Agv deviation correction control method, electronic device and computer storage medium

Abstract

The embodiment of the application relates to the technical field of AGV deviation rectification control, and discloses an AGV deviation rectification control method, electronic equipment and a computer storage medium. The AGV deviation rectification control method comprises the following steps: acquiring an actual pose and a real-time speed of an AGV; calculating a pose deviation according to the actual pose and an ideal pose; calculating a scaling factor coefficient through speed hierarchical reasoning according to the real-time speed; inputting the pose deviation into a fuzzy controller to obtain a steering angle of a vehicle body running center based on the scaling factor coefficient; and inputting the real-time speed and the steering angle of the vehicle body running center into an AGV motion controller to regulate and control the actual pose of the AGV through the AGV motion controller. The application designs a fuzzy deviation rectification controller based on speed grading, changes the output domain of the fuzzy deviation rectification controller in real time, improves the effectiveness and rapidity of the deviation rectification control in high-speed operation, and realizes fast and stable pose deviation rectification.

Description

TECHNICAL FIELD

[0001] The present application relates to the AGV deviation control technical field, and in particular to an AGV deviation control method, an electronic device and a computer storage medium. BACKGROUND

[0002] AGV (Automated Guided Vehicle, automatic guided vehicle) is widely used in various industrial fields, including warehousing industry, manufacturing industry, dangerous places and special industries, etc. In many application fields, the deviation control of AGV is the basis of realizing automatic guidance control, and is also the core problem of AGV research. The deviation control of AGV belongs to discrete control, and has the characteristics of nonlinearity, uncertainty of model and environment, and inaccuracy of measurement. Due to its special importance, scholars at home and abroad have carried out in-depth research on it.

[0003] In the prior art, the deviation control of AGV is often fuzzy reasoning by position deviation and vehicle body angle deviation, and in this process, the fuzzy rule of deviation change is not considered. However, due to the influence of discrete time, when AGV runs at high speed, the problem that AGV is affected by discrete time and the control effect is low will occur, which affects the stable deviation effect of AGV. SUMMARY

[0004] Therefore, the embodiments of the present application provide an AGV deviation control method, which improves the deviation control effect of AGV.

[0005] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] In a first aspect, the embodiments of the present application provide an AGV deviation control method, characterized in that it comprises: acquiring the actual pose and real-time speed of AGV; calculating the pose deviation according to the actual pose and ideal pose; calculating the scaling factor coefficient according to the real-time speed; inputting the pose deviation into the fuzzy controller based on the scaling factor coefficient to obtain the steering angle of the vehicle body running center; inputting the real-time speed and the steering angle of the vehicle body running center into the AGV motion controller to control the actual pose of AGV through the AGV motion controller.

[0007] Based on the first aspect, in some embodiments, the pose deviation includes the position deviation of the vehicle body deviating from the track and the angle deviation of the vehicle body.

[0008] In the first aspect, in some embodiments, the scaling factor coefficient is calculated according to the real-time speed by speed grading inference, including: determining a domain of the real-time speed and a domain of the scaling factor coefficient; determining a plurality of preset levels of the real-time speed based on the domain of the real-time speed; determining a plurality of preset levels of the scaling factor coefficient based on the domain of the scaling factor coefficient; generating a first fuzzy rule table based on the plurality of preset levels of the real-time speed and the plurality of preset levels of the scaling factor coefficient; and calculating the scaling factor coefficient by the first fuzzy rule table and the real-time speed.

[0009] In the first aspect, in some embodiments, elements of the first fuzzy rule table include the real-time speed and the scaling factor coefficient; the number of the plurality of preset levels of the real-time speed is equal to the number of the plurality of preset levels of the scaling factor coefficient, and in the first fuzzy rule table, each preset level of the real-time speed corresponds to a preset level of the scaling factor coefficient.

[0010] In the first aspect, in some embodiments, the scaling factor coefficient is calculated by the first fuzzy rule table and the real-time speed, including: determining a preset level of the real-time speed in the first fuzzy rule table; determining a preset level of the scaling factor coefficient corresponding to the preset level of the real-time speed in the first fuzzy rule table according to the first fuzzy rule table; and calculating the scaling factor coefficient according to the preset level of the scaling factor coefficient.

