KR20130099672A - Fuzzy inference system and method - Google Patents

Abstract

PURPOSE: A fuzzy inference system and a method thereof solve the problem of cumulative errors in a gyroscope free inertial navigation system (GFINS) by effectively correcting data of an accelerometer. CONSTITUTION: An autonomous ground vehicle (AGV) linear velocity calculation unit (140) calculates the linear velocity of an AGV by using the rotation speed of a wheel. An accelerometer linear velocity measurement unit (150) measures a linear velocity by using an accelerometer. An encoder linear velocity calculation unit (160) calculates a linear velocity based on an encoder signal. A first linear velocity rate calculation unit (170) calculates the ratio of the AGV linear velocity to the accelerometer linear velocity. A second linear velocity rate calculation unit (180) calculates the ratio of the accelerometer linear velocity to the encoder linear velocity. An accelerometer correction unit (190) corrects the accelerometer linear velocity based on the linear velocity ratios calculated by the first linear velocity rate calculation unit and the second linear velocity ratio calculation unit. [Reference numerals] (140) AGV linear velocity calculation unit; (150) Accelerometer linear velocity measurement unit; (160) Encoder linear velocity calculation unit; (170) First linear velocity rate calculation unit; (180) Second linear velocity rate calculation unit; (190) Accelerometer correction unit

Description

Fuzzy Inference System and Method

The present invention relates to a fuzzy inference system and method, and more particularly, to a fuzzy inference system and method that can solve the cumulative error problem of GFINS by effectively correcting the data of the accelerometer.

Recently, in the logistics industry, in order to increase work efficiency, interest in autonomous vehicles capable of traveling in all directions regardless of the attitude of the robot, together with both the wheel driving method and the axle driving method, has increased.

Representative omni-directional drives include a swave drive, a lonomic drive, and a mecanum drive. Of these, the mecanum drive is driven by using a mecanum wheel, which is equipped with a plurality of rollers inclined at 45 ° around a general wheel. Mecanum wheels offer greater strength and greater power transmission than holographic drives. However, it is difficult to measure the position with the encoder alone because slippage may occur due to the roller mounted around the wheel. Therefore, inertial navigation is required to precisely position the AGV (Autonomous Ground Vechicle) regardless of the sliding of the roller.

INS (Inertial Navigation System) is a device that measures the relative moving distance and direction of an object by measuring the movement inertia for translational movement and rotational inertia for rotational movement. In general, INS uses a gyroscope to measure movement inertia. However, in the early days of INS research, the problem of price increase caused by expensive gyroscopes was raised, and interest in GFINS (Gyroscope Free Inertial Navigation System) without using a gyroscope was raised to solve this problem. However, recently, with the development of the micro-electromechanical system (MEMS), the price of the gyroscope has been reduced, and the cost advantage of the GFINS has been reduced. However, it is still actively studied because of the advantages of miniaturization and low power.

GFINS is a device that can measure rotational inertia using only accelerometer or tilt sensor without using gyroscope to measure rotational inertia. In GFINS, the accelerometer is mainly used as an inertial sensor, and the linear velocity and yaw angle are calculated by integrating the measured inertial data. However, the accelerometer has a problem in that reliability is inferior because there is a problem that an error accumulates over time.

An object of the present invention is to provide a fuzzy inference system (FIS) and a method for solving the cumulative error problem of GFINS by effectively correcting the data of an accelerometer. .

Fuzzy inference system according to an embodiment of the present invention for achieving the above object, AGV linear velocity calculation unit for calculating the linear velocity of the AGV (Autonomous Ground Vechicle) using the rotational speed of the wheel; An accelerometer linear velocity measurement unit for measuring a linear velocity using an accelerometer; An encoder linear velocity calculation unit configured to calculate a linear velocity based on an encoder signal transmitting power to a motor connected to the wheel; A first linear velocity ratio calculation unit that calculates a ratio of the AGV linear velocity calculated by the AGV linear velocity calculation unit and the acceleration linear velocity measured by the accelerometer linear velocity measuring unit; A second linear velocity ratio calculation unit that calculates a ratio of the acceleration linear velocity measured by the acceleration linear velocity measurement unit and the encoder linear velocity calculated by the encoder linear velocity calculation unit; And an accelerometer correction for correcting the linear velocity of the accelerometer according to a correction ratio set based on the linear velocity ratio calculated by the first linear velocity ratio calculator and the linear velocity ratio calculated by the second linear velocity ratio calculator. It is characterized by including a wealth.

