A chassis calibration method for a dual-wheel differential storage logistics robot

Abstract

The present invention is applicable to the field of unmanned driving technology, and provides a two-wheel differential storage logistics robot chassis calibration method, which pre-sets the moving distance and publishes the moving speed to the car; continuously monitors the distance between the car's own base coordinate system and the odometer coordinate system (odom), and when it is detected that the distance between the base coordinate system and the odometer coordinate system (odom) is less than the first preset range, determines that the car has reached the preset moving distance; and measures the difference between the actual moving distance of the car and the distance detected between the coordinate systems. The linear velocity and angular velocity of the two-wheel differential mobile chassis can be calibrated quickly and accurately, so that the chassis can more accurately locate and move the target point during automatic navigation, and ensure the accuracy and reliability of the chassis when performing tasks, thereby improving the intelligence of the two-wheel differential storage logistics robot chassis calibration method.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned driving, and in particular relates to a chassis calibration method for a dual-wheel differential storage logistics robot. Background Art

[0002] With the rapid development of e-commerce, supply chain management and logistics industry, traditional warehousing and logistics methods are facing more and more challenges and pressures. In this case, two-wheel differential warehouse logistics robots have become one of the solutions. With their automation, intelligence and high efficiency, they play an important role in the field of warehousing and logistics.

[0003] As a mechanical structure and motor-driven device, the two-wheel differential mobile chassis will inevitably have errors during the installation process, and the performance of different motors will also be different; even if the control program is the same, the movement effects of different chassis will be different. Therefore, after each two-wheel differential mobile chassis is made, its linear velocity and angular velocity need to be calibrated. Calibration is a dynamic process used to check the accuracy of the test equipment and eliminate errors to ensure the accuracy of the measurement results. Only after calibration can the theoretical value of the chassis's moving distance be consistent with the actual measurement value, and the chassis can more accurately locate and move the target point during automatic navigation to ensure the accuracy and reliability of the chassis when performing tasks. To this end, the patent of the present invention provides a chassis calibration method for a two-wheel differential mobile robot, which is convenient for the rapid calibration of the two-wheel differential mobile robot. Summary of the invention

[0004] The purpose of the present invention is to provide a dual-wheel differential storage logistics robot chassis calibration method, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented as follows: a chassis calibration method for a dual-wheel differential storage logistics robot, the method comprising:

[0006] Step S100: pre-set the moving distance and issue the moving speed to the car;

[0007] Continuously monitor the distance between the base coordinate system of the car and the odometer coordinate system (odom), and when it is detected that the distance between the base coordinate system and the odometer coordinate system (odom) is less than a first preset range, determine that the car has reached a preset moving distance;

[0008] Measure the difference between the actual moving distance of the car and the distance detected between the coordinate systems. If the difference is greater than the preset threshold, it is determined that the correction value of the odometer needs to be modified;

[0009] Step S200: Control the speed of the left and right wheels of the robot so that the left and right wheels of the robot rotate in opposite directions and at the same speed, and make circular motion along the center point of the chassis. The test angle is 360 degrees rotation of the chassis.

[0010] After the rotation is completed, the actual rotation angle of the robot is measured and compared with the preset theoretical value to obtain the rotation angle error;

[0011] If the rotation angle error is greater than the second preset range, it is determined that the robot chassis parameters need to be modified to achieve the target angle value.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the step S100 specifically includes:

[0013] Step S101: Make the chassis move in a straight line until the target value is equal to the odom feedback value and the actual chassis position and posture;

[0014] Step S102: suspend the chassis in the air, open the linear speed calibration tool, set the chassis running distance to 3 meters, the speed to 0.3 m / s, the allowed error distance to 0.01 m, and the odom correction coefficient to 1;

