CN111928844B - Model system of MEMS gyroscope on AGV application - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
- G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
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Abstract
The invention discloses a model system of an MEMS gyroscope in AGV application, which comprises: the gyro data acquisition module is used for: the system comprises an AGV, a MEMS gyroscope, a sensor and a sensor, wherein the AGV is used for acquiring angular speed data of the MEMS gyroscope installed on the AGV; and the gyro data compensation module is used for: compensating the acquired gyro data, wherein the compensation method sequentially comprises temperature compensation, calibration and zero offset subtraction; the state judging module is used for: comparing the compensated gyro data with a dynamic and static state switching threshold A, and if the gyro data is larger than the dynamic and static state switching threshold A, judging that the AGV is in a moving state and judging that the AGV is in a static state; heading angle YAW calculation module: the course angle YAW in the moving state and the course angle YAW in the stationary state are calculated by a dynamic course angle YAW calculation method and a static course angle YAW calculation method, respectively. The invention enables the common MEMS gyroscope to be applied to the AGV, and the performance is equivalent to or even better than that of a high-price gyroscope, thereby greatly reducing the cost of AGV application and development.
Description
Technical Field
The invention relates to the technical field of MEMS (micro-electromechanical systems), in particular to application of a general MEMS gyroscope to an AGV (automatic guided vehicle).
Background
AGV is an abbreviation of automatic handling trolley, and at present, the AGV has become one of the important devices of modern intelligent logistics, movement and storage, and is gradually accepted and introduced.
There are various navigation modes of the indoor AGV trolley, including visual navigation, laser navigation, electromagnetic navigation and inertial navigation. The navigation modes can be used singly or in combination to finish the tasks of high measurement precision and accurate navigation. The visual and laser navigation modes have high precision but high price, and are different from 4000 to tens of thousands of yuan; traditional inertial navigation requires a high-precision gyroscope to provide accurate navigation, but the price is more than thousand yuan, and the number of gyro suppliers is small, so that the gyro suppliers are problems in price and stable supply; the navigation features of electromagnetic navigation generally need to be used together with inertial navigation.
The common MEMS gyroscope which is easy to obtain in the market is low in price, the selling price is less than hundred yuan, the choice of suppliers is large, and the supply is sufficient. However, the gyroscope has the defects of zero offset, poor zero offset repeatability, large temperature drift, poor nonlinearity and the like in performance, so that the gyroscope has angle drift and inaccurate course angle in AGV application, and an AGV trolley cannot be accurately positioned, and therefore the gyroscope is not adopted by the AGV industry.
From the above overview, today's navigation technology is very expensive in applications that provide high-precision navigation, severely impacting use and popularization.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a model system of the MEMS gyroscope on the AGV application, and the accurate positioning of the AGV trolley is realized through the general MEMS gyroscope.
In order to solve the technical problems, the invention adopts the following technical scheme: a model system for a MEMS gyroscope on an AGV application, comprising:
the gyro data acquisition module is used for: the system comprises an AGV, a MEMS gyroscope, a sensor and a sensor, wherein the AGV is used for acquiring angular speed data of the MEMS gyroscope installed on the AGV;
and the gyro data compensation module is used for: compensating the acquired gyro data, wherein the compensation method sequentially comprises temperature compensation, precision calibration and zero offset subtraction;
the state judging module is used for: comparing the compensated gyro data with a dynamic and static state switching threshold A, and if the gyro data is larger than the dynamic and static state switching threshold A, judging that the AGV is in a moving state and judging that the AGV is in a static state;
heading angle YAW calculation module: respectively calculating a course angle YAW in a motion state and a course angle YAW in a static state by adopting a dynamic course angle YAW calculation method and a static course angle YAW calculation method;
the method for calculating zero offset of the gyro during power-on is as follows:
firstly, the system is electrified to collect static 20 gyroscope data and store the static 20 gyroscope data into a cache GyroBuff;