The AGV linear velocity calculation unit may calculate the linear velocity caused by the rotation of each wheel and the linear velocity actually acting on the floor by the rollers by using the kinematics of the AGV equipped with a mecanum wheel.

The first linear velocity ratio calculation unit calculates the linear velocity ratio using the AGV average linear velocity calculated by the AGV linear velocity calculation unit and the average linear velocity measured by the accelerometer linear velocity measurement unit.

Fuzzy inference method according to an embodiment of the present invention for achieving the above object, the step of calculating the linear velocity of the AGV using the rotational speed of the wheel; Measuring the linear velocity using an accelerometer; Calculating a linear velocity based on an encoder signal for transmitting power to a motor connected to the wheel; Calculating a first linear velocity ratio between the AGV linear velocity calculated by the AGV linear velocity calculation step and the accelerometer linear velocity measured by the accelerometer linear velocity measurement step; Calculating a second linear velocity ratio of the acceleration linear velocity measured by the acceleration linear velocity measurement step and the encoder linear velocity calculated by the encoder linear velocity calculation step; And correcting the linear velocity of the accelerometer according to the correction ratio set based on the first linear velocity ratio and the second linear velocity ratio.

In the AGV linear velocity calculation step, the linear velocity caused by the rotation of each wheel and the linear velocity actually acting on the floor by the roller may be calculated using the kinematics of the AGV equipped with a mecanum wheel.

In the first linear velocity ratio calculating step, the linear velocity ratio is calculated using the AGV average linear velocity calculated by the AGV linear velocity calculation unit and the average linear velocity measured by the accelerometer linear velocity measurement unit.

According to the present invention, the cumulative error problem of the GFINS can be solved by effectively correcting the data of the accelerometer using a fuzzy inference system that inputs the linear velocity ratio of the AGV and the accelerometer and the linear velocity ratio of the encoder and the accelerometer.

1 is a view schematically showing the configuration of an AGV equipped with a mecanum wheel.
2 is a diagram schematically illustrating a configuration of a fuzzy inference system according to an exemplary embodiment of the present invention.
3 is a view for explaining the kinematics of the AGV equipped with a mecanum wheel.
4 is a diagram showing the membership function of the FIS, (a) shows the linear velocity ratio between the AGV and the accelerometer, (b) shows the linear velocity ratio between the encoder and the accelerometer, and (c) shows the correction ratio of the accelerometer. .
5 is a diagram comparing the RMSE of the yaw angle, (a) shows the RMSE for the straight direction, (b) shows the RMSE for the lateral direction, (c) shows the RMSE for the diagonal direction.
6 is a diagram comparing the RMSE of the linear velocity, (a) shows the RMSE for the linear direction, (b) shows the RMSE for the lateral direction, and (c) shows the RMSE for the diagonal direction.
7 is a flowchart illustrating a fuzzy inference method according to an exemplary embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description, a detailed description of known techniques well known to those skilled in the art may be omitted.

In describing the constituent elements of the present invention, the same reference numerals may be given to constituent elements having the same name, and the same reference numerals may be given thereto even though they are different from each other. However, even in such a case, it does not mean that the corresponding component has different functions according to the embodiment, or does not mean that the different components have the same function. It should be judged based on the description of each component in the example.

In the following description of the embodiments of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

In addition, in describing the component of this invention, terms, such as 1st, 2nd, A, B, (a), (b), can be used. These terms are intended to distinguish the constituent elements from other constituent elements, and the terms do not limit the nature, order or order of the constituent elements. When a component is described as being "connected", "coupled", or "connected" to another component, the component may be directly connected or connected to the other component, Quot; may be "connected," "coupled," or "connected. &Quot;

1 is a view schematically showing the configuration of an AGV equipped with a mecanum wheel.

