CN114253241B - Path tracking method for industrial intelligent trolley - Google Patents
Path tracking method for industrial intelligent trolley Download PDFInfo
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- CN114253241B CN114253241B CN202111569359.7A CN202111569359A CN114253241B CN 114253241 B CN114253241 B CN 114253241B CN 202111569359 A CN202111569359 A CN 202111569359A CN 114253241 B CN114253241 B CN 114253241B
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Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/4189—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by the transport system
- G05B19/41895—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by the transport system using automatic guided vehicles [AGV]
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a path tracking method for an industrial intelligent trolley, which is characterized in that a current position, a current gesture and an inertial parameter of the vehicle are obtained according to a positioning module and a planning module which are arranged in the vehicle, whether the current vehicle state accords with the gesture of the planned path or not is calculated, and a steering wheel angle which is to be controlled at the next moment is calculated, wherein the vehicle corner for controlling the steering wheel angle is obtained by adding a transverse angle deviation, a route angle deviation and a yaw angle deviation. The invention relates to an improved stanley algorithm considering the dynamic characteristics of a vehicle, which has obvious improvement on the effect of the original stanley algorithm, can better control a steering wheel of the vehicle in the path tracking process to drive the vehicle along the path, improves the path tracking precision, particularly can basically reach the centimeter level under the turning working condition with poor tracking precision, effectively improves the transverse control level of the vehicle, and can smoothly pass through a plurality of curves with small turning radius and narrow space.
Description
Technical Field
The invention relates to the technical field of intelligent automatic trolleys in the field of industrial automation, in particular to a path tracking method for an industrial intelligent trolley.
Background
Along with the development of intelligent factories, intelligent automatic trolleys (Automated Guided Vehicle, AGVs for short) are increasingly applied, the intelligent trolleys sense surrounding environment and vehicle positions through cameras, radars, high-precision positioning sensors and the like, unmanned intelligent trolleys in the factories are realized through path planning algorithms and vehicle transverse and longitudinal control technologies, and the operation efficiency of the factories is improved.
In the control process of intelligent automatic trolleys in the field of industrial automation, a path tracking algorithm is often used, and the main function of the algorithm is to analyze the current vehicle pose and a target path and calculate the angle of a steering wheel of the vehicle to be controlled at the next moment. Currently, a wide-ranging stanley path tracking algorithm is used for calculating the wheel angle required for driving to a target path according to the course angle and the transverse deviation of a vehicle by utilizing the steering geometric relation of the vehicle, so as to realize path tracking. Referring to fig. 1, in fig. 1: e represents the shortest distance from the front wheels of the vehicle to the path; v represents the current speed of the own vehicle; delta represents wheel rotation angle; psi 1 represents the heading angle of the corresponding path point of the front wheel; psi 2 represents the current heading angle of the own vehicle; beta represents the wheel rotation angle calculated due to the lateral deviation.
The algorithm can realize the path tracking effect in a straight-line working condition, and can not oscillate at high speed, but has larger path tracking deviation and can not accurately track the path to run in a turning working condition, especially in a road condition with smaller turning radius.
Disclosure of Invention
In order to overcome the defects, the invention provides a path tracking method for an industrial intelligent trolley, and the improved path tracking method is provided by changing the gesture of a vehicle in the movement process, and can effectively improve the path tracking precision of turning working conditions by combining the dynamic characteristics of the vehicle in the driving process, so that the required control effect is achieved, and the application requirements are met through practical tests.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a path tracking method for an industrial intelligent trolley, which is used for obtaining the current position, the gesture and the inertia parameters of a vehicle according to a positioning module and a planning module which are arranged in the vehicle, calculating whether the current vehicle state accords with the gesture of the planning path, and calculating the steering wheel angle which should be controlled at the next moment, comprises the following steps:
acquiring the current position coordinates, the current course angle and the angular speed change rate of each axis of the vehicle;
determining the transverse angle deviation of the vehicle according to the current position coordinates of the vehicle and the planned path of the vehicle;
determining an included angle between the heading of the vehicle and a tangent line of a nearest point of a target path, namely, a route angle deviation according to the current position coordinates and the current heading angle of the vehicle;
obtaining the yaw rate of the vehicle under the turning working condition according to the vehicle posture, and determining the yaw rate deviation of the vehicle;
and adding the determined transverse angle deviation, the determined course angle deviation and the determined yaw angle deviation of the current vehicle to obtain the wheel angle of the vehicle.