secondly, sorting the cache GyroBuff to obtain GyroBuff1;
thirdly, taking 16 data in the middle of the cache GyroBuff1 to perform mean value processing to obtain zero BIAS BIAS of the gyroscope; carrying out full-temperature compensation at-40-85 ℃ on the gyroscope:
firstly, performing second-order least square fitting on the acquired full-temperature gyro data and temperature:
Gyro＝At·Temp 2 +Bt·Temp+Ct
wherein Gyro is Gyro data, temp is temperature data, at is a second-order coefficient, bt is a first-order coefficient, and Ct is a constant;
calculating the values of At, bt and Ct through fitting;
secondly, calculating a temperature curve value Gyro1 at the current temperature:
Gyro1＝At·Temp 2 +Bt·Temp+Ct
wherein Temp is the current temperature value;
thirdly, calculating a temperature curve value Gyro2 at 25 ℃:
Gyro2＝At·25 2 +Bt·25+Ct
fourthly, temperature compensation is carried out on the gyro data:
GroOut＝GyroRead-(Gyro1-Gro2)
wherein gyrout is a gyro value after temperature compensation, gyroRead is a read gyro value;
the gyro precision calibration method comprises the following steps:
collecting n groups of data of the gyroscope at +/-x degrees/s, wherein n is more than or equal to 7, averaging each group of data respectively, performing fitting treatment on the data by using a least square method, finally obtaining scale factors, zero offset and cross coupling parameters of each group of gyroscope data, and compensating the collected gyroscope data:
wherein GyroXOut, gyroYOut, gyroZOut is a compensated gyro value, gyroXRead, gyroYRead, gyroZRead is a read gyro value, and Data0 to Data11 are fitting parameters;
the calculation method of the dynamic and static state switching threshold A comprises the following steps: a=gnoise+gtemp+gdata, wherein GNoise is a 1/2 noise fluctuation peak value when the gyro is stationary; GTemp is the zero offset variation value of the gyroscope in the working temperature range of the system; GData is a reserved threshold;
the course angle YAW calculation method under the motion state comprises the following steps: yaw=yaw1+gyro·time,
YAW is the current course angle, YAWl is the last course angle, gyro is angular velocity data acquired by a Gyro, and Time is system integration Time of 10ms;
the course angle YAW calculation method under the static state comprises the following steps: YAWYAWd ten random noise,
YAW is the current course angle, YAWd is the course angle when dynamic enters a static state, and Rannomoise is the noise estimated value of the system and is limited within +/-0.1;
after angular velocity data of the gyroscope are collected, singular points of the gyroscope are removed firstly, and then the gyroscope data are compensated;
and carrying out amplitude limiting filtering on the compensated gyro data by adopting a digital filter, and then calculating a dynamic course angle by integrating operation.
By adopting the technical scheme, when the MEMS gyroscope is applied to the AGV, the heading angle is judged not simply by relying on the performance of the gyroscope, but an adaptive algorithm is invented through temperature, parameter calibration and digital filtering so as to integrate the gyroscope according to the current motion state of the AGV.
Therefore, the method has the following beneficial effects:
the MEMS gyroscope has the advantages that the MEMS gyroscope can be applied to the AGV, the performance of the MEMS gyroscope is equivalent to or even better than that of a gyroscope with high price, the cost of AGV application development is greatly reduced, more choices are provided for the AGV application, the market of the MEMS gyroscope in the AGV application is opened, and the cost of the gyroscope and the burden of the AGV manufacturer on the model selection are reduced.
The specific technical scheme and the beneficial effects of the invention are described in detail in the following detailed description with reference to the accompanying drawings.
Drawings
The invention is further described with reference to the drawings and detailed description which follow:
FIG. 1 is a schematic diagram of a typical MEMS gyroscope in an AGV application;
FIG. 2 is a flowchart of an algorithm for a typical MEMS gyroscope on an AGV application;
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes only and not for all embodiments of the present invention. Based on the examples in the implementation manner, other examples obtained by a person skilled in the art without making creative efforts fall within the protection scope of the present invention.
Referring to fig. 1 and 2, a model system of a general MEMS gyroscope in an AGV application, which implements positioning of an AGV trolley by using the general MEMS gyroscope, includes:
the gyro data acquisition module is used for: the system is used for collecting angular velocity data of the MEMS gyroscope installed on the AGV.
And singular points of the angular velocity data need to be removed.