In the mecanum wheel AGV according to the present invention, the fuzzy estimation system 100 may be installed corresponding to each wheel 110, and DAQ may be used to control a motor of each wheel 110. A BLDC motor of each wheel 110 is provided with a motor drive (not shown) for receiving and transmitting a flag control for controlling the operation of the motor and a signal from the encoder 120. The signal of the encoder 120 is a quadrature waveform, and the rotation amount of the motor can be known using the interrupt of the AVR. The measured data of the encoder 120 is collected by a main controller (not shown) using CAN communication, and the data of the encoder 120 transmitted to the main controller is converted into a linear velocity and an angular velocity using a kinematic equation. In addition, the AGV is provided with an accelerometer 130 corresponding to each wheel (110).

2 is a diagram schematically illustrating a configuration of a fuzzy inference system according to an exemplary embodiment of the present invention.

2, the fuzzy inference system 100 according to an embodiment of the present invention, the AGV linear velocity calculation unit 140, the accelerometer linear velocity measurement unit 150, the encoder linear velocity calculation unit 160, the first line And a speed ratio calculator 170, a second linear speed ratio calculator 180, and an accelerometer correction unit 190.

The AGV linear velocity calculation unit 140 calculates the linear velocity of the AGV using the rotational speed of the wheel.

In FIG. 3, which is shown to explain the kinematics of an AGV equipped with mecanum wheels, L is the longitudinal distance between the center of rotation of the AGV and the wheel center, and W is the horizontal distance between the center of rotation of the AGV and the wheel center. . v _iw is the linear velocity due to the rotation of each wheel, and v _ir is the linear velocity actually acting on the floor by the rollers. In practice, the linear velocities v _iX and v _iY acting on each wheel can be derived using Equation 1 using v _iw and v _ir .

[Equation 1]

Equation 1 can be summarized as Equation 2 with respect to v _iw .

&Quot; (2) "

The linear velocity acting on the wheels different from Equation 2 as shown in Equation 3 by using the speeds v _X , v _Y and the angular velocity w _Z acting on the center axis of rotation of AGV, as shown in Equation 3 v _iX and v _iY Can be obtained.

&Quot; (3) "

By substituting Equation 3 into Equation 2, v _iw can be expressed as an equation relating to v _x , v _y , and w _z as in Equation 4.

&Quot; (4) "

Equation 4 is expressed as a determinant for each wheel.

&Quot; (5) "

The inverse of F is calculated in Equation 5, multiplied by both sides, and summed up as Equation 6.

&Quot; (6) "

If the radius of the wheel is R _w and the rotational angular velocity of the wheel is θ _i , then v _iw in Equation 6 can be expressed as Equation 7 in the form of R _w θ _i .

[Equation 7]

Using Equation 7, it is possible to calculate the linear velocity of the AGV using the rotational speed of the wheel.

The accelerometer linear velocity measuring unit 150 measures the linear velocity using the accelerometer 130.

The encoder speed calculator 160 calculates a linear speed based on a signal of the encoder 120 that transmits power of a motor connected to the wheel 110. The linear velocity of the encoder 120 used to calibrate the accelerometer 130 of the GFINS may be calculated by applying to Equation (7).

The first linear velocity ratio calculating unit 170 calculates a ratio of the AGV linear velocity calculated by the AGV linear velocity calculating unit 140 and the accelerometer linear velocity measured by the accelerometer linear velocity measuring unit 150.

In addition, the second linear velocity ratio calculating unit 180 calculates a ratio of the accelerometer linear velocity measured by the accelerometer linear velocity measuring unit 150 and the linear velocity calculated by the encoder linear velocity calculating unit 160.

That is, two inputs of the FIS used in the fuzzy inference system 100 according to the embodiment of the present invention are provided. The first input is the ratio of the linear velocity of the accelerometer 130 and the AGV for proportionally calibrating the accelerometer using the average linear velocity of the AGV. The second input is the linear velocity of the accelerometer 130. The ratio of the linear velocities of the encoder 120 and the accelerometer 130 is used to convert them to be similar to the linear velocities.

In the embodiment of the present invention, the laser navigation may be installed in the center of the AGV to measure the linear velocity of the AGV. The x- and y-axis linear velocities of AGVs measured in laser navigation are shown in Table 1, and the average linear velocities are used to calculate the ratio of the linear velocities of the AGVs and the linear velocities of the accelerometer. The straight line and the side are the linear velocities when the AGV is driven along the y and x axes, respectively, and the diagonal is the linear velocities when the diagonal lines are run on the x and y axis coordinate planes. In Table 1, each numerical unit represents mm / s.

