Omni-directional mobile platform adaptive path tracking control method and system based on multi-mode switching

Abstract

The application discloses a kind of omnidirectional mobile carrying platform adaptive path tracking control method and system based on multi-mode switching, including establishing three-mode target steering kinematics model;Fuzzy reasoning rule base based on lateral motion intensity factor is constructed;According to the current platform longitudinal velocity, current path curvature and fuzzy reasoning rule base, determine the optimal rotation mode;Establish pre-aim error model and error dynamics equation, design integral sliding mode lateral controller, obtain the desired yaw angular velocity;According to the optimal rotation mode and the desired yaw angular velocity, obtain four-wheel target speed;Based on quintic polynomial, longitudinal velocity planning is carried out, and according to four-wheel target speed, path tracking control is carried out on omnidirectional mobile carrying platform.The application can effectively reduce lateral tracking error and heading angle error, significantly improve the tracking accuracy of omnidirectional mobile platform under variable curvature curve, cargo transportation stability and narrow space passability, and can be widely applied to carrying platform motion control technical field.

Description

Technical Field

[0001] This application relates to the field of motion control technology for transportation platforms, and in particular to an adaptive path tracking control method and system for an omnidirectional mobile transportation platform based on multi-mode switching. Background Technology

[0002] With the widespread application of omnidirectional mobile platforms in complex scenarios, Mecanum wheeled transport platforms, leveraging four-wheel independent drive and the principle of skew roller speed synthesis, can achieve three-degree-of-freedom omnidirectional motion in a plane. They possess the capabilities of in-situ spinning, lateral movement, diagonal travel, and turning at any center point, making them widely used in confined spaces such as warehousing and logistics, flexible production lines, and special operations. In actual operations, the platform often needs to transport goods along paths with varying curvature, completing complex maneuvers such as continuous curves, sharp turns with small radii, and high-speed smooth cornering, placing extremely high demands on path tracking accuracy, steering flexibility, cargo transport stability, and environmental adaptability.

[0003] However, existing path tracking control methods for omnidirectional mobile platforms suffer from problems such as a single and fixed steering mode, lack of adaptive decision-making based on operating conditions, poor robustness of lateral control, and disconnect between control commands and underlying execution. Summary of the Invention

[0004] The main objective of this application is to propose an adaptive path tracking control method and system for an omnidirectional mobile transport platform based on multi-mode switching, which can adaptively select the rotation center, has strong anti-disturbance capability, and can achieve precise wheel speed allocation in multiple modes.

[0005] To achieve the above objectives, one aspect of this application proposes an adaptive path tracking control method for an omnidirectional mobile transport platform based on multi-mode switching, the method comprising the following steps: Establish a three-mode target steering kinematic model for an omnidirectional mobile transport platform; Construct a fuzzy inference rule base based on lateral motion intensity factors; The current longitudinal speed and current path curvature of the omnidirectional mobile platform are obtained, and the optimal rotation mode is determined based on the current longitudinal speed, the current path curvature, and the fuzzy inference rule base. Establish a pre-aiming error model and an error dynamics equation, design an integral sliding mode lateral controller based on the pre-aiming error model and the error dynamics equation, and then obtain the desired yaw rate based on the integral sliding mode lateral controller; Based on the optimal rotation mode and the desired yaw rate, the target rotational speed of the four wheels is obtained; Based on the fifth-order polynomial, longitudinal speed planning is performed to obtain the desired longitudinal vehicle speed, and the omnidirectional mobile transport platform is path-tracking control is performed according to the target rotational speed of the four wheels.

[0006] In some embodiments, establishing the three-mode target steering kinematic model of the omnidirectional mobile transport platform specifically involves: A three-mode target steering kinematic model is established, including a rotation mode around the center of mass, a rotation mode around the rear axle center, and a rotation mode around the right rear wheel center.