And the gyro data compensation module is used for: compensating the acquired gyro data, wherein the compensation method sequentially comprises temperature compensation, precision calibration and zero offset subtraction;
the state judging module is used for: comparing the compensated gyro data with a dynamic and static state switching threshold A, and if the gyro data is larger than the dynamic and static state switching threshold A, judging that the AGV is in a moving state and judging that the AGV is in a static state;
heading angle YAW calculation module: the course angle YAW in the moving state and the course angle YAW in the stationary state are calculated by a dynamic course angle YAW calculation method and a static course angle YAW calculation method, respectively.
Zero bias treatment of the gyro during power-on:
firstly, the system is electrified to collect static 20 gyroscope data and store the static 20 gyroscope data into a cache GyroBuff;
secondly, sorting the cache GyroBuff to obtain GyroBuff1;
and thirdly, taking the middle 16 data of the cache GyroBuff1 to perform mean value processing to obtain zero BIAS BIAS of the gyroscope.
Those skilled in the art will appreciate that the number of powered-up acquisition static gyroscopic data may be greater than 20. And once stationary, zero offset needs to be recalculated.
And (3) carrying out full-temperature compensation at the temperature of-40-85 ℃ on the gyroscope. Since the AGV trolley to which the present invention relates is for industrial-level applications, temperature compensation corresponds to industrial-level temperatures.
Firstly, performing second-order least square fitting on the acquired full-temperature gyro data and temperature:
Gyro＝At·Temp 2 +Bt·Temp+Ct
wherein Gyro is Gyro data, temp is temperature data, at is a second-order coefficient, bt is a first-order coefficient, and Ct is a constant;
the first step can be used to determine the At, bt and Ct values.
Secondly, calculating a temperature curve value Gyro1 at the current temperature:
Gyro1＝At·Temp 2 +Bt·Temp+Ct
wherein Temp is the current temperature value;
thirdly, calculating a temperature curve value Gyro2 at 25 ℃:
Gyro2＝At·25 2 +Bt·25+Ct
fourthly, temperature compensation is carried out on the gyro data:
GroOut＝GyroRead-(Gyro1-Gyro2)
wherein gyrout is a gyro value after temperature compensation, gyroRead is a read gyro value.
And calibrating the gyroscope.
The system collects multiple groups of data (each group comprises positive values and negative values) of the gyroscope at +/-5 degrees/s, +/-30 degrees/s, +/-50 degrees/s, +/-75 degrees/s, +/-85 degrees/s, +/-100 degrees/s and the like, averages the data of each group, and uses a least square method to carry out fitting treatment on the data to finally obtain the scale factors, zero offset coefficients and cross coupling parameters of the gyroscope data of each group. The specific calculation method can refer to the prior art, for example, paper literature < avionic attitude measurement system technical research based on MEMS-IMU >, harbin engineering university, shuoshi research: liu Kun, guide: liu Fanming.
Compensating the collected gyro data according to the scale factors, zero offset coefficients and cross coupling parameters:
wherein GyroXOut, gyroYOut, gyroZOut is the compensated gyro value, gyroXRead, gyroYRead, gyroZRead is the read gyro value, and Data0 to Data11 are fitting parameters. Data0, data4, data8 are scale factor coefficients of the tri-axis gyroscope, data9, data10, data11 are zero bias coefficients of the tri-axis gyroscope, and the rest are cross-coupling coefficients, so that three axes are required to be calibrated together because an integrated tri-axis gyroscope is used.
Real-time judging motion state of AGV
Step one, calculating a threshold value A of system dynamic and static state switching:
a=gnoise ten GTemp ten GData
Wherein, GNoil is 1/2 noise fluctuation peak value when the gyro is static; GTemp is zero offset change value of the gyroscope in the working temperature range of the system, and GData is a reserved threshold value of software.
Secondly, integrating and calculating a course angle YAW under the dynamic condition:
YAWYAWl+Gyro·Time
YAW is the current course angle, YAWl is the last course angle, gyro is angular velocity data acquired by the Gyro, and Time is the system integration Time of 10ms.
And judging the dynamic situation, and carrying out digital filtering processing on the compensated data by a digital filter before carrying out integral calculation. The motion state can be calibrated by temperature, precision, zero offset reduction and digital filtering.
Third, calculating a course angle YAW under static conditions:
YAW＝YAWd+RandomNoise
YAW is the current course angle, YAWd is the course angle when dynamic enters static state, rannomoise is the noise estimated value of the system and is limited within +/-0.1, and the noise estimated value is calculated by adding random values to the system and adding the random values to the output value. The angular noise itself is random and irregular, and therefore random numbers are used, which are generated by a random function.