[0011] In the first aspect, in some embodiments, the steering angle of the running center of the vehicle body is obtained by inputting the pose deviation into the fuzzy controller based on the scaling factor coefficient, including: determining a domain of the position deviation and a domain of the angle deviation; determining a plurality of preset levels of the position deviation based on the domain of the position deviation, and determining a plurality of preset levels of the angle deviation based on the domain of the angle deviation; determining a domain of the steering angle of the running center of the vehicle body and a plurality of preset levels of the steering angle of the running center of the vehicle body, wherein the plurality of preset levels of the steering angle of the running center of the vehicle body are determined based on the domain of the steering angle of the running center of the vehicle body; generating a second fuzzy rule table based on the plurality of preset levels of the position deviation, the plurality of preset levels of the angle deviation, and the plurality of preset levels of the steering angle of the running center of the vehicle body; and calculating the steering angle of the running center of the vehicle body by the second fuzzy rule table, the pose deviation, and the scaling factor coefficient.

[0012] In the first aspect, in some embodiments, elements of the second fuzzy rule table include the position deviation, the angle deviation, and the steering angle of the running center of the vehicle body; the horizontal axis and the vertical axis of the second fuzzy rule table are the plurality of preset levels of the position deviation and the plurality of preset levels of the angle deviation respectively, and each group of the plurality of preset levels of the position deviation and the plurality of preset levels of the angle deviation corresponds to a preset level of the steering angle of the running center of the vehicle body.

[0013] In the first aspect, in some embodiments, the turning angle of the vehicle body running center is calculated by the second fuzzy rule table, the pose deviation and the scaling factor coefficient, comprising: determining the preset level of the pose deviation in the second fuzzy rule table; determining the preset level of the turning angle of the vehicle body running center corresponding to the preset level of the pose deviation in the second fuzzy rule table according to the second fuzzy rule table; and multiplying the preset level of the turning angle of the vehicle body running center by the scaling factor coefficient to obtain the turning angle of the vehicle body running center.

[0014] In the second aspect, the embodiments of the present application provide a terminal device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the AGV deviation correction control method according to any one of the above first aspect when executing the computer program.

[0015] In the third aspect, the embodiments of the present application provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the AGV deviation correction control method according to any one of the above first aspect.

[0016] In the embodiments of the present application, a fuzzy deviation correction controller based on speed grading is designed, the output universe of discourse of the fuzzy deviation correction controller is changed in real time, the effectiveness and rapidity of the deviation correction control at high speed are improved, the pose deviation is corrected quickly and stably, and the deviation correction control effect of the AGV is improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 is a flowchart of the AGV deviation correction control method provided by the embodiments of the present application;

[0019] Figure 2 is a decomposition diagram of AGV left turn provided by the embodiments of the present application;

[0020] Figure 3 is a decomposition diagram of AGV right turn provided by the embodiments of the present application;

[0021] Figure 4 is a schematic diagram of the principle of fuzzy deviation correction control provided by the embodiments of the present application;

[0022] Figure 5 is a schematic diagram of the principle of speed grading control provided by the embodiments of the present application;

[0023] Figure 6 is a variable domain schematic diagram provided by an embodiment of the present application;

[0024] Figure 7 is a position deviation change curve provided by an embodiment of the present application;

[0025] Figure 8 is an angle deviation change curve provided by an embodiment of the present application;

[0026] Figure 9 is a schematic diagram of a terminal device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0027] The present application will be described in more detail by the specific embodiments. The following embodiments will help those skilled in the art to further understand the role of the present application, but do not limit the present application in any form. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application.

[0028] In order to make the purpose, technical scheme and advantages of the present application more clear, the following will be described by specific embodiments in conjunction with the drawings.