As a further development of the invention, the lateral angular deviation is obtained by means of the current speed of the vehicle, the current position of the vehicle, and the corresponding path projection position of the vehicle, which are obtained by means of the positioning module and the planning module.
As a further development of the invention, the course angle deviation is obtained by the positioning module and the planning module providing the desired course angle.
As a further improvement of the present invention, the yaw angle deviation is obtained by vehicle yaw rate information provided by the positioning module.
As a further development of the invention, the magnitude of the vehicle yaw angle deviation is calculated by multiplying the vehicle yaw angle speed by the time interval.
As a further improvement of the present invention, the time interval is set according to the speed, curvature of the vehicle.
As a further improvement of the present invention, when the vehicle speed, curvature are large, the time interval setting is large; when the vehicle speed, curvature is small, the time interval setting is small.
The beneficial effects of the invention are as follows: the invention mainly aims at the transverse control problem of the intelligent automatic trolley, discovers the defects of the algorithm based on the existing stanley path tracking algorithm, solves the problems that the original stanley algorithm has larger path tracking error under the turning working condition and can not well run along the path, and the like, proposes to dynamically correct the output wheel corner by combining the dynamic characteristics of the vehicle during turning, and effectively reduces the tracking deviation caused by transverse and heading during path tracking, so that the vehicle can accurately run along the path, thereby solving the path tracking problem of the intelligent automatic trolley in an industrial automatic factory.
Drawings
FIG. 1 is a schematic diagram of a vehicle state of a conventional path tracking algorithm;
FIG. 2 is a schematic representation of the vehicle state of the method of the present invention;
FIG. 3 is a schematic flow chart of the method of the present invention.
Wherein, in fig. 1: e represents the shortest distance from the front wheels of the vehicle to the path; v represents the current speed of the own vehicle; delta represents wheel rotation angle; psi 1 represents the heading angle of the corresponding path point of the front wheel; psi 2 represents the current heading angle of the own vehicle; beta represents the wheel rotation angle calculated due to the lateral deviation;
in fig. 2: e is the shortest distance from the front wheels of the vehicle to the path; v is the vehicle speed; delta is the wheel rotation angle; psi is course angle; ω is the yaw angle.
Detailed Description
A preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.
According to the path tracking method for the industrial intelligent trolley, the position, the posture and the inertia parameters (angular velocities of all shafts) of the self-vehicle are analyzed, whether the current vehicle state meets the posture requirement of path planning or not is calculated, displacement errors and posture errors are calculated, the steering wheel angle which is required to be controlled at the next moment is calculated according to the inertia parameters, and then high-precision path tracking of a specified path is realized according to real-time feedback of vehicle data.
The input data of the present invention includes:
1) The current position coordinates of the vehicle, the current course angle and the change rate of the angular speed of each shaft of the vehicle;
2) Tracking the coordinates of the track points of the path and the course angle of each track point;
the output data includes: steering wheel angle of vehicle.