[0007] In some embodiments, the construction of a fuzzy inference rule base based on lateral motion intensity factors specifically includes: Using the platform's longitudinal velocity and path curvature as input variables, the universe of discourse of the lateral motion intensity factor is divided into multiple fuzzy subsets; Define the membership function of each fuzzy subset, and construct the fuzzy inference rule base according to the fuzzy subset and the fuzzy label corresponding to each fuzzy subset; Based on the membership function, sub-region labels in the fuzzy inference rule base are determined, and the mapping rules between the sub-region labels and the three-mode target steering kinematic model are determined.

[0008] In some embodiments, determining the optimal rotation mode based on the current platform longitudinal velocity, the current path curvature, and the fuzzy inference rule base specifically includes: The current lateral motion intensity factor is calculated based on the current platform longitudinal velocity and the current path curvature. Determine the target fuzzy subset corresponding to the current lateral motion intensity factor; Calculate the input membership degree of the current lateral motion intensity factor in each of the fuzzy subsets based on the membership function; Based on the input membership degree and the target fuzzy subset, determine the target sub-region label corresponding to the current lateral motion intensity factor; Based on the input membership degree and the mapping rule, the target sub-region label is deblurred using the centroid method to obtain the optimal rotation mode.

[0009] In some embodiments, establishing the pre-aiming error model and error dynamics equation specifically includes: The reference path of the omnidirectional mobile transport platform is discretized to obtain the coordinates, tangent direction angle, and curvature of each path point; Calculate the lateral error from the reference path to the platform centroid based on the coordinates and the tangent direction angle; Based on the tangent direction angle, calculate the heading error between the reference path tangent direction and the platform heading; Based on the lateral error, the heading error, and the aiming distance, the aiming error model is established; The error dynamics equation is established based on the lateral error, the heading error, and the curvature, according to the vehicle kinematics relationship.

[0010] In some embodiments, the step of designing an integral sliding mode lateral controller based on the pre-aiming error model and the error dynamics equation specifically includes: The error dynamics equation is converted into standard state-space form; Establish a sliding surface function that includes a preview error term, a preview error differential term, and a preview error integral term; Based on the substitution of the sign function with the hyperbolic tangent function, and based on the sliding surface function, an improved multi-power reaching law is designed; Based on the error dynamics equation in standard state-space form and the improved multi-power approach law, the desired yaw rate control law is obtained. Based on the sliding surface function and the improved multi-power approach law, an adaptive law is designed. The stability of the sliding surface function, the improved multi-power approach law, the desired yaw rate control law, and the adaptive law are verified to obtain the integral sliding lateral controller.

[0011] In some embodiments, obtaining the target rotational speed of the four wheels based on the optimal rotation mode and the desired yaw rate specifically includes: Obtain the wheelbase, track width, and Mecanum wheel radius of the omnidirectional mobile transport platform; Based on the optimal rotation mode, the four-wheel rotation speeds are calculated according to the desired yaw rate, the wheelbase, the track width, and the Mecanum wheel radius to obtain the target rotation speeds of the four wheels.

[0012] In some embodiments, the longitudinal speed planning based on a fifth-order polynomial to obtain the desired longitudinal vehicle speed, and the path tracking control of the omnidirectional mobile transport platform based on the target rotational speeds of the four wheels, specifically includes: Establish a fifth-order polynomial based on the desired longitudinal vehicle speed and design boundary conditions; The desired longitudinal vehicle speed is obtained by solving the fifth-degree polynomial based on the boundary conditions. Using the target speed of the four wheels as the given command for the speed loop, the current speed of each wheel motor is collected, the speed error is calculated based on the current speed, and then the PI controller outputs the desired current command based on the speed error; The desired current command is tracked by a current loop, the current of each wheel motor is collected, the current error is calculated based on the current current, and then the motor torque output is adjusted by a PI controller based on the current error.