[0015] Step S103: Start the robot and let it drive along the reference line to the end point. During the whole driving process, use a ruler to measure the driving distance of the left and right wheels, and get the pulse number of the left and right wheels at the same time. Divide the driving distance of the right wheel by the driving distance of the left wheel to get the proportional relationship between the driving distances of the right wheel and the left wheel. After repeating for many times, get the radius coefficient of the right wheel to correct the driving distance of the right wheel, so that the chassis can drive in a straight line, and the deviation distance is two or three millimeters, and the deviation measurement accuracy is one thousandth;

[0016] Step S104: repeat step S103, when the travel distances of the left and right wheels are equal, record the feedback data of odom, including the two values ​​of x and y;

[0017] Divide the actual travel distance by x to get the correction coefficient, which reflects the proportional relationship between the odom feedback data and the actual travel distance. Multiply the radius coefficients of the left and right wheels by the correction coefficient at the same time, so that the robot can move more accurately according to the odom feedback data in actual movement, thereby improving the accuracy of movement.

[0018] Step S105: Repeat step S104 for multiple times, and fine-tune the radius coefficient based on step S104 according to actual experimental conditions, until the actual displacements of the left and right wheels are consistent with the x in the data fed back by odom, so as to meet the accuracy requirements;

[0019] Step S106: When processing the y data fed back by odom, due to the difference between the two motors, the same angular velocity and the same radius coefficient result in different numbers of pulses output by the motors, so it is necessary to process the arc per millimeter of the motor rotation:;

[0020] Record the number of pulses at the start of the left and right wheels and the number of pulses after the left and right wheels stop during the experiment, and get the number of pulses d1 and d2 of the left and right wheels. Calculate the displacement of the left and right wheels according to the formula: displacement of left and right wheels = number of pulse changes / radians rotated per millimeter by the motor*corrected radius coefficient of the left and right wheels;

[0021] The y value is corrected by averaging the displacements of the left and right wheels to obtain the corrected value of the motor per millimeter.

[0022] Step S107: Repeat the above steps to achieve consistency among the theoretical value, the odom feedback data and the actual position and posture of the mobile chassis, and achieve the accuracy requirement of 0.2%.

[0023] As a further limitation of the technical solution of the embodiment of the present invention, the step S200 specifically includes:

[0024] Step S201: Arrange an angular velocity calibration scene, and modify the angle of chassis rotation according to actual conditions;

[0025] Step S202: Suspend the chassis in the air, open the angular velocity calibration tool, test the chassis rotation angle of 360 degrees, the speed is 0.3rad / s, the allowed error angle is set to 1 degree, and the correction coefficient of odom is set to 1. This coefficient makes the quaternion of odom Feedback outputs a fixed value each time;

[0026] Step S203: Convert the quaternion to Euler angles, and obtain the Euler angle conversion formula: in is the inverse tangent function, It is the inverse sine function. The w in the quaternion is the scalar part, which represents the size of the rotation. The x, y, and z are the vector parts, which represent the direction of the rotation axis. The roll angle refers to the rotation around the front-rear axis (X axis), and the rotation angle of the chassis around this axis is calculated. The pitch angle refers to the rotation around the left-right axis (Y axis), and the rotation angle of the chassis around this axis is calculated. The yaw angle refers to the rotation around the vertical axis (Z axis), and the rotation angle of the chassis around this axis is calculated.

[0027] The actual rotation angle of the chassis is obtained through the above Euler angle conversion formula, and the error value between the actual rotation angle and the theoretical rotation angle is made within the allowable error range by modifying the correction coefficient of odom.

[0028] As a further limitation of the technical solution of the embodiment of the present invention, when calibrating the linear velocity and angular velocity, since the ground friction coefficients of different road surfaces are different, it is necessary to calibrate the linear velocity and angular velocity separately under different road surfaces and different friction coefficients.