[Table 1]

The first linear velocity ratio calculator 170 calculates an average linear velocity of AGV and a linear velocity ratio of the accelerometer 130 with respect to the first input of the FIS using Equation 8.

[Equation 8]

In Equation 8, n _{i , X} , ni _{, and Y} are ratios of linear velocities of the AGV and the accelerometer 130 with respect to the x and y axes, respectively, and A _{ri , X} , A _{ri , and Y} are accelerometers 130 at time t. X- and y-axis linear velocities. And C _vX and C _vY are the average linear velocities of the x and y axes of AGV, respectively.

The second linear velocity ratio calculating unit 180 calculates a ratio of the accelerometer linear velocity measured by the accelerometer linear velocity measuring unit 150 and the encoder linear velocity calculated by the encoder linear velocity calculating unit 160.

The encoder 120 may not accurately measure the position due to the sliding of the roller, but may accurately measure the rotation of the wheel 110. In addition, the accelerometer 130 may have an approximate measurement of the amount of change in position regardless of the sliding error. Therefore, the data of the accelerometer 130 may be corrected according to the change of the encoder 120. In this case, the second linear velocity ratio calculator 180 may calculate a ratio between the accelerometer linear velocity and the encoder linear velocity as shown in Equation (9).

&Quot; (9) "

In Equation 9, R _{i , X} , R _{i , Y} are linear velocity ratios of the encoder 120 and the accelerometer 130 with respect to the x and y axes, and r _{i , X} , r _{i , Y} are accelerometers 130, respectively. Is a value obtained by changing the x and y axis linear velocities of the encoder 120 into the x and y axis linear velocities. And e _{i , X} , e _{i , and Y} are the x and y axis linear velocities of the encoder 120, respectively.

&Quot; (10) "

A _{i , X} , a _{i , and Y} in Equation 10 are the differences in the linear velocities of the accelerometer 130 and the linear velocities of the accelerometer 130 corrected using FIS, respectively, for the x and y axes. Calculate

&Quot; (11) "

In Equation 11, Af is the linear velocity of the accelerometer 130 corrected using the FIS in the previous time. The moment m can be obtained through experiments using the fact that the previous linear velocity has a constant effect on the current linear velocity. In the present invention, a value of 1.657 was obtained through a preliminary experiment in which AGV was repeatedly run at 380 mm / s.

The accelerometer correction unit 190 is configured based on the linear speed ratio calculated by the first linear speed ratio calculation unit 170 and the linear speed ratio calculated by the second linear speed ratio calculation unit 180. Accordingly, the linear velocity of the accelerometer 130 is corrected.

There are two inputs of the FIS for the calibration of the accelerometer 130, and the linear velocity ratio n of the AGV and the linear velocity of the accelerometer 130 obtained according to the above equations, the linear velocity of the encoder 120 and the accelerometer ( 130) is the linear velocity ratio R. Both inputs are correction rates between 0 and 1. The membership function of the FIS used in the present invention was experimentally designed using a heuristic technique. The membership function of the FIS is as shown in FIG. 4. The value output through the output function of FIG. 4C is used to correct the linear velocity of the accelerometer 130. The fuzzy rules used are shown in Table 2.

[Table 2]

The experiment for the fuzzy inference system according to the present invention was repeated 10 times in the direction of the straight line, the side, and the diagonal at the speed of 380 mm / s in the space of 300 cm x 300 cm, and the fuzzy-GFINS and the actual AGV according to the present invention. In order to compare the movements of the lasers, a global positioning sensor, laser navigation, was attached to the center of the AGV. Laser navigation is a device whose global position can be measured within an error range of 4 mm to 20 mm in the space where the reflector is installed.

In addition, in order to evaluate the performance of the fuzzy-GFINS according to the present invention, the linear velocity and yaw angle calculated by the data of the encoder 120 and the accelerometer 130 were compared with the linear velocity and yaw angle measured by laser navigation.

The equation for calculating the yaw angle from each sensor is shown in Equation 12.