[0013] To achieve the above objectives, another aspect of this application proposes an adaptive path tracking control system for an omnidirectional mobile transport platform based on multi-mode switching, comprising: The three-mode motion model establishment module is used to establish a three-mode target steering kinematic model for an omnidirectional mobile transport platform. The fuzzy inference rule base construction module is used to build a fuzzy inference rule base based on the lateral motion intensity factor; The rotation mode determination module is used to obtain the current longitudinal speed and current path curvature of the omnidirectional mobile platform, and determine the optimal rotation mode based on the current longitudinal speed, the current path curvature and the fuzzy inference rule base. The yaw rate calculation module is used to establish a pre-aiming error model and an error dynamics equation, design an integral sliding mode lateral controller based on the pre-aiming error model and the error dynamics equation, and then obtain the desired yaw rate based on the integral sliding mode lateral controller. The four-wheel speed calculation module is used to obtain the target speed of the four wheels based on the optimal rotation mode and the desired yaw rate. The path tracking control module is used to perform longitudinal speed planning based on a fifth-order polynomial to obtain the desired longitudinal vehicle speed, and to perform path tracking control on the omnidirectional mobile transport platform according to the target rotational speed of the four wheels.

[0014] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0015] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0016] The embodiments of this application include at least the following beneficial effects: The adaptive path tracking control method and system for an omnidirectional mobile platform based on multi-mode switching of this application first establishes a three-mode target steering kinematic model of the omnidirectional mobile platform; constructs a fuzzy inference rule base based on lateral motion intensity factors; then obtains the current longitudinal velocity and current path curvature of the omnidirectional mobile platform, and determines the optimal rotation mode based on the current longitudinal velocity, current path curvature, and fuzzy inference rule base; then establishes a pre-aiming error model and error dynamic equation, designs an integral sliding mode lateral controller based on the pre-aiming error model and error dynamic equation, and obtains the desired yaw rate based on the integral sliding mode lateral controller; then obtains the target rotation speed of the four wheels based on the optimal rotation mode and the desired yaw rate; finally, performs longitudinal velocity planning based on a fifth-order polynomial to obtain the desired longitudinal vehicle speed, and performs path tracking control of the omnidirectional mobile platform based on the target rotation speed of the four wheels. This application can adaptively switch the rotation center mode according to changes in vehicle speed and curvature through fuzzy inference, effectively reducing lateral tracking error and heading angle error. Combined with integral sliding mode control and kinematic allocation, it significantly improves the tracking accuracy, cargo transportation stability and narrow space passage of the omnidirectional mobile platform under variable curvature curves, and achieves multi-mode precise wheel speed allocation. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments of this application are described below. It should be understood that the drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions in this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the steps of an adaptive path tracking control method for an omnidirectional mobile transport platform based on multi-mode switching, provided in one embodiment of this application. Figure 2 This is a flowchart illustrating the multi-mode switching decision logic of one embodiment of this application; Figure 3 This is a structural diagram of an integral sliding mode lateral controller provided in one embodiment of this application; Figure 4 This is a flowchart of multi-mode four-wheel speed calculation and closed-loop control provided in one embodiment of this application; Figure 5 A schematic diagram of the structure of an adaptive path tracking control system for an omnidirectional mobile transport platform based on multi-mode switching, provided in one embodiment of this application; Figure 6 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] With the widespread application of omnidirectional mobile platforms in complex scenarios, Mecanum wheeled transport platforms, leveraging four-wheel independent drive and the principle of skew roller speed synthesis, can achieve three-degree-of-freedom omnidirectional motion in a plane. They possess the capabilities of in-situ spinning, lateral movement, diagonal travel, and turning at any center point, making them widely used in confined spaces such as warehousing and logistics, flexible production lines, and special operations. In actual operations, the platform often needs to transport goods along paths with varying curvature, completing complex maneuvers such as continuous curves, sharp turns with small radii, and high-speed smooth cornering, placing extremely high demands on path tracking accuracy, steering flexibility, cargo transport stability, and environmental adaptability.

[0022] However, existing path tracking and control methods for omnidirectional mobile transport platforms have the following obvious drawbacks: 1. Single and fixed steering mode. Most solutions only adopt a single mode of rotation around the geometric center of the vehicle body, which cannot adapt to the different driving conditions required for smooth high-speed steering with small curvature, fast response with medium curvature, and extremely small radius steering with large curvature. The passability and flexibility are seriously insufficient in narrow curves and complex road conditions.