Referring to fig. 2, the principle and structure of the method of the present invention are schematically shown, and the present invention is described in detail below:
(1) Calculation of the lateral angular deviation:
assuming that the predicted trajectory of the vehicle at the current wheel turning angle intersects the closest point tangent at a closest point d (t) on a given target path, the following function is derived from the geometric relationship:
d (t) in the above formula is an artificial assumption, and it is generally considered that d (t) is related to the vehicle speed v and k, and the magnitude of k determines the convergence rate of the lateral deviation, but too large an overshoot, requiring repeated debugging to achieve the required accuracy
(2) Calculating course angle deviation:
the course angle deviation refers to the difference between the current course angle of the vehicle and the course angle of the closest point of the target path, namely the included angle between the course of the vehicle and the tangent line of the closest point of the target path,
ψ(t)＝ψ1(t)-ψ2(t)
(3) Calculation of yaw angle deviation:
under the turning working condition, the vehicle generates yaw rate under the action of lateral force, and due to the system delay, the vehicle posture at the current moment and the action moment is changed, and the deviation is corrected by utilizing the yaw angle, wherein dt is a time interval, and the vehicle speed and the path curvature corresponding to the current moment jointly influence the dt, such as: when the speed and the curvature are larger, dt is larger, and when the speed and the curvature are smaller, dt is smaller. By adjusting the dt, the angle correction is realized, and the required control precision is achieved
ω(t)＝α*dt
(4) Calculating the wheel rotation angle:
adding the three angle deviations to obtain the wheel angle of the vehicle
δ(t)＝ψ(t)+β(t)+ω(t)
The flow chart of the present invention is shown in fig. 3.
Therefore, the invention provides a path tracking method for an industrial intelligent trolley, which is an improved stanley algorithm considering the dynamic characteristics of the vehicle, has obvious improvement on the effect of the original stanley algorithm, can better control a steering wheel of the vehicle in the path tracking process to drive the vehicle along the path, improves the path tracking precision, particularly can achieve centimeter-level tracking precision under the turning condition with poor tracking precision, effectively improves the transverse control level of the vehicle, and can smoothly pass through a plurality of curves with small turning radius and narrow space. The method can realize the path tracking requirement of the intelligent automatic trolley in the intelligent factory with high precision through a large number of verification and experiments, and improves the automation degree of the intelligent factory.
In the above description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The foregoing description is only of a preferred embodiment of the invention, which can be practiced in many other ways than as described herein, so that the invention is not limited to the specific implementations disclosed above. While the foregoing disclosure has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes and modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. Any simple modification, equivalent variation and modification of the above embodiments according to the technical substance of the present invention without departing from the technical solution of the present invention still falls within the scope of the technical solution of the present invention.
Claims (6)
1. The path tracking method for the industrial intelligent trolley is characterized by comprising the steps of obtaining the current position, the gesture and the inertia parameters of a vehicle according to a positioning module and a planning module which are arranged in the vehicle, calculating whether the current vehicle state accords with the gesture of the planned path, and calculating the steering wheel angle which is required to be controlled at the next moment, and the method is characterized by comprising the following steps:
acquiring the current position coordinates, the current course angle and the angular speed change rate of each axis of the vehicle;
determining the transverse angle deviation of the vehicle according to the current position coordinates of the vehicle and the planned path of the vehicle;
determining an included angle between the heading of the vehicle and a tangent line of a nearest point of a target path, namely, a route angle deviation according to the current position coordinates and the current heading angle of the vehicle;
obtaining the yaw rate of the vehicle under the turning working condition according to the vehicle posture, and determining the yaw rate deviation of the vehicle;
adding the determined transverse angle deviation, the determined route angle deviation and the determined yaw angle deviation of the current vehicle to obtain the wheel angle of the vehicle;
the transverse angular deviation is obtained through the current speed of the vehicle, the current position of the vehicle and the corresponding path projection position of the vehicle, which are obtained by the positioning module and the planning module.
2. The path tracking method for an industrial intelligent car according to claim 1, characterized in that: the course angle deviation is obtained by providing a required course angle through a positioning module and a planning module.
3. The path tracking method for an industrial intelligent car according to claim 1, characterized in that: the yaw angle deviation is obtained through vehicle yaw angle speed information provided by the positioning module.
4. A path tracking method for an industrial intelligent car according to claim 3, characterized in that: the magnitude of the vehicle yaw angle deviation is calculated by multiplying the vehicle yaw angle speed by a time interval.
5. The path tracking method for an industrial intelligent car according to claim 4, wherein: the time interval is set according to the speed and curvature of the vehicle.
6. The path tracking method for an industrial intelligent car according to claim 5, wherein: when the speed and the curvature of the vehicle are large, the time interval is set large; when the vehicle speed, curvature is small, the time interval setting is small.
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