&Quot; (12) "

Is the yaw angle, and X and Y are the x and y axis positions measured from the sensors, respectively. However, the AGV equipped with mecanum wheels cannot compare the calculated yaw angles directly because the rotation speed and direction of the wheels differ depending on the driving direction. Therefore, in order to compare the yaw angle calculated from the fuzzy-GFINS and the sensors according to the present invention, RMSE with laser navigation was calculated for each driving direction. 5 is a diagram comparing the RMSE of the yaw angle, (a) shows the RMSE for the straight direction, (b) shows the RMSE for the lateral direction, (c) shows the RMSE for the diagonal direction.

As shown in FIG. 5, the fuzzy-GFINS according to the present invention showed similar performance to the encoder 120 in the case of the straight line and the side, but the performance was improved in the case of the diagonal. The average, maximum and minimum of the calculated RMSE for each direction using the data of FIG. 5 are shown in Table 3.

[Table 3]

As can be seen in FIG. 5 and Table 3, the accelerometer 130 has an error that increases with time when traveling in a straight line, side, and diagonal due to the cumulative error, thereby resulting in a different result for each experiment. there was. However, the fuzzy-GFINS according to the present invention was found to have a smaller error than the GFINS using only the accelerometer by correcting the cumulative error of the accelerometer. Fuzzy-GFINS according to the present invention has been shown to be similar to the encoder in the case of straight and side travel, it can be seen that improved than the encoder in the diagonal travel. This is because, due to the characteristics of the mecanum wheel, the slippage of the roller mounted on the wheel is more generated when the wheel is diagonally driven than the straight and the lateral drive, thereby increasing the measurement error.

The equation for calculating the linear velocity using the data measured by the accelerometer is shown in Equation 13.

&Quot; (13) "

In Equation 13, V is a linear velocity calculated by the amount of change in position of the x and y axes. ΔX and ΔY are the amount of change in position of the x and y axes, respectively, and acc _X and acc _Y are the accelerations of the x and y axes. In order to compare the linear velocities calculated from the sensors, the RMSE for laser navigation was calculated as the yaw angle. 6 is a graph showing the change of RMSE of linear velocity with time.

As can be seen in Fig. 6, the linear velocity is similar to the yaw angle. Fuzzy-GFINS according to the present invention can be confirmed that the error is less than the GFINS using only the accelerometer for straight, side, and diagonal driving direction. This means that the cumulative error of GFINS using only accelerometer is effectively corrected. The RMSE of the fuzzy-GFINS and the encoder according to the present invention were similar with respect to the straight line and the lateral direction. This is because the encoder cannot recognize the sliding of the roller attached to the mecanum wheel. However, the fuzzy-GFINS according to the present invention has a relatively small error compared to the encoder because the linear velocity is measured regardless of slip. Table 4 shows the average, maximum and minimum RMSE for each direction represented by the data of FIG. 6.

[Table 4]

The experimental results show that the RMSE of the fuzzy-GFINS according to the present invention is lower than that of the accelerometer in the straight and lateral directions similar to the yaw angle and similar to the encoder. In addition, it was confirmed that the diagonal performance is improved compared to the encoder.

7 is a flowchart illustrating a fuzzy inference method according to an exemplary embodiment of the present invention.

1 to 7, the AGV linear velocity calculating unit 140 calculates the linear velocity of the AGV using the rotational speed of the wheel, and the accelerometer linear velocity measuring unit 150 uses the accelerometer 130 to calculate the linear velocity. The speed is measured, and the encoder speed calculator 160 calculates the linear speed based on the signal of the encoder 120 which transmits the power of the motor connected to the wheel 110 (S710).

The first linear velocity ratio calculator 170 calculates a ratio of the AGV linear velocity calculated by the AGV linear velocity calculator 140 and the accelerometer linear velocity measured by the accelerometer linear velocity measurer 150. The linear velocity ratio calculator 180 calculates a ratio of the accelerometer linear velocity measured by the accelerometer linear velocity measurer 150 and the linear velocity calculated by the encoder linear velocity calculator 160 (S720).

The accelerometer correction unit 190 is configured based on the linear speed ratio calculated by the first linear speed ratio calculation unit 170 and the linear speed ratio calculated by the second linear speed ratio calculation unit 180. Accordingly, the linear velocity of the accelerometer 130 is corrected (S730).