[0023] 2. Lack of adaptive decision-making based on operating conditions. An intelligent mapping mechanism between cargo transport speed, path curvature, and the optimal rotation center mode has not been established, making it impossible to dynamically switch steering strategies based on real-time road conditions and cargo transport status. This results in a mismatch between the control strategy and the operating conditions.

[0024] 3. Poor robustness of lateral control. Conventional PID control is weak in suppressing model errors, ground slippage, and load disturbances, and steady-state errors are difficult to eliminate; model predictive control has high computational complexity and is difficult to meet the requirements of embedded real-time control; traditional sliding mode control suffers from chattering, which affects motion smoothness and tracking accuracy.

[0025] 4. Control commands are disconnected from underlying execution. A complete calculation chain is not formed, which links the desired yaw rate to the multi-rotation center mode to the target speed of the four wheels. As a result, yaw control commands cannot be accurately converted into drive commands for each wheel, leading to large trajectory tracking errors, significant heading deviations, and unreasonable torque distribution between the left and right wheels.

[0026] In view of this, this application proposes an adaptive path tracking control method for an omnidirectional mobile platform based on multi-mode switching. First, a three-mode target steering kinematic model of the omnidirectional mobile platform is established; a fuzzy inference rule base based on lateral motion intensity factors is constructed; then, the current longitudinal velocity and current path curvature of the omnidirectional mobile platform are obtained, and the optimal rotation mode is determined based on the current longitudinal velocity, current path curvature, and the fuzzy inference rule base; next, a pre-aiming error model and error dynamics equations are established, and an integral sliding mode lateral controller is designed based on the pre-aiming error model and error dynamics equations, and the desired yaw rate is obtained based on the integral sliding mode lateral controller; then, the target rotation speeds of the four wheels are obtained based on the optimal rotation mode and the desired yaw rate; finally, longitudinal velocity planning is performed based on a fifth-order polynomial to obtain the desired longitudinal vehicle speed, and path tracking control of the omnidirectional mobile platform is performed based on the target rotation speeds of the four wheels. This application can adaptively switch the rotation center mode according to changes in vehicle speed and curvature through fuzzy inference, effectively reducing lateral tracking error and heading angle error. Combined with integral sliding mode control and kinematic allocation, it significantly improves the tracking accuracy, cargo transportation stability and narrow space passage of the omnidirectional mobile platform under variable curvature curves, and achieves multi-mode precise wheel speed allocation.

[0027] Reference Figure 1 , Figure 1 This is a flowchart illustrating the steps of an adaptive path tracking control method for an omnidirectional mobile platform based on multi-mode switching, according to an embodiment of this application. This application proposes an adaptive path tracking control method for an omnidirectional mobile platform based on multi-mode switching, which may include, but is not limited to, the following steps S101 to S106: Step S101: Establish a three-mode target steering kinematic model for an omnidirectional mobile transport platform; As an optional implementation, step S101 can be further divided into the following steps S1011: Step S1011: Establish a three-mode target steering kinematic model that includes a rotation mode around the center of mass, a rotation mode around the rear axle center, and a rotation mode around the right rear wheel center.

[0028] For example, three target steering rotation center modes are defined to cover steering requirements under all working conditions, including: Rotation around the center of mass mode (Mode a): The platform rotates clockwise around the geometric center of the vehicle body, with symmetrical wheel speeds and evenly distributed yaw torque. This mode is suitable for roads with small curvatures or high-speed cruising conditions, ensuring optimal smoothness of cargo transportation and energy utilization efficiency.

[0029] Rotation around the rear axle center (Mode b): The platform rotates clockwise around the center point of the rear wheel axle, while the rear wheel contact trajectory remains unchanged. The front wheels achieve steering through differential drive. This mode is suitable for medium-curvature curves, improving steering response sensitivity and allowing the platform trajectory to better conform to changes in path curvature.

[0030] Rotation mode around the center of the right rear wheel (Mode c): The platform rotates clockwise around the center point of contact with the right rear wheel, with the instantaneous linear velocity of the right rear wheel being zero, thus becoming the pivot point of rotation. This mode achieves the minimum right-hand rotation radius of the platform, making it suitable for large-curvature bends or right-angle turns in narrow spaces, significantly improving passability.