[0029] AGV automatic guide car is widely used in various industrial fields, including warehousing industry, manufacturing industry, dangerous places and special industries, etc. In many application fields, the correction control of AGV is the basis of realizing automatic guide control, and is also the core problem of AGV research. AGV correction control belongs to discrete control, which has the characteristics of nonlinearity, uncertainty of model and environment, and inaccuracy of measurement. Due to its special importance, scholars at home and abroad have carried out in-depth research on it.

[0030] The correction control of AGV in the prior art often uses fuzzy reasoning of position deviation and angle deviation of vehicle body. In this process, the fuzzy rule of deviation change is not considered. However, due to the influence of discrete time, when AGV runs at high speed, the problem that AGV is affected by discrete time and the control effect is low will occur, which affects the stable correction effect of AGV.

[0031] For the above problem, the present application provides an AGV correction control method, as shown in Figure 1 , comprising steps 101 to 105.

[0032] Step 101: obtaining the actual pose and real-time speed of AGV.

[0033] The AGV studied in this paper is a four-wheel structure, in which the driving wheel is a diagonal steering wheel, and the driving strategy is front steering wheel driving and guiding, and the rear steering wheel only drives. As shown in Figure 2As shown, the front and rear steering wheels of the trolley are shown when the left steering wheel is turned, the steering radius of the trolley body movement center. Among them, v c is the running speed of the moving trolley body center, v b is the rear steering wheel speed, v f is the front steering wheel speed, is the steering wheel rotation angle, is the steering angle of the trolley body, C is the geometric center point, r1 is the front wheel rotation radius, r2 is the C point rotation radius, and r3 is the rear wheel rotation radius.

[0034] The front steering wheel rotation angle of the trolley is The left rotation angle range of the front steering wheel is defined as The right rotation angle range is The steering angle of the trolley body is the angle between the speed direction of the movement center and the positive direction of the x-axis is defined The positive direction of the x-axis is positive, and the negative direction is negative. The attitude angle of the trolley body is the angle between the positive direction of the x-axis and the positive direction of the x-axis. The actual pose of the trolley body is the steering angle of the trolley body and the attitude angle of the trolley body.

[0035] As Figure 3 shown, taking the right turn of the trolley as an example, the AGV moves in a two-dimensional plane, and the reference coordinate system X-O-Y is established. Define the positive center C point of the trolley body as the trolley body movement center, and establish the x-C-y as the local coordinate system of the trolley with the C point as the origin. The front steering wheel rotates at a speed of v f , the steering wheel rotation angle When the rear steering wheel rotates to the right, the rear steering wheel speed v b , the steering angle of the trolley body The relationship between the speed of the trolley body movement center v c and the front steering wheel speed v f and the steering wheel rotation angle is shown as Figure 3 .

[0036] Ignoring the influence of ground friction, the steering radius of the AGV when turning left and right and the steering angle of the trolley body can be obtained according to the geometric relationship and :

[0037]

[0038]

[0039]

[0040] In the formula, the size of L is the length of the midpoint of the front wheel connecting line and the midpoint of the rear wheel connecting line, and the size of W is equal to the length of the front wheel or rear wheel connecting line.

[0041]

[0042] Since the angular velocities of the AGV are equal, the rear steering wheel speed and the center of motion speed of the vehicle body are according to the rotation radius and v f It can be obtained that:

[0043]

[0044]

[0045] Define the clockwise rotation of the AGV as negative and the counterclockwise rotation as positive, then the angular velocity of the AGV when it travels at a constant speed is:

[0046]

[0047] The real-time speed of the vehicle body includes the angular velocity ω c and the linear speed v f of the center of motion of the vehicle body.

[0048] The running state of the AGV can be represented as:

[0049] C=[X Y θ] T (8)

[0050]

[0051] Since the actual control of the AGV is discrete control, it is further discretized to obtain the pose of the trolley at time (k+1):

[0052]

[0053] In the formula, k is the sampling time, and ΔT is the sampling time interval.

[0054] Step 102: Calculate the pose deviation according to the actual pose and the ideal pose.

