN), radio waves, and other communication technologies known in the art.

[0042] In some embodiments, the remote device may allow a user to send one or more input commands to the one or more processors 304 for adjusting offset errors present in the detected gesture data. The one or more input commands may include or be associated with, but are not limited to, performing one or more functions, calculating gesture data, determining multiple calibration coefficients, providing one or more values ​​of desired gesture data, resetting multiple calibration coefficients, or executing a "script" of commands at different time intervals between detecting gesture data or applying multiple calibration coefficients.

[0043] In some embodiments, the remote device may include a smart phone, a tablet computer, a laptop, a personal computer (PC), a smart watch, or any other computing device known in the art. In one embodiment, a user may use a smart phone or a tablet computer as a remote device. In another embodiment, a dedicated Android or IOS application may be developed to interact with one or more processors 304. In addition, a dedicated application may be used to discover, configure, and / or command one or more processors 304.

[0044] As described herein, one or more processors 304 may be operably coupled to at least one sensor 302. In addition, one or more processors 304 may be configured to receive attitude data via at least one sensor. As discussed, the detected attitude data may include information about the orientation of the vehicle in three-dimensional space. The orientation of the vehicle may also include measurements related to the roll, pitch, and yaw values ​​(RPY) of the vehicle. One or more processors 304 may be configured to average the received attitude data. In addition, one or more processors 304 may average the received attitude data at periodic intervals. One or more processors 304 may average another received attitude data after a periodic interval. For example, one or more processors 304 may average another received attitude data after 1 minute.

[0045] In some embodiments, one or more processors 304 may be configured to determine a plurality of calibration coefficients based at least on the received posture data. A plurality of calibration coefficients may be determined based at least on the average value of the received posture data. In addition, a plurality of calibration coefficients may be determined using piecewise linear equations. A piecewise linear equation may be a mathematical function consisting of a plurality of linear segments, each of which is defined within a specific interval. In an example embodiment, a piecewise linear equation may include: Furthermore, the plurality of calibration coefficients may include coefficients a0, a1, a2 of the piecewise linear equation. The coefficients a0, a1, a2 may be determined based on an average value of the detected gesture data.

[0046] In some embodiments, in a piecewise linear equation, a piecewise function can represent a series of multiple linear segments. In addition, each linear segment can be defined on a specific data set. In order to estimate the value in a specific data set, a linear interpolation method can be applied to the data set. The linear interpolation method for the data set is composed of multiple segments of linear interpolation. In addition, the linear interpolation method for the data set can include a cascade of linear interpolations between each pair of data sets. Therefore, a continuous curve can be obtained. Therefore, the linear interpolation method can estimate the value in each linear interpolation to represent a complex relationship. In addition, one or more processors 304 can be configured to apply the determined multiple calibration coefficients to the average value of the received attitude data. In some embodiments, one or more processors 304 can apply the determined multiple calibration coefficients to the roll, pitch and yaw (RPY) values ​​of the vehicle.

[0047] In some embodiments, one or more processors 304 may be configured to adjust the offset error present in the detected posture data based at least on the application of the multiple calibration coefficients. One or more processors 304 may adjust the offset error until the posture data is within the linear region of the piecewise linear equation. In some embodiments, at least one sensor 302 may repeatedly detect the posture data, and one or more processors 304 may repeatedly determine multiple calibration coefficients until the posture data is within the linear region of the piecewise linear equation.

[0048] In some embodiments, the offset error may include a constant error present in all measurements of at least one sensor 302. The offset error may be an "offset" in the output of at least one sensor 302. In one example, if at least one sensor 302 should output 24°, but outputs 26°, the at least one sensor 302 may have an offset error of 2°. In addition, at least one sensor 302 may repeatedly detect posture data, and one or more processors 304 may repeatedly determine multiple calibration coefficients until the posture data is within the linear region of the piecewise linear equation. Therefore, the posture data may be closer to the linear region of the piecewise linear equation, and the offset error may decrease with each repetition. In addition, the final multiple calibration coefficients may be stored in the memory 306.

[0049] As described above, the remote device may allow the user to send one or more input commands to the one or more processors 304 to adjust the offset error present in the detected posture data. In addition, the one or more processors 304 may receive input commands to perform combined Figure 4 and Figure 5 One or more of the functions described.