[0031] It is understandable that the above definition is based on the right-hand drive condition. In the left-hand drive condition, the rotation centers of each mode are symmetrically distributed, that is, mode c corresponds to the rotation around the center point of the left rear wheel ground contact. The wheel speed distribution is adjusted accordingly according to the principle of symmetry. The specific control process is the same as that of the right-hand drive condition.

[0032] Step S102: Construct a fuzzy inference rule base based on lateral motion intensity factor; Specifically, lateral motion intensity factor It is a core indicator for measuring the lateral stability of an omnidirectional mobile transport platform during cargo transportation under different curvature paths, comprehensively reflecting the coupled influence of the platform's longitudinal velocity and path curvature on lateral stability. Compared to ordinary vehicles, Mecanum wheel platforms, due to their skewed roller structure, have complex tire contact characteristics and are more sensitive to lateral forces; therefore, the lateral motion intensity factor is used. As a mediating variable for mode switching, it has stronger engineering adaptability.

[0033] As an optional implementation, step S102 can be further divided into the following steps S1021 to S1023: Step S1021: Using the platform's longitudinal velocity and path curvature as input variables, divide the universe of discourse of the lateral motion intensity factor into multiple fuzzy subsets; In some optional embodiments, a lateral motion intensity factor is introduced. This coefficient serves as the mapping parameter for the fuzzy decision-making module. It is determined by the platform's real-time speed and the curvature of the path ahead, and is calculated using the following formula: ; in, The longitudinal speed of the vehicle on the platform is expressed in m / s. The geometric curvature of the reference trajectory, in meters. -1 ; This is the acceleration due to gravity, in units of 9.8 m / s². 2 .

[0034] With platform longitudinal speed and reference path curvature The input variables are all fuzzy sets: {S (small), M (medium), B (large)}, which provide clear physical boundaries for subsequent mode switching.

[0035] Lateral motion intensity factor The universe of discourse is divided into three fuzzy subsets, as shown in the following example: S (small) fuzzy subset: corresponds to straight-line cargo transportation or gentle curve conditions. The lateral force is small in this interval, the cargo sway is negligible, the platform attitude is stable, and mode a (rotation mode around the center of mass) is applicable. The control strategy prioritizes smoothness and cargo safety.

[0036] M (Medium) Fuzzy Subset: Corresponds to medium curvature curves or medium-to-high speed cornering conditions. In this range, lateral forces can be clearly perceived, and the cargo tends to sway slightly. Applicable mode b (rotation mode around the rear axle center) is used. The control strategy ensures tracking accuracy while also taking into account the stability of cargo transportation.

[0037] B (Large) Fuzzy Subset: For emergency curves or high-speed cornering conditions. The lateral force is large in this section, and there is a risk of cargo sliding or overturning. Mode c (rotation around the right rear wheel mode) is applicable, and the control strategy prioritizes cargo anti-slip and anti-overturning.

[0038] Step S1022: Define the membership function of each fuzzy subset, and construct a fuzzy inference rule base based on the fuzzy subsets and the fuzzy labels corresponding to each fuzzy subset; Step S1023: Determine the sub-region labels in the fuzzy inference rule base according to the membership function, and determine the mapping rules between the sub-region labels and the three-mode target turning kinematic model.

[0039] Specifically, to ensure the continuity of the mode switching process and prevent lateral motion intensity factors... Fluctuations near the sub-region boundary cause frequent changes in control commands. This application's embodiment uses a triangular function to adjust the lateral motion intensity factor. Perform fuzzy mapping.

[0040] In some optional embodiments, the membership function of each fuzzy subset is defined as follows: S (small) membership function: ; in, , as well as These represent the lower boundary, upper boundary, and membership peak of the subset S, respectively.

[0041] M (Membership function): ; in, , as well as These represent the lower boundary, upper boundary, and membership peak of the subset M, respectively.