While the invention has been described in terms of specific embodiments, it will be appreciated by those skilled in the art that the invention is not limited to the specific embodiments described above. Any modifications which do not depart from the functional and structural principles of the present invention are intended to be included within the scope of the appended claims.
Claims (1)
1. A model system for a MEMS gyroscope in an AGV application, comprising:
the gyro data acquisition module is used for: the system comprises an AGV, a MEMS gyroscope, a sensor and a sensor, wherein the AGV is used for acquiring angular speed data of the MEMS gyroscope installed on the AGV;
and the gyro data compensation module is used for: compensating the acquired gyro data, wherein the compensation method sequentially comprises temperature compensation, precision calibration and zero offset subtraction;
the state judging module is used for: comparing the compensated gyro data with a dynamic and static state switching threshold A, and if the gyro data is larger than the dynamic and static state switching threshold A, judging that the AGV is in a moving state and judging that the AGV is in a static state;
heading angle YAW calculation module: respectively calculating a course angle YAW in a motion state and a course angle YAW in a static state by adopting a dynamic course angle YAW calculation method and a static course angle YAW calculation method;
the method for calculating zero offset of the gyro during power-on is as follows:
firstly, the system is electrified to collect static 20 gyroscope data and store the static 20 gyroscope data into a cache GyroBuff;
secondly, sorting the cache GyroBuff to obtain GyroBuff1;
thirdly, taking 16 data in the middle of the cache GyroBuff1 to perform mean value processing to obtain zero BIAS BIAS of the gyroscope; carrying out full-temperature compensation at-40-85 ℃ on the gyroscope:
firstly, performing second-order least square fitting on the acquired full-temperature gyro data and temperature:
Gyro＝At·Temp 2 +Bt·Temp+Ct
wherein Gyro is Gyro data, temp is temperature data, at is a second-order coefficient, bt is a first-order coefficient, and Ct is a constant;
calculating the values of At, bt and Ct through fitting;
secondly, calculating a temperature curve value Gyro1 at the current temperature:
Gyro1＝At·Temp 2 +Bt·Temp+Ct
wherein Temp is the current temperature value;
thirdly, calculating a temperature curve value Gyro2 at 25 ℃:
Gyro2＝At·25 2 +Bt·25+Ct
fourthly, temperature compensation is carried out on the gyro data:
GyroOut＝GyroRead-(Gyro1-Gyro2)
wherein gyrout is a gyro value after temperature compensation, gyroRead is a read gyro value;
the gyro precision calibration method comprises the following steps:
collecting n groups of data of the gyroscope at +/-x degrees/s, wherein n is more than or equal to 7, averaging each group of data respectively, performing fitting treatment on the data by using a least square method, finally obtaining scale factors, zero offset and cross coupling parameters of each group of gyroscope data, and compensating the collected gyroscope data:
wherein GyroXOut, gyroYOut, gyroZOut is a compensated gyro value, gyroXRead, gyroYRead, gyroZRead is a read gyro value, and Data0 to Data11 are fitting parameters;
the calculation method of the dynamic and static state switching threshold A comprises the following steps: a=gnoise+gtemp+gdata,
wherein, GNoil is 1/2 noise fluctuation peak value when the gyro is static; GTemp is the zero offset variation value of the gyroscope in the working temperature range of the system; GData is a reserved threshold;
the course angle YAW calculation method under the motion state comprises the following steps: yaw=yawl+gyro·time,
YAW is the current course angle, YAWI is the last course angle, gyro is angular velocity data acquired by a Gyro, and Time is the system integration Time of 10ms;
the course angle YAW calculation method under the static state comprises the following steps: yaw=yawd+random noise,
YAW is the current course angle, YAWd is the course angle when dynamic enters a static state, and Rannomoise is the noise estimated value of the system and is limited within +/-0.1;
after angular velocity data of the gyroscope are collected, singular points of the gyroscope are removed firstly, and then the gyroscope data are compensated;
and carrying out amplitude limiting filtering on the compensated gyro data by adopting a digital filter, and then calculating a dynamic course angle by integrating operation.
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