CN112678726B - Forklift AGV kinematics model-based goods taking positioning method and system - Google Patents
Forklift AGV kinematics model-based goods taking positioning method and system Download PDFInfo
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Abstract
The invention discloses a goods taking positioning method and a goods taking positioning system based on a forklift AGV kinematics model, wherein based on the established kinematics model when a forklift turns, a certain transverse deviation is reserved when the forklift turns to a turning terminal point, and then a database is established according to the transverse error, a vehicle body angle and a steering wheel angle, so that the steering wheel angle required to be given is predicted, and the forklift can accurately drive to a goods taking destination point; and establishing a database when the forklift runs to a turning target point, and establishing a plurality of databases in the goods taking distance from the forklift to predict to ensure that the error is small when the forklift runs to the goods taking target point.
Description
Technical Field
The invention relates to a forklift AGV kinematics model-based goods taking positioning method and system, and belongs to the technical field of mobile robots.
Background
The current mobile robot technology develops faster, the method for positioning the bottom layer of the forklift is an important technology in the AGV, the problem that the forklift type AGV moves to a target point position inaccurately is solved by adopting some mathematical methods mainly through establishing a mathematical model, and the problem is mainly caused by the fact that a forklift steering wheel reacts not fast enough, has the characteristic of time delay and the uneven ground. The performance of the steering wheel of the forklift is difficult to improve from the aspect of hardware.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a goods taking and positioning method and system based on a forklift AGV kinematics model.
In order to solve the technical problem, the invention provides a goods taking and positioning method based on a forklift type AGV kinematics model,
1) acquiring the kinematic model data of the current forklift, and calling a test database determined by the kinematic model data of the forklift in advance; the test database comprises a plurality of correction point databases positioned at correction points and a plurality of turning point databases positioned at turning target points, and each database comprises a plurality of groups of data of vehicle body angles, transverse deviations and steering wheel angles; at least one correction database is arranged between the adjacent turning target points and between the last turning target point and the goods taking target point;
2) acquiring the body angle and the transverse deviation of a forklift at a certain turning target point, comparing the body angle and the transverse deviation with a turning point database of the turning target point, acquiring a group of data, in the turning point database of the turning target point, of which the errors with the body angle and the transverse deviation of the forklift are within a preset first error, if a plurality of groups of data meet conditions, adding the body angle and the transverse deviation in each group of data, determining the group of data with smaller error as a final group of data, and controlling the forklift to run at a front fork according to the steering wheel angle in the final group of data;
3) After the forklift runs for a preset distance, the body angle and the transverse deviation of the forklift at the current position are collected, the data of the database at the previous position are used for judging once, if the error of the body angle and the error of the transverse deviation are both smaller than a second error preset in advance, the forklift continues to run,
otherwise, comparing the body angle and the transverse deviation of the current position forklift with a correction point database of the correction point, acquiring a group of data of which the errors with the body angle and the transverse deviation of the current position forklift are within a preset second error in the correction point database of the correction point, if a plurality of groups of data meet the condition, adding the body angle and the transverse deviation in each group of data, determining the group of data with smaller error as a final group of data, and controlling the forklift to drive according to the steering wheel angle in the final group of data;
4) repeating the step 3) until the forklift runs to the next turning target point;
5) and repeating the steps 2) to 4) until the forklift runs to the goods taking target point.
Further, the determination process of the test database determined in advance by the kinematic model data of the forklift includes:
Establishing a kinematic model of the current forklift:
x^2+y^2＝1,0＜x＜1,0＜y＜1
wherein x represents the lower horizontal coordinate of the warehouse map coordinate system, and y represents the lower vertical coordinate of the warehouse map coordinate system;
and (3) deducing the transverse deviation of the forklift reaching a turning target point according to the kinematic model of the forklift:
en′＝1-sin(beta)
wherein beta represents a vehicle body angle;
and transversely reserving a transverse error a in the forklift, wherein the current transverse deviation is as follows:
en＝en′+a
wherein en' is the transverse deviation of a turning target point of the forklift;
and carrying out a test of forking the front fork of the forklift to a goods taking target point for at least one hundred times, collecting at least one hundred groups of data of the angle of the forklift body, the transverse deviation and the angle of the steering wheel at the turning target point, collecting at least one hundred groups of data of the angle of the forklift body, the transverse deviation and the angle of the steering wheel at a preset distance between the turning target point and the goods taking target point, and establishing a correction point database and a turning point database.