[0029] Compared with the prior art, the present invention pre-sets the moving distance and publishes the moving speed to the car; continuously monitors the distance between the car's own base coordinate system and the odometer coordinate system (odom), and when it is detected that the distance between the base coordinate system and the odometer coordinate system (odom) is less than the first preset range, it is determined that the car has reached the preset moving distance; measures the difference between the actual moving distance of the car and the distance detected between the coordinate systems, and if the difference is greater than the preset threshold, it is determined that the correction value of the odometer needs to be modified; controls the speed of the left and right wheels of the robot so that the left and right wheels of the robot rotate in opposite directions and at the same speed, and make circular motion along the center point of the chassis, and the test angle is 360 degrees of chassis rotation; after the rotation is completed, measures the actual rotation angle of the robot, and compares it with the preset theoretical value to obtain the rotation angle error; if the rotation angle error is greater than the second preset range, it is determined that the robot chassis parameters need to be modified to achieve the target angle value;

[0030] The linear velocity and angular velocity of the two-wheel differential mobile chassis can be calibrated quickly and accurately, so that the chassis can more accurately locate and move the target point during automatic navigation, and ensure the accuracy and reliability of the chassis when performing tasks, thereby improving the intelligence of the two-wheel differential warehouse logistics robot chassis calibration method. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A flowchart of a method provided by an embodiment of the present invention;

[0032] Figure 2 A diagram of a linear speed calibration scene arrangement of the method provided in an embodiment of the present invention;

[0033] Figure 3 A diagram of an angular velocity calibration scene arrangement for the method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0035] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0036] Specifically, a dual-wheel differential storage logistics robot chassis calibration method is provided, and the method specifically comprises the following steps:

[0037] Step S100, pre-set the moving distance and issue the moving speed to the car;

[0038] Continuously monitor the distance between the base coordinate system of the car and the odometer coordinate system (odom), and when it is detected that the distance between the base coordinate system and the odometer coordinate system (odom) is less than a first preset range, determine that the car has reached a preset moving distance;

[0039] The difference between the actual moving distance of the car and the distance detected between the coordinate systems is measured. If the difference is greater than the preset threshold, it is determined that the correction value of the odometer needs to be modified.

[0040] Wherein, step S100 specifically includes the following steps:

[0041] Step S101: Make the chassis move in a straight line until the target value is equal to the odom feedback value and the actual chassis position. The calibration scene needs to select a flat and spacious enough field without obstacles and other factors that affect the robot's straight-line driving; determine the starting point and target point and the driving baseline on the field. The linear speed calibration scene is as follows: Figure 2 As shown;

[0042] Step S102: suspend the chassis in the air, open the linear speed calibration tool, set the chassis running distance to 3 meters, the speed to 0.3 m / s, the allowed error distance to 0.01 m, and the odom correction coefficient to 1;

[0043] It is understandable that, based on the difference between the actual measured moving distance and the expected moving distance, the coefficient of the odometer is fine-tuned or corrected so that it can more accurately reflect the actual operation of the robot.

[0044] Step S103: Start the robot and let it drive along the reference line to the end point. During the whole driving process, use a ruler to measure the driving distance of the left and right wheels, and get the pulse number of the left and right wheels at the same time. Divide the driving distance of the right wheel by the driving distance of the left wheel to get the proportional relationship between the driving distances of the right wheel and the left wheel. After repeating for many times, get the radius coefficient of the right wheel to correct the driving distance of the right wheel so that the chassis can drive in a straight line, and the deviation distance is two or three millimeters, and the deviation accuracy is one thousandth.

[0045] Step S104: repeat step S103, when the travel distances of the left and right wheels are equal, record the feedback data of odom, including the two values ​​of x and y;

[0046] The actual travel distance is divided by x to get the correction coefficient, which reflects the proportional relationship between the odom feedback data and the actual travel distance. The radius coefficients of the left and right wheels are multiplied by the correction coefficient at the same time, so that the robot can move more accurately according to the odom feedback data in actual movement, thereby improving the accuracy of movement.