[0055] As shown in Figure 4 , the AGV deviation correction control method calculates the pose deviation according to the ideal pose and the actual pose of the AGV, the pose deviation includes the position deviation e d of the vehicle body from the track and the angle deviation e θ of the vehicle body, the domain stretching factor coefficient is inferred by speed grading, and then the steering angle of the AGV is inferred according to the pose deviation of the AGV, the vehicle body steering angle and the speed adjustment AGV rudder wheel speed and rotation angle, so that it can return to the original path with high speed and accuracy.

[0056] Step 103: Calculate the stretching factor coefficient by speed grading inference according to the real-time speed.

[0057] Due to the influence of discrete time, the correction control effect of AGV is low when AGV runs at high speed. In the variable universe fuzzy control of AGV based on speed grading, if the speed of AGV is large, the output universe of the steering angle of the running center of the AGV vehicle body is adjusted (i.e. the scaling factor of the fuzzy controller is adjusted), so as to reduce the influence of discrete time on the correction control of AGV. Figure 5 As shown in FIG. 17, based on the speed grading of the running AGV, the correction control of the AGV is performed.

[0058] In order to perform fuzzy control through the fuzzy rule table, the universe of the real-time speed and the scaling factor coefficient need to be determined first. Based on the universe of the real-time speed, a plurality of preset levels of the real-time speed are determined, and based on the universe of the scaling factor coefficient, a plurality of preset levels of the scaling factor coefficient are determined. Based on the plurality of preset levels of the real-time speed and the plurality of preset levels of the scaling factor coefficient, a first fuzzy rule table is generated.

[0059] The elements of the first fuzzy rule table include the real-time speed and the scaling factor coefficient. The number of the plurality of preset levels of the real-time speed is equal to the number of the plurality of preset levels of the scaling factor coefficient, and in the first fuzzy rule table, one preset level of the real-time speed corresponds to one preset level of the scaling factor coefficient.

[0060] In some embodiments, the running speed v c As the input variable, the scaling factor coefficient a of the universe of the steering angle of the running center of the vehicle body is taken as the output variable, and the input variable and the output variable are both subjected to fuzzy processing by adopting four quantization levels, and the fuzzy language set is divided into {ZE (moderate), PS (positive small), PM (positive medium), PB (positive large)} and {NB (negative large), NM (negative medium), NS (negative small), ZE (unchanged)}. The universe of the running speed of the AGV is set as (0.7 m / s, 1.5 m / s), and the universe of the scaling factor coefficient of the universe of the steering angle of the running center of the vehicle body is set as (0.3, 1). The fuzzy rule table is formulated, as shown in Table 1.

[0061]

[0062] The scaling factor coefficient is calculated by the first fuzzy rule table and the real-time speed. The preset level of the real-time speed in the first fuzzy rule table is determined. According to the first fuzzy rule table, the preset level of the scaling factor coefficient corresponding to the preset level of the real-time speed in the first fuzzy rule table is determined. According to the preset level of the scaling factor coefficient, the real-time speed is input into the membership function to calculate the scaling factor coefficient.

[0063] Step 104: based on the scaling factor coefficient, the pose deviation is input into the fuzzy controller to obtain the steering angle of the running center of the vehicle body.

[0064] The fuzzy controller selects the pose deviation of the AGV output, i.e., the position deviation and the angle deviation, as the input variable, and the steering angle of the AGV running center as the output variable. The scaling factor coefficient is used to adjust the output domain of the steering angle of the AGV running center.

[0065] The domain of the position deviation and the domain of the angle deviation are determined. Based on the domain of the position deviation, a plurality of preset levels of the position deviation are determined, and based on the domain of the angle deviation, a plurality of preset levels of the angle deviation are determined.

[0066] The domain of the steering angle of the vehicle body running center and a plurality of preset levels of the steering angle of the vehicle body running center are determined, wherein the values of the plurality of preset levels of the steering angle of the vehicle body running center are determined based on the domain of the steering angle of the vehicle body running center.

[0067] Based on the plurality of preset levels of the position deviation, the plurality of preset levels of the angle deviation, and the plurality of preset levels of the steering angle of the vehicle body running center, a second fuzzy rule table is generated.

[0068] The elements of the second fuzzy rule table include the position deviation, the angle deviation, and the steering angle of the vehicle body running center.