[0042] B (large) membership function: ; in, , as well as These represent the lower boundary, upper boundary, and membership peak of subset B, respectively.

[0043] Next, a fuzzy rule base was developed. This was based on the basic input variable (vehicle speed). curvature The three levels of fuzzy labels are used to construct a 3×3 inference rule matrix, which aims to demonstrate the control logic based on the lateral motion intensity factor. Compared with exhaustive logic judgment, the rule table is more convenient and easier to adjust and expand according to the actual platform characteristics. Each element in the matrix represents the fuzzy inference output. Sub-region labels (S / M / B) are represented by rules as shown in Table 1 below.

[0044]

[0045] The rules are explained as follows: When the curvature is S (small curvature), the output is S (small lateral motion intensity factor) when the speed is S, S (small lateral motion intensity factor) when the speed is M, and M (medium lateral motion intensity factor) when the speed is B. When the curvature is M (medium curvature), the output is S (small lateral motion intensity factor) when the speed is S, M (medium lateral motion intensity factor) when the speed is M, and B (large lateral motion intensity factor) when the speed is B. When the curvature is B (large curvature), the output is S (small lateral motion intensity factor) when the speed is S, M (medium lateral motion intensity factor) when the speed is M, and B (large lateral motion intensity factor) when the speed is B.

[0046] Obtained by fuzzy reasoning The mapping rules between sub-region labels (S / M / B) and each mode in the three-mode target steering kinematic model are as follows: If the output is S ( If the value is relatively small, the platform executes mode a (rotation mode around the center of mass). In this mode, the four wheels rotate at equal speeds but in opposite directions, allowing the platform to smoothly navigate gentle curves and maximizing the stability of cargo transportation. The rationale for this setting is that lateral acceleration is negligible under small curvature conditions, lateral force is minimal at low and medium speeds (output S), and lateral force increases slightly at high speeds (output M). Therefore, mode a is used when outputting S to prioritize smoothness and cargo safety.

[0047] If the output is M ( If the value is medium, the platform switches to mode b (rotation mode around the rear axle center). The rear wheels maintain low speed, and the front wheels perform differential steering to suppress the cumulative amplification of trajectory deviation, balancing tracking accuracy and cargo safety. The rationale for this setting is that under medium curvature conditions, the lateral force increases with speed, with low speed output S, medium speed output M, and high speed output B. Therefore, mode b is used when outputting M to achieve a balance between tracking accuracy and cargo stability.

[0048] If the output is B ( If the value is large, the platform will be forced into mode c (rotation mode around the center of the right rear wheel). The other three wheels, excluding the right rear wheel, will drive through the sharp bend with the minimum turning radius to prevent the cargo from sliding or overturning. The rationale for this setting is that the lateral force is significant on sharp bends under high curvature conditions. Low speed output is M, and medium to high speed output is B. Therefore, when outputting B, mode c is used to trigger the limit mode, maximizing the safety of cargo transportation.

[0049] Step S103: Obtain the current longitudinal speed and current path curvature of the omnidirectional mobile platform; determine the optimal rotation mode based on the current longitudinal speed, current path curvature, and fuzzy inference rule base. As an optional implementation, the step of determining the optimal rotation mode based on the current platform longitudinal speed, the current path curvature, and the fuzzy inference rule base can be further divided into the following steps S1031 to S1035: Step S1031: Calculate the current lateral motion intensity factor based on the current platform longitudinal velocity and the current path curvature; Step S1032: Determine the target fuzzy subset corresponding to the current lateral motion intensity factor; Step S1033: Calculate the input membership degree of the current lateral motion intensity factor in each fuzzy subset according to the membership function; Step S1034: Determine the target sub-region label corresponding to the current lateral motion intensity factor based on the input membership degree and target fuzzy subset; Step S1035: Based on the input membership degree and mapping rules, perform centroid deblurring on the target sub-region labels to obtain the optimal rotation mode.