Further, the error value of the vehicle body angle in the first error is larger than the error value of the vehicle body angle in the second error; the value of the error of the lateral deviation in the first error is greater than the value of the error of the lateral deviation in the second error.
A goods taking and positioning system based on a forklift type AGV kinematics model,
the system comprises a calling module, a receiving module and a processing module, wherein the calling module is used for obtaining the kinematic model data of the current forklift and calling a test database determined by the kinematic model data of the forklift in advance; the test database comprises a plurality of correction point databases positioned at correction points and a plurality of turning point databases positioned at turning target points, and each database comprises a plurality of groups of data of vehicle body angles, transverse deviations and steering wheel angles; at least one correction database is arranged between the adjacent turning target points and between the last turning target point and the goods taking target point;
The system comprises a first prediction module, a second prediction module and a control module, wherein the first prediction module is used for acquiring the vehicle body angle and the transverse deviation of the forklift located at a certain turning target point, comparing the vehicle body angle and the transverse deviation with a turning point database of the turning target point, acquiring a group of data in the turning point database of the turning target point, the errors of the vehicle body angle and the transverse deviation of the forklift are within a preset first error, if a plurality of groups of data meet conditions, adding the vehicle body angle and the transverse deviation in each group of data, determining the group of data with smaller error as a final group of data, and controlling the forklift to carry out front fork running according to the steering wheel angle in the final group of data;
the second prediction module is used for collecting the body angle and the transverse deviation of the forklift at the current position after the forklift runs for a preset distance, using the data of the database at the previous position for one-time judgment, continuing running if the error of the body angle and the error of the transverse deviation are both smaller than a second error preset in advance,
otherwise, comparing the body angle and the transverse deviation of the current position forklift with a correction point database of the correction point, acquiring a group of data of which the errors with the body angle and the transverse deviation of the current position forklift are within a preset second error in the correction point database of the correction point, if a plurality of groups of data meet the condition, adding the body angle and the transverse deviation in each group of data, determining the group of data with smaller error as a final group of data, and controlling the forklift to drive according to the steering wheel angle in the final group of data;
The first control module is used for controlling the second prediction module to work until the forklift runs to the next turning target point;
and the second control module is used for controlling the first prediction module and the second prediction module to work until the forklift runs to the goods taking target point.
Further, the retrieving module comprises:
the building module is used for building a kinematic model of the current forklift:
x^2+y^2＝1,0＜x＜1,0＜y＜1
wherein x represents the lower horizontal coordinate of the warehouse map coordinate system, and y represents the lower vertical coordinate of the warehouse map coordinate system;
and the pushing module is used for deducing the transverse deviation of the forklift reaching the turning target point according to the kinematic model of the forklift:
en′＝1-sin(beta)
wherein beta represents a vehicle body angle;
and transversely reserving a transverse error a in the forklift, wherein the current transverse deviation is as follows:
en＝en′+a
wherein en' is the transverse deviation of a turning target point of the forklift;
the test module is used for testing at least one hundred times of front fork of the turning forklift to a goods taking target point, acquiring at least one hundred groups of data of the angle of the forklift body, the transverse deviation and the angle of the steering wheel at the turning target point, acquiring at least one hundred groups of data of the angle of the forklift body, the transverse deviation and the angle of the steering wheel at a preset distance between the turning target point and the goods taking target point, and establishing a correction point database and a turning point database.
Further, the error value of the vehicle body angle in the first error is larger than the error value of the vehicle body angle in the second error; the value of the error of the lateral deviation in the first error is greater than the value of the error of the lateral deviation in the second error.
The invention achieves the following beneficial effects:
according to the invention, when the forklift turns to a turning terminal point, a certain transverse deviation is reserved, and then a database is established according to the transverse error, the vehicle body angle and the steering wheel angle, so that the steering wheel angle required to be given is predicted, and the forklift can accurately drive to a goods taking destination point. When the forklift runs to a turning target point and runs for a certain distance, a database is established for prediction, and the angle of the steering wheel is predicted at least twice in total, so that the error of the forklift running to a goods taking target point is small.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention are described below clearly and completely, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A goods taking positioning method based on a forklift type AGV kinematics model aims to output a steering wheel angle theta according to a collected and input vehicle body angle beta and a transverse deviation en, control a forklift to carry out front fork form, and accurately reach a goods taking target point, in the embodiment, a turning target point and a correction point are taken as an example, but not limited to only one turning target point and one correction point, and the details are as follows:
the method comprises the following steps:
step 1: and establishing a turning kinematic model of the forklift.