[0069] The horizontal axis and the vertical axis of the second fuzzy rule table are the plurality of preset levels of the position deviation and the plurality of preset levels of the angle deviation, respectively, and each group of the plurality of preset levels of the position deviation and the plurality of preset levels of the angle deviation corresponds to a preset level of the steering angle of the vehicle body running center.

[0070] In some embodiments, the input variable position deviation e d , the angle deviation e θ is quantized and processed by 7 quantization levels. is quantized and processed by 9 quantization levels. The domain value of e d is set to [-20 cm, 20 cm] (a negative deviation indicates that it is located on the left side of the running route, and a positive deviation indicates that it is located on the right side of the running surface), and the domain value of e θ is set to The domain initial value of e is set to [-40°, 40°]. The scaling factor of e

[0071] The fuzzy rule is established by taking the AGV position deviation and the angle deviation as parameters, and the fuzzy rule table is formulated according to the influence of the steering angle of the vehicle body on the pose, and Table 2 is shown below.

[0072] Table 2 Fuzzy rule table of the fuzzy correction controller

[0073]

[0074] The steering angle of the vehicle running center is calculated by the second fuzzy rule table, the pose deviation, and the scaling factor coefficient.

[0075] The pose deviation is determined in a preset level in the second fuzzy rule table. The preset level of the steering angle of the vehicle running center corresponding to the preset level of the pose deviation in the second fuzzy rule table is determined according to the second fuzzy rule table. The preset level of the steering angle of the vehicle running center is multiplied by the scaling factor coefficient to obtain the steering angle of the vehicle running center.

[0076] The size of the scaling factor coefficient of the universe of discourse of the steering angle of the vehicle running center is determined by the running speed of the AGV, so that the output universe of discourse of the steering angle of the vehicle running center changes with the size of the speed. When the running speed is low, the discrete time has a low impact on the correction control, and the fuzzy correction control of the initial universe of discourse can achieve effective correction. When the AGV running speed is large, the output universe of discourse is reduced to improve the correction performance of the AGV at high speed.

[0077] The fixed universe of discourse limits the adaptability of the fuzzy control. If the initial universe of discourse range is set too small, the control accuracy will decrease, and if the initial universe of discourse range is set too large, the control stability will decrease. The scaling factor is introduced to adjust the universe of discourse range of the input and output variables of the fuzzy controller. As shown in Figure 6 The variable universe of discourse increases the control rules and improves the quantization accuracy. The variable universe of discourse control with the scaling factor coefficient changes the output universe of discourse of the fuzzy correction controller from [-E, E] to [-aE, aE], and the universe of discourse of the steering angle of the vehicle running center is adaptively changed according to different speeds to obtain better control effect.

[0078] Step 105: input the real-time speed and the steering angle of the vehicle running center into the AGV motion controller, and control the actual pose of the AGV through the AGV motion controller.

[0079] In some embodiments, the AGV motion controller controls the actual pose of the AGV by adjusting the front steering wheel speed v f , the rear steering wheel speed v b , and the steering wheel rotation angle of the vehicle body.

[0080] The application designs and improves the fuzzy controller in deviation correction control according to the characteristics of AGV structure and motion. The fuzzy controller based on speed grading is proposed, that is, the speed is taken as the input variable, the output variable is the expansion factor of the output domain of the steering angle fuzzy controller, and the output domain expansion factor fuzzy controller is designed. The deviation correction simulation under various operating speeds verifies the effectiveness of the variable universe fuzzy controller based on speed grading, and realizes the fast and stable deviation correction of AGV.

[0081] Embodiment 1

[0082] In order to verify the effectiveness of the variable universe fuzzy controller based on speed grading, the AGV deviation correction control system is built in MATLAB / Simulink. First, the control effect of the variable universe fuzzy controller based on speed grading is simulated and analyzed, and the control effects of the traditional fuzzy control and the traditional PID control at different operating speeds are compared. The initial values of the AGV are set as shown in Table 3, and the simulation analysis is carried out, and the position deviation and angle deviation change curves at different speeds are obtained.