[0050] It should be noted that the embodiments of this application employ a Mamdani-type inference mechanism, which uses the centroid method to convert fuzzy outputs into deterministic mode instructions. Mamdani-type inference is the most commonly used fuzzy inference method in engineering, and it can effectively handle decision problems with multiple inputs and single outputs. The centroid method defuzzification obtains the mode quantization value by weighted averaging of the activation intensity of each rule. After continuous output changes, the value is rounded to the nearest integer, achieving controlled hysteresis switching in the mode boundary region, further suppressing mode jumps.

[0051] Specifically, such as Figure 2 The diagram shows the multi-mode switching decision logic flowchart. First, based on the current platform longitudinal velocity and the current path curvature, the current... Value, then based on the current Calculate its membership degree in the three sub-regions; then activate the corresponding rules according to the aforementioned rule table; finally, perform centroid defuzzification using the following formula: ; in, , , These correspond to the quantization values ​​for modes a, b, and c, respectively.

[0052] The final output mode command is rounded to the nearest integer: 1 corresponds to mode a, 2 to mode b, and 3 to mode c. Equivalently, the mode corresponding to the sub-region with the highest membership degree can be directly selected as the output command, and the final output satisfies: .

[0053] Step S104: Establish the aiming error model and error dynamics equation, design the integral sliding mode lateral controller based on the aiming error model and error dynamics equation, and then obtain the desired yaw rate based on the integral sliding mode lateral controller. As an optional implementation, the step of establishing the aiming error model and error dynamics equation can be further divided into the following steps S1041 to S1045: Step S1041: Discretize the reference path of the omnidirectional mobile platform to obtain the coordinates, tangent direction angle and curvature of each path point; Specifically, the reference path is discretized into a series of path points, each path point containing the following information: coordinates. Tangent direction angle curvature The pose of the omnidirectional mobile platform is represented as follows: ; in, The coordinates of the platform's geometric center in the global coordinate system. This refers to the platform's heading angle.

[0054] Step S1042: Calculate the lateral error from the reference path to the platform centroid based on the coordinates and tangent direction angle; Step S1043: Calculate the heading error between the tangent direction of the reference path and the platform heading based on the tangent direction angle; Specifically, define the lateral error. Normal distance from the platform's centroid to the reference path: ; Define heading error The deviation between the platform's heading and the tangent direction of the reference path: .

[0055] Step S1044: Based on the lateral error, heading error, and aiming distance, establish an aiming error model; Specifically, to improve the forward-looking capability of path tracking, a pre-aiming error quantity that integrates lateral deviation and heading deviation is defined. : ; in, For the pre-aiming distance, In the formula Pre-aiming distance as the base value This is a speed coefficient; the higher the vehicle speed, the greater the aiming distance. When the size is large, the controller focuses more on look-ahead path tracking; when When the error is small, the controller focuses more on quickly eliminating the current position error. For lateral error, This represents the heading error.

[0056] Step S1045: Based on the lateral error, heading error, and curvature, establish the error dynamics equation based on the vehicle kinematics relationship.

[0057] Specifically, based on the vehicle's kinematics, the error dynamics equation is derived as follows: ; ; ; in, The first derivative of the aiming error represents the rate of change of the aiming tracking error over time, which incorporates lateral deviation and heading deviation. The first derivative of the heading error represents the rate of change of the deviation between the platform's heading angle and the tangent direction angle of the reference path over time. The first derivative of the lateral error represents the rate of change of the normal distance from the platform's centroid to the reference path over time. This refers to the platform's actual yaw rate. This represents the rate of change of the heading angle of the reference path.

[0058] As an optional implementation, the step of designing an integral sliding mode lateral controller based on the pre-aiming error model and the error dynamics equation can be further divided into the following steps S1046 to S10411: Step S1046: Convert the error dynamics equation into standard state-space form; Specifically, such as Figure 3 The diagram shown is a structural diagram of the integral sliding mode transverse controller. To facilitate the subsequent design of the integral sliding controller, the above error dynamics are organized into a standard state-space form: ; in, For nonlinear functions containing model feedforward terms, This is the function to control the input gain. To integrate the aiming errors of lateral deviation and heading deviation, Let be the first derivative of the aiming error, representing the rate of change of the aiming error over time; Let be the second derivative of the aiming error, and let represent the acceleration of the aiming error.