x^2+y^2＝1,0＜x＜1,0＜y＜1
Wherein x represents the lower horizontal coordinate of the warehouse map coordinate system, and y represents the lower vertical coordinate of the warehouse map coordinate system;
step 2: and deducing the transverse deviation of the forklift reaching the turning target point by using the forklift kinematic model.
en′＝1-sin(beta)
Wherein beta represents a vehicle body angle;
en＝1-sin(beta)
and step 3: and 3cm of transverse error is reserved in the transverse direction of the forklift, namely the forklift turns to reach a target turning point, and the transverse error between the target turning point and the target goods taking point is 3 cm. The current lateral deviation is then:
en＝en'+3
where en' is the lateral deviation of the point of turning purpose of the truck.
And 4, step 4: and carrying out a test from a front fork to a goods taking point during one hundred times of turning, collecting one hundred groups of data of the vehicle body angle, the transverse deviation and the steering wheel angle at a turning target point, collecting one hundred groups of data of the vehicle body angle, the transverse deviation and the steering wheel angle at a half distance between the turning target point and the goods taking target point, and establishing two databases.
And 5: when the first prediction is carried out, the position of the forklift is the position of a turning target point, the current vehicle body angle and the transverse deviation are collected, the vehicle body angle and the transverse deviation in the first database are traversed, when the collected data are compared with a data group in the database, the vehicle body angle error (errbeta) is smaller than 0.2 degrees, and the transverse deviation error (erren) is smaller than 2mm, the data in the first database are taken out, and the steering wheel angle of the data is predicted. If a plurality of groups of data meet the condition, the two errors are added, and the group of data with smaller errors is taken.
n→0…100
errbeta＝beta-beta1n
erren＝en-en1n
if errbeta<0.2&erren<2
min(errbeta+erren)→theta1n
Wherein beta1n is certain data of the vehicle body angle in the database I, en1n is certain data of the transverse deviation in the database I, beta is the vehicle body angle of the turning target point, and en is the transverse deviation of the turning target point.
Step 6: and (4) using the steering wheel angle predicted by the database to carry out front fork running, and judging once according to the data selected by the first prediction when the front fork runs half of the distance. And if the vehicle body angle error (errbeta) is less than 0.15 degrees and the transverse deviation error (erren) is less than 1.5mm, continuing driving, otherwise, entering the next step.
errbeta＝beta-beta2i
erren＝en-en2i
if errbeta<0.15&erren<1.5
continue
else next step
And 7: when the second prediction is carried out, the forklift is the middle position from the turning target point to the forklift goods taking point, the current vehicle body angle and the transverse deviation are collected, the vehicle body angle and the transverse deviation in the database are traversed, when the collected data are compared with the data set in the database, the vehicle body angle error (errbeta) is smaller than 0.15 degrees, and the transverse deviation error (erren) is smaller than 1.5mm, the data set in the database is taken out, and the steering wheel angle is predicted to be the steering wheel angle of the data set. If a plurality of groups of data meet the condition, the two errors are added, and the group of data with smaller errors is taken.
n→0…100
errbeta＝beta-beta2n
erren＝en-en2n
if errbeta<0.15&erren<1.5
min(errbeta+erren)→theta2n
Beta2n is certain data of the angle of the truck body in the database II, en2n is certain data of the transverse deviation in the database II, beta is the angle of the truck body from the turning target point to the middle position of the goods taking point of the forklift, and en is the transverse deviation from the turning target point to the middle position of the goods taking point of the forklift.
And 8: and (4) using the steering wheel angle predicted by the database II to carry out front fork driving and reach a goods taking target point.