[0050] refer to Figure 4 , and an exemplary table 400 for a TARE PGN is shown according to an example embodiment or the present disclosure. TARE may be an offset error. It will be apparent to those skilled in the art that a TARE PGN is a peer-to-peer controller area network (CAN) message according to the SAE J1939 standard. A TARE PGN may receive information such as Figure 5 400 may include TARE PGN values ​​for use in the TARE device 100. Table 400 may include a target address (PS). The PS may indicate the address of the system. In addition, table 400 may include a requesting party address (SA). The SA may indicate the address of the user / remote device. In addition, the CAN message for each PS and SA may be provided with the number of bits used in each PS and SA.

[0051] Additionally, CAN messages for roll, pitch, yaw (RPY) may be provided to calculate RPY values. In some embodiments, uint16=B may be used H *2 8 +B L To calculate the RPY value. In addition, the calculated uint16 can be used to calculate the angle in degrees. Angle (degrees) = (uint16-2 15 ) / 100 to calculate the angle. The angle may indicate the actual output obtained after applying the plurality of calibration coefficients to the detected posture data.

[0052] refer to Figure 5 , and an exemplary table 500 of TARE commands is shown according to an example embodiment or the present disclosure. Table 500 may include a TARE command for each roll, pitch, yaw, and all roll, pitch, yaw. Each type of TARE command may include 2 bits. In addition, the 2 bits of "all" may include bit ID positions 7 and 6. In addition, the 2 bits of "roll" may include bit ID positions 5 and 6. The 2 bits of "pitch" may include bit ID positions 3 and 2. In addition, the 2 bits of "yaw" may include bit ID positions 1 and 0.

[0053] In addition, in table 500, the CAN message may include one or more functions. In addition, the one or more processors 304 may receive a TARE command to perform the one or more functions. The one or more functions may also include 00B, 01B, 10B, and 11B. A 00B CAN message may include a "no operation" function. A 01B CAN message may include a "reset all" or "set all" function. A 10B CAN message may include an "add" function. A 11B CAN message may include a "reserve" function. The reset all function may correspond to clearing the determined multiple calibration coefficients to 0. The set function may correspond to replacing the RPY value with a PGN RPY value, such as described herein. The add function may correspond to updating the RPY value using multiple calibration coefficients determined based on the detected attitude data.

[0054] In various embodiments, one or more processors are configured to receive a command from a user for switching from a normal mode to a compensation mode to adjust the offset error. The normal mode may correspond to detecting posture data via at least one sensor. The compensation mode may correspond to adjusting the offset error present in the posture data. The remote device may include a display that displays whether the TARS device 100 is in normal mode or compensation mode. In addition, the TARS device 100 may correspond to at least one of the transport attitude reference system (TARS) devices 100 mounted above the vehicle 102. Therefore, the offset error can be adjusted to correctly mount the TARS device 100 above the vehicle 102.

[0055] The detection of the gesture data and the performance of further processing steps may be performed by one or more processors of the TARS device 100 using the at least one sensor 302 without departing from the scope of the present disclosure.

[0056] The above-described components of the TARS apparatus 100 are provided for exemplary purposes only.

[0057] Figure 6 A flow chart of an exemplary method 600 for compensating for field installation errors of a TARS device 100 according to an example embodiment of the present disclosure is shown. The method 600 iteratively adjusts the offset errors present in the posture data until the posture data is within the linear region, wherein each iteration includes:

[0058] First, at step 602, attitude data is received via at least one sensor 302. The attitude data includes an offset error. In some embodiments, at least one sensor 302 may be mounted above the vehicle to detect the attitude data. In addition, at least one sensor 302 may detect the attitude data in real time. In some embodiments, the attitude data may include the angular velocity, acceleration, and inclination of the vehicle. In addition, a roll / pitch / yaw (RPY) value is detected from the attitude data. In addition, at least one sensor 302 may include at least a 3-axis gyroscope and a 3-axis accelerometer. In addition, an average value of the detected attitude data may be taken.