The present invention is not necessarily limited to these embodiments, as all the constituent elements constituting the embodiment of the present invention are described as being combined or operated in one operation. In other words, within the scope of the present invention, all of the components may be selectively operated in combination with one or more. In addition, although all of the components may be implemented as one independent hardware, some or all of the components may be selectively combined to perform a part or all of the functions in one or a plurality of hardware. As shown in FIG. In addition, such a computer program may be stored in a computer-readable medium such as a USB memory, a CD disk, a flash memory, etc., and read and executed by a computer, thereby implementing embodiments of the present invention. As the storage medium of the computer program, a magnetic recording medium, an optical recording medium, a carrier wave medium, or the like may be included.

Furthermore, all terms including technical or scientific terms have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, unless otherwise defined in the Detailed Description. Terms used generally, such as terms defined in a dictionary, should be interpreted to coincide with the contextual meaning of the related art, and shall not be interpreted in an ideal or excessively formal sense unless explicitly defined in the present invention.

The foregoing description is merely illustrative of the technical idea of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention. In addition, the embodiments disclosed in the present invention are not intended to limit the scope of the present invention but to limit the scope of the technical idea of the present invention. Accordingly, the scope of protection of the present invention should be construed according to the claims, and all technical ideas within the scope of equivalents should be interpreted as being included in the scope of the present invention.

Claims (6)

An AGV linear velocity calculation unit configured to calculate a linear velocity of AGV (Autonomous Ground Vechicle) using the rotational speed of the wheel;
An accelerometer linear velocity measurement unit for measuring a linear velocity using an accelerometer;
An encoder linear velocity calculation unit configured to calculate a linear velocity based on an encoder signal transmitting power to a motor connected to the wheel;
A first linear velocity ratio calculation unit that calculates a ratio of the AGV linear velocity calculated by the AGV linear velocity calculation unit and the acceleration linear velocity measured by the accelerometer linear velocity measuring unit;
A second linear velocity ratio calculation unit that calculates a ratio of the acceleration linear velocity measured by the acceleration linear velocity measurement unit and the encoder linear velocity calculated by the encoder linear velocity calculation unit; And
An accelerometer corrector for correcting the linear velocity of the accelerometer according to a correction ratio set based on the linear velocity ratio calculated by the first linear velocity ratio calculator and the linear velocity ratio calculated by the second linear velocity ratio calculator
Fuzzy inference system comprising a.
The method of claim 1,
The AGV linear velocity calculation unit,
Fuzzy inference system, characterized by calculating the linear velocity due to the rotation of each wheel and the linear velocity actually acting on the floor by the roller using the kinematics of the AGV equipped with a mecanum wheel.
The method of claim 1,
The first linear velocity ratio calculation unit,
Fuzzy inference system, characterized in that to calculate the linear velocity ratio using the average linear velocity measured by the AGV average linear velocity calculated by the AGV linear velocity calculation unit.
Calculating the linear velocity of the AGV using the rotational speed of the wheel, measuring the linear velocity using an accelerometer, and calculating the linear velocity based on an encoder signal transmitting power to the motor connected to the wheel;
The first linear velocity ratio of the AGV linear velocity calculated by the AGV linear velocity calculation step and the acceleration linear velocity measured by the accelerometer linear velocity measurement step, and the acceleration linear velocity measured by the acceleration linear velocity measurement step; Calculating a second linear velocity ratio of the encoder linear velocity calculated by the encoder linear velocity calculating step; And
Correcting the linear velocity of the accelerometer according to a correction ratio set based on the first linear velocity ratio and the second linear velocity ratio
Fuzzy inference method comprising a.
5. The method of claim 4,
The AGV linear velocity calculation step,
Fuzzy reasoning method characterized by calculating the linear velocity due to the rotation of each wheel and the linear velocity actually acting on the floor by the roller using the kinematics of the AGV equipped with a mecanum wheel.
5. The method of claim 4,
The first linear velocity ratio calculating step,
Fuzzy inference method characterized in that to calculate the linear velocity ratio using the average linear velocity measured by the AGV average linear velocity calculated by the AGV linear velocity calculation unit.

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