Correspondingly, the invention also provides a goods taking and positioning system based on the forklift AGV kinematics model,
the system comprises a calling module, a receiving module and a processing module, wherein the calling module is used for obtaining the kinematic model data of the current forklift and calling a test database determined by the kinematic model data of the forklift in advance; the test database comprises a plurality of correction point databases positioned at correction points and a plurality of turning point databases positioned at turning target points, and each database comprises a plurality of groups of data of vehicle body angles, transverse deviations and steering wheel angles; at least one correction database is arranged between the adjacent turning target points and between the last turning target point and the goods taking target point;
the system comprises a first prediction module, a second prediction module and a control module, wherein the first prediction module is used for acquiring the vehicle body angle and the transverse deviation of the forklift located at a certain turning target point, comparing the vehicle body angle and the transverse deviation with a turning point database of the turning target point, acquiring a group of data in the turning point database of the turning target point, the errors of the vehicle body angle and the transverse deviation of the forklift are within a preset first error, if a plurality of groups of data meet conditions, adding the vehicle body angle and the transverse deviation in each group of data, determining the group of data with smaller error as a final group of data, and controlling the forklift to carry out front fork running according to the steering wheel angle in the final group of data;
The second prediction module is used for collecting the body angle and the transverse deviation of the forklift at the current position after the forklift runs for a preset distance, using the data of the database at the previous position for one-time judgment, continuing running if the error of the body angle and the error of the transverse deviation are both smaller than a second error preset in advance,
otherwise, comparing the body angle and the transverse deviation of the current position forklift with a correction point database of the correction point, acquiring a group of data of which the errors with the body angle and the transverse deviation of the current position forklift are within a preset second error in the correction point database of the correction point, if a plurality of groups of data meet the condition, adding the body angle and the transverse deviation in each group of data, determining the group of data with smaller error as a final group of data, and controlling the forklift to drive according to the steering wheel angle in the final group of data;
the first control module is used for controlling the second prediction module to work until the forklift runs to the next turning target point;
and the second control module is used for controlling the first prediction module and the second prediction module to work until the forklift runs to the goods taking target point.
The calling module comprises:
the building module is used for building a kinematic model of the current forklift:
x^2+y^2＝1,0＜x＜1,0＜y＜1
and the pushing module is used for deducing the transverse deviation of the forklift reaching the turning target point according to the kinematic model of the forklift:
en′＝1-sin(beta)
wherein beta represents a vehicle body angle;
and transversely reserving a transverse error a in the forklift, wherein the current transverse deviation is as follows:
en＝en′+a
wherein en' is the transverse deviation of a turning target point of the forklift;
the test module is used for testing at least one hundred times of front fork of the turning forklift to a goods taking target point, acquiring at least one hundred groups of data of the angle of the forklift body, the transverse deviation and the angle of the steering wheel at the turning target point, acquiring at least one hundred groups of data of the angle of the forklift body, the transverse deviation and the angle of the steering wheel at a preset distance between the turning target point and the goods taking target point, and establishing a correction point database and a turning point database.
The error value of the vehicle body angle in the first error is greater than the error value of the vehicle body angle in the second error; the value of the error of the lateral deviation in the first error is greater than the value of the error of the lateral deviation in the second error.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (4)
1. A goods taking and positioning method based on a forklift type AGV kinematics model is characterized in that,
1) acquiring the kinematic model data of the current forklift, and calling a test database determined by the kinematic model data of the forklift in advance; the test database comprises a plurality of correction point databases positioned at correction points and a plurality of turning point databases positioned at turning target points, and each database comprises a plurality of groups of data of vehicle body angles, transverse deviations and steering wheel angles; at least one correction database is arranged between the adjacent turning target points and between the last turning target point and the goods taking target point;
the determination process of the test database determined in advance by the kinematic model data of the forklift comprises the following steps:
establishing a kinematic model of the current forklift:
x^2+y^2＝1,0＜x＜1,0＜y＜1
wherein x represents the lower horizontal coordinate of the warehouse map coordinate system, and y represents the lower vertical coordinate of the warehouse map coordinate system;
and (3) deducing the transverse deviation of the forklift reaching a turning target point according to the kinematic model of the forklift:
en′＝1-sin(beta)
wherein beta represents a vehicle body angle;
and transversely reserving a transverse error a in the forklift, wherein the current transverse deviation is as follows:
en＝en′+a
wherein en' is the transverse deviation of a turning target point of the forklift;
carrying out a test of forking a front fork of the forklift to a goods taking target point for at least one hundred times, collecting at least one hundred groups of data of the angle of the forklift body, the transverse deviation and the angle of a steering wheel at the turning target point, collecting at least one hundred groups of data of the angle of the forklift body, the transverse deviation and the angle of the steering wheel at a preset distance between the turning target point and the goods taking target point, and establishing a correction point database and a turning point database;
2) Acquiring the body angle and the transverse deviation of a forklift at a certain turning target point, comparing the body angle and the transverse deviation with a turning point database of the turning target point, acquiring a group of data, in the turning point database of the turning target point, of which the errors with the body angle and the transverse deviation of the forklift are within a preset first error, if a plurality of groups of data meet conditions, adding the body angle and the transverse deviation in each group of data, determining the group of data with smaller error as a final group of data, and controlling the forklift to run at a front fork according to the steering wheel angle in the final group of data;
3) after the forklift runs for a preset distance, the body angle and the transverse deviation of the forklift at the current position are collected, the data of the database at the previous position are used for judging once, if the error of the body angle and the error of the transverse deviation are both smaller than a second error preset in advance, the forklift continues to run,
otherwise, comparing the body angle and the transverse deviation of the current position forklift with a correction point database of the correction point, acquiring a group of data of which the errors with the body angle and the transverse deviation of the current position forklift are within a preset second error in the correction point database of the correction point, if a plurality of groups of data meet the condition, adding the body angle and the transverse deviation in each group of data, determining the group of data with smaller error as a final group of data, and controlling the forklift to drive according to the steering wheel angle in the final group of data;
4) Repeating the step 3) until the forklift runs to the next turning target point;
5) and (5) repeating the steps 2) to 4) until the forklift runs to the goods taking target point.