[0059] For example, the vehicle 102 is equipped with at least one sensor 302, namely a 3-axis gyroscope and a 3-axis accelerometer working together. The 3-axis gyroscope reports an angular velocity of 30 degrees per second, and the 3-axis accelerometer reports an acceleration of 2 meters per second squared and a tilt of 5 degrees.

[0060] Next, at step 604, a plurality of calibration coefficients are determined based at least on the received posture data using the piecewise linear equation. In some embodiments, the plurality of calibration coefficients may include: The obtained coefficients a0, a1, a2. In addition, the plurality of calibration coefficients may be applied to the average value of the detected posture data.

[0061] For example, using the one or more processors 104, the system 100 determines a plurality of calibration coefficients as 3, 2, 2 based on an angular velocity of 30 degrees per second, an acceleration of 2 meters per second squared, and an inclination of 5 degrees.

[0062] Next, at step 606, the determined plurality of calibration coefficients are applied to the received posture data. In addition, the plurality of calibration coefficients may be applied to an average of the detected posture data. For example, the one or more processors 104 apply the determined plurality of calibration coefficients 3, 2, 2 to the received posture data. In addition, the one or more processors 104 adjust the angular velocity from 30 degrees per second to 28 degrees per second, the acceleration from 2.0 meters per second squared to 1.5 meters per second squared, and the inclination from 5.0 degrees to 4.3 degrees.

[0063] Next, at step 608, the bias error present in the posture data is adjusted based on at least the application of the plurality of calibration coefficients until the posture data is within the linear region. In some embodiments, the posture data may be repeatedly detected and a plurality of calibration points may be determined until the posture data is within the linear region. For example, the one or more processors 100 adjust the bias error in the angular velocity from 2 degrees per second to 0.3 degrees per second, thereby obtaining highly accurate posture data.

[0064] In some embodiments, method 600 may also include performing one or more functions via one or more processors based at least on one or more commands received from a user, wherein the one or more functions include resetting all functions, setting functions, and adding functions. In addition, resetting all functions may correspond to clearing determined multiple calibration coefficients to 0. In addition, setting functions may correspond to replacing detected gesture data with the average value of detected gesture data at periodic intervals. In addition, adding functions may correspond to updating detected gesture data using the difference between average gesture data and gesture data detected after periodic intervals.

[0065] In some embodiments, method 600 may further include receiving a command from a user via one or more processors to switch from a normal mode to a compensation mode to adjust the offset error. In addition, the normal mode may correspond to detecting gesture data via at least one sensor. And, the compensation mode may correspond to adjusting the offset error present in the gesture data.

[0066] Figure 7 FIG. 7 is a flow chart showing an exemplary method 700 for compensating for field installation errors of a TARS device 100 according to an exemplary embodiment of the present disclosure. Figure 6 right Figure 7 Give a description.

[0067] First, at step 702, the TARS device 100 is mounted at a desired location on the vehicle 102. In some embodiments, the vehicle 102 may be, for example, a forest vehicle, a construction vehicle, a pile driver, a skid steer loader, a mobile crane, a smart leveling tractor, a wheel loader, an excavator vehicle, or any other vehicle known in the art. In one example, the TARS device 100 may be mounted above the left front tire of the construction vehicle.

[0068] Next, at step 704, the vehicle 102 moves relative to the flat platform (not shown) as seen by the TARS device 100. In addition, the vehicle 102 may move relative to the flat platform so that the TARS device 100 detects posture data. The vehicle may move in a forward direction or a rearward direction.

[0069] Next, at step 706, a compensation command is triggered. In some embodiments, a compensation command or command may be received from a user to switch the TARS device 100 from a normal mode to a compensation mode to adjust the offset error. Next, at step 708, the compensation progress status is indicated. When the TARS device 100 may be switched to a compensation mode, the progress status including the compensation may be visible to the user on a display of the remote device.

[0070] Next, at step 710, the posture data (tilt) is detected at a frequency of 200 Hz using a 10 Hz low-pass filter, and the average value of the detected posture data (tilt) is calculated. In addition, the posture data can be detected by at least one sensor 302, as explained in step 602. In some embodiments, the posture data can include the angular velocity, acceleration, and tilt of the vehicle 102. In addition, the detected posture data can be averaged.