[0047] Step S105: Repeat step S104 for multiple times, and fine-tune the radius coefficient based on step S104 according to actual experimental conditions, until the actual displacements of the left and right wheels are consistent with the x in the data fed back by odom, so as to meet the accuracy requirements;

[0048] Step S106: When processing the y data fed back by odom, due to the difference between the two motors, the same angular velocity and the same radius coefficient result in different numbers of pulses output by the motors, so it is necessary to process the arc per millimeter of the motor rotation:;

[0049] Record the number of pulses at the start of the left and right wheels and the number of pulses after the left and right wheels stop during the experiment, and get the number of pulses d1 and d2 of the left and right wheels. Calculate the displacement of the left and right wheels according to the formula: displacement of left and right wheels = number of pulse changes / radians rotated per millimeter by the motor*corrected radius coefficient of the left and right wheels;

[0050] The y value is corrected by averaging the displacements of the left and right wheels to obtain the corrected value of the motor per millimeter.

[0051] Step S107: Repeat the above steps to achieve consistency among the theoretical value, the odom feedback data and the actual position and posture of the mobile chassis, and achieve the accuracy requirement of 0.2%.

[0052] Furthermore, the dual-wheel differential storage logistics robot chassis calibration method further includes the following steps:

[0053] Step S200, control the speed of the left and right wheels of the robot so that the left and right wheels of the robot rotate in opposite directions and at the same speed, and make circular motion along the center point of the chassis. The test angle is 360 degrees rotation of the chassis.

[0054] After the rotation is completed, the actual rotation angle of the robot is measured and compared with the preset theoretical value to obtain the rotation angle error;

[0055] If the rotation angle error is greater than the second preset range, it is determined that the robot chassis parameters need to be modified to achieve the target angle value.

[0056] Wherein, step S200 specifically includes the following steps:

[0057] Step S201: Arrange the angular velocity calibration scene, and modify the angle of chassis rotation according to the actual situation. It can be understood that the angular velocity calibration scene is arranged as follows: Figure 3 As shown, the rotation angle of the chassis can be modified according to actual conditions;

[0058] Step S202: Suspend the chassis in the air, open the angular velocity calibration tool, test the chassis rotation angle of 360 degrees, the speed is 0.3rad / s, the allowed error angle is set to 1 degree, and the correction coefficient of odom is set to 1. This coefficient makes the quaternion of odom Feedback outputs a fixed value each time;

[0059] Step S203: Convert the quaternion to Euler angles, and obtain the Euler angle conversion formula: in is the inverse tangent function, It is the inverse sine function. The w in the quaternion is the scalar part, which represents the size of the rotation. The x, y, and z are the vector parts, which represent the direction of the rotation axis. The roll angle refers to the rotation around the front-rear axis (X axis), and the rotation angle of the chassis around this axis is calculated. The pitch angle refers to the rotation around the left-right axis (Y axis), and the rotation angle of the chassis around this axis is calculated. The yaw angle refers to the rotation around the vertical axis (Z axis), and the rotation angle of the chassis around this axis is calculated.

[0060] The actual rotation angle of the chassis is obtained through the above Euler angle conversion formula, and the error value between the actual rotation angle and the theoretical rotation angle is made within the allowable error range by modifying the correction coefficient of odom.

[0061] It is understandable that when calibrating the linear velocity and angular velocity, since the ground friction coefficients of different road surfaces are different, it is necessary to calibrate the linear velocity and angular velocity separately under different road surfaces and different friction coefficients.