2. The forklift AGV kinematic model based pickup positioning method of claim 1, wherein the error value of the body angle in the first error is larger than the error value of the body angle in the second error; the value of the error of the lateral deviation in the first error is greater than the value of the error of the lateral deviation in the second error.
3. A goods taking and positioning system based on a forklift type AGV kinematics model is characterized in that,
the system comprises a calling module, a receiving module and a processing module, wherein the calling module is used for obtaining the kinematic model data of the current forklift and calling a test database determined by the kinematic model data of the forklift in advance; the test database comprises a plurality of correction point databases positioned at correction points and a plurality of turning point databases positioned at turning target points, and each database comprises a plurality of groups of data of vehicle body angles, transverse deviations and steering wheel angles; at least one correction database is arranged between the adjacent turning target points and between the last turning target point and the goods taking target point;
the determination process of the test database determined in advance by the kinematic model data of the forklift comprises the following steps:
Establishing a kinematic model of the current forklift:
x^2+y^2＝1,0＜x＜1,0＜y＜1
wherein x represents the lower horizontal coordinate of the warehouse map coordinate system, and y represents the lower vertical coordinate of the warehouse map coordinate system;
and (3) deducing the transverse deviation of the forklift reaching a turning target point according to the kinematic model of the forklift:
en′＝1-sin(beta)
wherein beta represents a vehicle body angle;
and transversely reserving a transverse error a in the forklift, wherein the current transverse deviation is as follows:
en＝en′+a
wherein en' is the transverse deviation of a turning target point of the forklift;
carrying out a test of forking a front fork of the forklift to a goods taking target point for at least one hundred times, collecting at least one hundred groups of data of the angle of the forklift body, the transverse deviation and the angle of a steering wheel at the turning target point, collecting at least one hundred groups of data of the angle of the forklift body, the transverse deviation and the angle of the steering wheel at a preset distance between the turning target point and the goods taking target point, and establishing a correction point database and a turning point database;
the system comprises a first prediction module, a second prediction module and a control module, wherein the first prediction module is used for acquiring the vehicle body angle and the transverse deviation of the forklift located at a certain turning target point, comparing the vehicle body angle and the transverse deviation with a turning point database of the turning target point, acquiring a group of data in the turning point database of the turning target point, the errors of the vehicle body angle and the transverse deviation of the forklift are within a preset first error, if a plurality of groups of data meet conditions, adding the vehicle body angle and the transverse deviation in each group of data, determining the group of data with smaller error as a final group of data, and controlling the forklift to carry out front fork running according to the steering wheel angle in the final group of data;
The second prediction module is used for collecting the body angle and the transverse deviation of the forklift at the current position after the forklift runs for a preset distance, using the data of the database at the previous position for one-time judgment, continuing running if the error of the body angle and the error of the transverse deviation are both smaller than a second error preset in advance,
otherwise, comparing the body angle and the transverse deviation of the current position forklift with a correction point database of the correction point, acquiring a group of data of which the errors with the body angle and the transverse deviation of the current position forklift are within a preset second error in the correction point database of the correction point, if a plurality of groups of data meet the condition, adding the body angle and the transverse deviation in each group of data, determining the group of data with smaller error as a final group of data, and controlling the forklift to drive according to the steering wheel angle in the final group of data;
the first control module is used for controlling the second prediction module to work until the forklift runs to the next turning target point;
and the second control module is used for controlling the first prediction module and the second prediction module to work until the forklift runs to the goods taking target point.
4. The forklift AGV kinematic model based pickup positioning system of claim 3, wherein the error value of the body angle in the first error is greater than the error value of the body angle in the second error; the value of the error of the lateral deviation in the first error is greater than the value of the error of the lateral deviation in the second error.
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