[0071] Then, at step 712, multiple calibration coefficients are determined by using piecewise linear equations. In addition, multiple calibration coefficients can be determined based on the average value of the detected posture data, as explained in step 604. Then, at step 714, another posture data (inclination) is detected at a frequency of 200 Hz using a 10 Hz low-pass filter, and the average value of the detected posture data (inclination) data is calculated. In addition, more posture data can be detected, and another posture data detected can be averaged. In addition, the determined multiple calibration coefficients can be applied to the detected posture data, as explained in step 602.

[0072] Next, at step 716, it is determined whether the detected gesture data is within the linear region of the piecewise linear equation. In some embodiments, gesture data may be repeatedly detected and multiple calibration points determined until the gesture data is within the linear region.

[0073] Next, at step 718, multiple calibration coefficients are discarded. In one case, if the posture data may not fall within the linear region, the multiple calibration coefficients can be discarded and another multiple calibration coefficients can be determined. Next, at step 720, the compensation completion status is indicated. In another case, if the posture data falls within the linear region, the compensation mode of the TARS device 100 can be completed. In addition, the completion status can be displayed on the display of the remote device. Next, at step 722, the multiple calibration coefficients are stored in the memory 302. In addition, the final multiple calibration coefficients can be stored in the memory of the TARS device 100.

[0074] It should be understood that methods 600, 700 can be implemented by one or more embodiments disclosed herein, which can be combined or modified as desired or required. In addition, the steps in methods 600, 700 can be modified, changed in order, performed differently, performed sequentially, in parallel or simultaneously, or modified in other ways as desired or required.

[0075] The above-described embodiments of the present disclosure may be performed by one or more processors 304 and methods 600 , 700 of the TARS device 100 using at least one sensor 302 without departing from the scope of the present disclosure.

[0076] Figure 8 An exemplary graph associated with results 800 of a piecewise linear correction of a TARS device 100 is shown according to an example embodiment of the present disclosure.

[0077] In some embodiments, gesture data may be repeatedly detected in one or more regions and multiple calibration coefficients may be determined. In addition, the one or more regions may include 0-60 seconds (s), 60s-120s, etc. In addition, the result 800 may involve finding the region from the one or more regions to determine the calibration coefficients that make the detected gesture data within the linear region of the piecewise linear equation.

[0078] As discussed herein, linear interpolation may be applied to estimate the values, thereby obtaining a continuous curve. Linear interpolation may be used to apply a piecewise linear correction to the detected posture data. In some embodiments, curve 802 may indicate an offset error in percent (%). In addition, curve 804 may indicate an actual inclination output in degrees (deg). In some embodiments, the actual inclination output may be an output obtained after applying multiple calibration coefficients of the detected posture data. Without applying an offset correction, the offset error on the TARS device 100 mounted on the vehicle 102 may be as large as 4%, which is a + / - 2 degree inclination error in the detected posture data.

[0079] In addition, the first level correction 806 at 0 to 60 seconds (s) may result in a 2% offset error, which is a + / -0.5 degree tilt error. In addition, the second level correction 808 at 60s to 120s may result in a 0.1% offset error, which is a + / -0.2 degree tilt error. In some embodiments, the tilt offset specification is + / -0.2 degrees. In some embodiments, the tilt offset specification may be a specification that the detected posture data may fall within the linear region of the piecewise linear equation. In addition, if the second level correction 808 may not enter the tilt offset specification, a third level correction (not shown) at 120s to 180s may be applied, and may continue until a 0% offset error is obtained.

[0080] Fig. 9 Exemplary simulation results 900 of a TARS device 100 according to an example embodiment of the present disclosure are shown.

[0081] In some embodiments, the user can send a command via a remote device to switch from a normal mode to a compensation mode to adjust the offset error. In addition, after receiving the command, the ongoing compensation status 902 can be displayed. In addition, 0 can indicate a normal mode, and 3 can indicate a compensation mode. Therefore, the command can cause the TARS device 100 to enter a compensation mode to adjust the offset error in the posture data.

[0082] In some embodiments, the attitude data may be repeatedly detected at a frequency of 200 Hz. The detected attitude data may include roll inclination 904 and pitch inclination 906. In addition, roll inclination 904 may be detected in degrees. In addition, pitch inclination 906 may be detected in degrees. The attitude data may be detected in periodic intervals. For example, the periodic interval may occur once every 1 minute.