[0062] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0063] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0064] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0065] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0066] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dual-wheel differential storage logistics robot chassis calibration method, characterized in that: The method comprises: Step S100: pre-set the moving distance and issue the moving speed to the car; Continuously monitor the distance between the base coordinate system of the car and the odometer coordinate system odom, and when it is detected that the distance between the base coordinate system and the odometer coordinate system odom is less than a first preset range, determine that the car has reached the preset moving distance; Measure the difference between the actual moving distance of the car and the distance detected between the coordinate systems. If the difference is greater than the preset threshold, it is determined that the correction value of the odometer needs to be modified; The step S100 specifically includes: Step S101: Make the chassis move in a straight line until the target value is equal to the odom feedback value and the actual chassis position and posture; Step S102: suspend the chassis in the air, open the linear speed calibration tool, set the chassis running distance to 3 meters, the speed to 0.3 m / s, the allowed error distance to 0.01 m, and the odom correction coefficient to 1; Step S103: Start the robot and let it drive along the reference line to the end point. During the whole driving process, use a ruler to measure the driving distance of the left and right wheels, and get the pulse number of the left and right wheels at the same time. Divide the driving distance of the right wheel by the driving distance of the left wheel to get the proportional relationship between the driving distances of the right wheel and the left wheel. After repeating for many times, get the radius coefficient of the right wheel to correct the driving distance of the right wheel, so that the chassis can drive in a straight line, and the deviation distance is two or three millimeters, and the deviation accuracy is one thousandth. Step S104: repeat step S103, when the travel distances of the left and right wheels are equal, record the feedback data of odom, including the two values ​​of x and y; Divide the actual travel distance by x to get the correction coefficient, which reflects the proportional relationship between the odom feedback data and the actual travel distance. Multiply the radius coefficients of the left and right wheels by the correction coefficient at the same time, so that the robot can move more accurately according to the odom feedback data in actual movement, thereby improving the accuracy of movement. Step S105: Repeat step S104 for multiple times, and fine-tune the radius coefficient based on step S104 according to actual experimental conditions, until the actual displacements of the left and right wheels are consistent with the x in the data fed back by odom, so as to meet the accuracy requirements; Step S106: When processing the y data fed back by odom, due to the difference between the two motors, the same angular velocity and the same radius coefficient result in different numbers of pulses output by the motors, so it is necessary to process the arc per millimeter of the motor rotation:; Record the number of pulses at the start of the left and right wheels and the number of pulses after the left and right wheels stop during the experiment, and get the number of pulses d1 and d2 of the left and right wheels. Calculate the displacement of the left and right wheels according to the formula: displacement of left and right wheels = number of pulse changes / radians rotated per millimeter by the motor*corrected radius coefficient of the left and right wheels; The y value is corrected by averaging the displacements of the left and right wheels to obtain the corrected value of the motor per millimeter. Step S107: Repeat the above steps to achieve consistency among the theoretical value, the odom feedback data and the actual position and posture of the mobile chassis, and achieve the accuracy requirement of 0.2%. Step S200: Control the speed of the left and right wheels of the robot so that the left and right wheels of the robot rotate in opposite directions and at the same speed, and make circular motion along the center point of the chassis. The test angle is 360 degrees rotation of the chassis. After the rotation is completed, the actual rotation angle of the robot is measured and compared with the preset theoretical value to obtain the rotation angle error; If the rotation angle error is greater than the second preset range, it is determined that the robot chassis parameters need to be modified to achieve the target angle value.

2. The dual-wheel differential storage logistics robot chassis calibration method according to claim 1 is characterized in that: The step S200 specifically includes: Step S201: Arrange an angular velocity calibration scene, and modify the angle of chassis rotation according to actual conditions; Step S202: Suspend the chassis in the air, open the angular velocity calibration tool, test the chassis rotation angle of 360 degrees, the speed is 0.3rad / s, the allowed error angle is set to 1 degree, and the correction coefficient of odom is set to 1. This coefficient makes the quaternion of odom Feedback outputs a fixed value each time; Step S203: Convert the quaternion to Euler angles, and obtain the Euler angle conversion formula: in is the inverse tangent function, It is the inverse sine function. The w in the quaternion is the scalar part, which represents the magnitude of the rotation. The x, y, and z are the vector parts, which represent the direction of the rotation axis. Roll refers to the rotation around the front and rear axis, and the rotation angle of the chassis around this axis is calculated. Pitch refers to the rotation around the left and right axis, and the rotation angle of the chassis around this axis is calculated. Yaw refers to the rotation around the vertical axis, and the rotation angle of the chassis around this axis is calculated. The actual rotation angle of the chassis is obtained through the above Euler angle conversion formula, and the error value between the actual rotation angle and the theoretical rotation angle is made within the allowable error range by modifying the correction coefficient of odom.

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