[0083] Table 3 AGV initial setting value

[0084] Angular deviation Positional deviation Running speed -pi / 20 10 cm 0.6 m / s -pi / 20 10 cm 1.1 m / s -pi / 20 10 cm 1.5 m / s

[0085] As shown in Figure 7 , Figure 8 , Figure 7 , (a) (b) (c) are the position deviation change curves at speeds v=0.6 m / s, v=1.1 m / s and v=1.5 m / s respectively, Figure 8 , (a) (b) (c) are the angle deviation change curves at speeds v=0.6 m / s, v=1.1 m / s and v=1.5 m / s respectively. It can be found from the analysis of the change curves that the variable universe fuzzy controller based on speed grading has more excellent performance in dynamic response time in deviation correction at different operating speeds compared with the traditional PID, and the overshoot is much lower than that of the traditional PID. When the AGV is not running at high speed, the variable universe fuzzy controller based on speed grading has the same control effect as the traditional fuzzy controller, and the performance is better than that of the traditional PID, which can realize the fast deviation correction of AGV.

[0086] When the AGV is running at high speed, the variable universe fuzzy controller based on speed grading has better control effect than the traditional fuzzy controller, which can greatly reduce the influence of system discrete time and achieve better deviation correction control effect. The effectiveness of the variable universe fuzzy controller based on speed grading is verified.

[0087] Figure 9 is a schematic diagram of a terminal device provided by an embodiment of the application. As shown in Figure 9As shown, the terminal device 9 of this embodiment includes a processor 90, a memory 91, and a computer program 92 stored in the memory 91 and executable on the processor 90, such as an AGV deviation correction control program. The processor 90 implements the steps in the above AGV deviation correction control method embodiments when executing the computer program 92, such as Figure 1 The steps 101 to 105 are shown.

[0088] For example, the computer program 92 can be divided into one or more modules / units, which are stored in the memory 91 and executed by the processor 90 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 92 in the terminal device 9.

[0089] The terminal device 9 can be a desktop computer, a notebook, a palm computer, a cloud server, and other computing devices. The terminal device can include, but is not limited to, the processor 90, the memory 91. Those skilled in the art can understand that, Figure 9 The terminal device 9 is only an example and does not constitute a limitation on the terminal device 9, which can include more or fewer components than shown, or combine certain components, or different components, for example, the terminal device can also include an input / output device, a network access device, a bus, etc.

[0090] The processor 90 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0091] The memory 91 can be an internal storage unit of the terminal device 9, such as a hard disk or a memory of the terminal device 9. The memory 91 can also be an external storage device of the terminal device 9, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the terminal device 9. Further, the memory 91 can also include both the internal storage unit and the external storage device of the terminal device 9. The memory 91 is used to store the computer program and other programs and data required by the terminal device. The memory 91 can also be used to temporarily store data that has been output or will be output.