[0083] In some embodiments, the TARS device 100 may average the roll inclination 904 and the pitch inclination 906 at periodic intervals. In addition, a low-pass filter may be used to remove noise from the average. The average may be considered as a first average sample of the compensation state 902 at 0 minutes. Similarly, another average sample may be obtained. In addition, a piecewise linear equation may be used to determine multiple calibration coefficients for the first average sample and another average sample. In addition, a correction to the determined multiple calibration coefficients may be applied. The correction may be applied according to linear interpolation. In one case, if the correction does not fall within the linear region, the determined multiple calibration coefficients may be discarded. Similarly, multiple average samples of attitude data may be obtained at periodic intervals. For example, a periodic interval may occur once every 1 minute.

[0084] The steps mentioned in the above embodiment may be repeated until the correction can fall within the linear region. And, the correction can be regarded as a final plurality of calibration coefficients and can also be stored in the memory 306. In addition, the remote device can indicate a successful compensation sequence 902. Therefore, the TARS device 100 can switch from the compensation mode back to the normal mode.

[0085] The technical personnel in the field of the present disclosure will think of many modifications and other embodiments of the present disclosure set forth herein after benefiting from the teachings presented in the foregoing description and the related drawings. Therefore, it should be understood that the present disclosure is not limited to the specific embodiments disclosed, and modifications and other embodiments are intended to be included within the scope of the appended claims. In addition, although the previous description and the related drawings describe example embodiments in the context of certain example combinations of elements and / or functions, it should be understood that different combinations of elements and / or functions can be provided by alternative embodiments without departing from the scope of the appended claims. In this regard, for example, as can be set forth in some appended claims, it is also conceivable to combine elements and / or functions that are different from those explicitly described above. Although specific terms are used herein, they are used only in a general and descriptive sense, not for restrictive purposes.

Claims

1. A system, comprising: at least one sensor configured to detect gesture data; one or more processors having a memory and operably coupled to the at least one sensor, wherein the one or more processors are configured to: Iteratively adjusting the bias error present in the pose data until the pose data is within a linear region, wherein each iteration comprises: receiving the pose data from the at least one sensor, wherein the pose data includes the offset error; determining a plurality of calibration coefficients based at least on the received posture data using a piecewise linear equation; applying the determined plurality of calibration coefficients to the received gesture data; and The offset error present in the gesture data is adjusted based at least on application of the plurality of calibration coefficients until the gesture data is within the linear region.

2. The system of claim 1 , wherein the attitude data comprises at least one of an angular velocity, an acceleration, and an inclination of the vehicle, wherein the attitude data is configured to calculate a roll / pitch / yaw (RPY) value to adjust the offset error in the attitude data.

3. The system of claim 2, wherein the one or more processors are further configured to perform one or more functions based at least on one or more commands received from a user, wherein the one or more functions include a reset all function, a set function, and an add function. 4 . The system of claim 3 , wherein the reset all function corresponds to clearing the determined plurality of calibration coefficients to zero.

5. The system of claim 3, wherein the setup function corresponds to replacing the RPY value with the PGN RPY value.

6. The system of claim 3, wherein the adding function corresponds to updating the RPY value using the plurality of calibration coefficients determined from the detected attitude data. 7 . The system of claim 1 , wherein the one or more processors are configured to receive a command from a user to switch from a normal mode to a compensation mode to adjust the offset error.

8. The system of claim 7, wherein the normal mode corresponds to detecting the gesture data via the at least one sensor, and the compensation mode corresponds to adjusting the offset error present in the gesture data.

9. The system of claim 1, wherein the system corresponds to at least one of a Transportation Attitude Reference System (TARS) device mounted above a vehicle.

10. A method comprising: Iteratively adjusting the bias error present in the pose data until the pose data is within a linear region, wherein each iteration comprises: receiving posture data from at least one sensor, wherein the posture data includes the offset error; determining a plurality of calibration coefficients based at least on the received posture data using a piecewise linear equation; applying the determined plurality of calibration coefficients to the received gesture data; and The offset error present in the gesture data is adjusted based at least on application of the plurality of calibration coefficients until the gesture data is within the linear region.

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