[0092] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0093] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0094] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0095] In the embodiments of the present application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other manners. For example, the embodiments of the apparatus / terminal device described above are merely schematic, and the division of the modules or units is merely logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0096] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0097] In addition, each functional unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can be a physically independent unit, or two or more units can be integrated into a unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0098] The integrated module / unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the processor executes the computer program, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer readable medium can include appropriate contents according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0099] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those ordinarily skilled in the art should understand: the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An AGV deviation correction control method, characterized by, The method comprises: acquiring an actual pose and a real-time speed of an AGV; calculating a pose deviation according to the actual pose and an ideal pose; calculating a scaling factor coefficient according to the real-time speed; wherein, a plurality of preset levels of the real-time speed are determined based on a domain of the real-time speed, a plurality of preset levels of the scaling factor coefficient are determined based on a domain of the scaling factor coefficient; a first fuzzy rule table is generated based on the plurality of preset levels of the real-time speed and the plurality of preset levels of the scaling factor coefficient; elements of the first fuzzy rule table include the real-time speed and the scaling factor coefficient; the number of the plurality of preset levels of the real-time speed is equal to the number of the plurality of preset levels of the scaling factor coefficient, and in the first fuzzy rule table, one preset level of the real-time speed corresponds to one preset level of the scaling factor coefficient; wherein, the running speed of the vehicle body of the AGV is taken as an input quantity, and the scaling factor coefficient of the domain of the center steering angle of the vehicle body is taken as an output quantity; inputting the pose deviation into a fuzzy controller based on the scaling factor coefficient to obtain a center steering angle of the vehicle body; inputting the real-time speed and the center steering angle of the vehicle body into an AGV motion controller to control the actual pose of the AGV through the AGV motion controller; the pose deviation includes a position deviation of the current vehicle body from a track and an angle deviation of the vehicle body; the inputting of the pose deviation into the fuzzy controller based on the scaling factor coefficient to obtain the center steering angle of the vehicle body comprises: determining a domain of the position deviation and a domain of the angle deviation; determining a plurality of preset levels of the position deviation based on the domain of the position deviation and a plurality of preset levels of the angle deviation based on the domain of the angle deviation; determining a domain of the center steering angle of the vehicle body and a plurality of preset levels of the center steering angle of the vehicle body, wherein the values of the plurality of preset levels of the center steering angle of the vehicle body are determined based on the domain of the center steering angle of the vehicle body; generating a second fuzzy rule table based on the plurality of preset levels of the position deviation, the plurality of preset levels of the angle deviation and the plurality of preset levels of the center steering angle of the vehicle body; calculating the center steering angle of the vehicle body through the second fuzzy rule table, the pose deviation and the scaling factor coefficient.

2. The AGV deviation correction control method according to claim 1, wherein The calculating of the scaling factor coefficient according to the real-time speed comprises: determining a domain of the real-time speed and a domain of the scaling factor coefficient; determining a plurality of preset levels of the real-time speed based on the domain of the real-time speed; determining a plurality of preset levels of the scaling factor coefficient based on the domain of the scaling factor coefficient; generating a first fuzzy rule table based on the plurality of preset levels of the real-time speed and the plurality of preset levels of the scaling factor coefficient; calculating the scaling factor coefficient through the first fuzzy rule table and the real-time speed.

3. The AGV deviation correction control method according to claim 2, wherein Elements of the first fuzzy rule table include the real-time speed and the scaling factor coefficient. The number of preset levels of the real-time speed and the number of preset levels of the scaling factor coefficient are equal, and in the first fuzzy rule table, one preset level of the real-time speed corresponds to one preset level of the scaling factor coefficient.

4. The AGV deviation correction control method according to claim 3, wherein The calculation of the scaling factor coefficient through the first fuzzy rule table and the real-time speed comprises: determining the preset level of the real-time speed in the first fuzzy rule table; determining the preset level of the scaling factor coefficient corresponding to the preset level of the real-time speed in the first fuzzy rule table according to the first fuzzy rule table; calculating the scaling factor coefficient according to the preset level of the scaling factor coefficient.

5. The AGV deviation correction control method according to claim 1, wherein The elements of the second fuzzy rule table include the position deviation, the angle deviation and the steering angle of the vehicle body running center. The horizontal axis and the vertical axis of the second fuzzy rule table are a plurality of preset levels of the position deviation and a plurality of preset levels of the angle deviation respectively, and each group of the plurality of preset levels of the position deviation and the plurality of preset levels of the angle deviation corresponds to one preset level of the steering angle of the vehicle body running center.

6. The AGV deviation correction control method according to claim 5, wherein The calculation of the steering angle of the vehicle body running center through the second fuzzy rule table, the pose deviation and the scaling factor coefficient comprises: determining the preset level of the pose deviation in the second fuzzy rule table; determining the preset level of the steering angle of the vehicle body running center corresponding to the preset level of the pose deviation in the second fuzzy rule table according to the second fuzzy rule table; multiplying the preset level of the steering angle of the vehicle body running center by the scaling factor coefficient to obtain the steering angle of the vehicle body running center.

7. A terminal device comprising a memory and a processor and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the AGV deviation correction control method according to any one of claims 1 to 6.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7. The computer program is executed by the processor to realize the steps of the AGV deviation correction control method according to any one of claims 1 to 6.

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