[0059] Step S1047: Establish a sliding surface function that includes a pre-aiming error term, a pre-aiming error differential term, and a pre-aiming error integral term; Specifically, to eliminate steady-state tracking errors and improve system robustness, embodiments of this application design a sliding surface that includes error, error derivative, and error integral terms: ; in, , as well as For control parameters and , To mitigate aiming error, an integral term is introduced to eliminate steady-state tracking error and improve system robustness. To prevent integral saturation from degrading control performance, a limit is set on the integral term. ,in This is the integral limit value.

[0060] Step S1048: Replace the sign function with the hyperbolic tangent function, and design an improved multi-power reaching law based on the sliding surface function; Specifically, to balance convergence speed and chattering suppression, this application's embodiments use a hyperbolic tangent function instead of the traditional sign function, and design the following multi-power exponential reaching law: ; in, This is an estimate of the reaching law gain. For fixed gain, It is a power exponent and , Smoothing factor and , The hyperbolic tangent function is used to replace the sign function to suppress chattering. For the integral sliding surface function, It is the first derivative of the sliding surface function.

[0061] Step S1049: Based on the error dynamics equation in standard state space form and the improved multi-power approach law, the desired yaw rate control law is obtained. Specifically, by substituting the error dynamics into the sliding surface derivative, the desired yaw rate control law is obtained: ; in, For the model feedforward compensation term, To control the input gain function, This is the model feedforward compensation term; This is an error feedback control term; This is a sliding mode robust control term.

[0062] We obtain a unified desired target steering angular velocity that can be directly used for multi-mode allocation. .

[0063] Step S10410: Design an adaptive law based on the sliding surface function and the improved multi-power approach law; Specifically, to avoid difficulties in parameter tuning, an adaptive law is designed for online estimation of the reaching law parameters. : ; in, For adaptive gain and , for The estimated value. Adaptive gain. The selection of [a specific component] must satisfy the Lyapunov stability condition. According to the stability proof in subsequent step S10411, [the following steps are required]. It should be greater than the square of the upper bound of the external disturbance to ensure the asymptotic convergence of the parameter estimation error.

[0064] Step S10411: The stability of the sliding surface function, the improved multi-power approach law, the desired yaw rate control law, and the adaptive law are verified to obtain the integral sliding lateral controller.

[0065] Specifically, for the error dynamics system, under the action of the sliding surface (S1047), the reaching law (S1048), the control law (S1049), and the adaptive law (S10410), the system state can asymptotically converge to the sliding surface. And all signals are consistent and eventually bounded.

[0066] Choose the following Lyapunov function: ; in, For parameter estimation error, for ideal value and .

[0067] right Differentiate: ; Substituting the sliding surface derivative and the control law into the first term, and the adaptive law into the second term, and then simplifying: ; because , ,and , ,therefore .

[0068] If and only if hour According to Lyapunov's stability theory and Lassalle's invariant set principle, the system state asymptotically converges to the sliding surface. ,Right now , The tracking error asymptotically converges to zero.

[0069] Step S105: Obtain the target rotation speed of the four wheels based on the optimal rotation mode and the desired yaw rate; As an optional implementation, step S105 can be further divided into the following steps S1051 to S1052: Step S1051: Obtain the wheelbase, track width, and Mecanum wheel radius of the omnidirectional mobile transport platform; Step S1052: Based on the optimal rotation mode, calculate the four-wheel speeds according to the desired yaw rate, wheelbase, track width, and Mecanum wheel radius to obtain the target speeds of the four wheels.

[0070] Specifically, such as Figure 4 The diagram shows the multi-mode four-wheel speed calculation and closed-loop control flowchart, which calculates the desired yaw rate output from step S104. The calculation is translated into independent speed commands for all four wheels, enabling the macro-control commands to be accurately executed by the underlying drive system. The wheel speeds satisfy: ; in, The desired yaw rate is the output. Let the radius of each wheel be the radius around the center of rotation. Let be the radius of the Mecanum wheel.

[0071] definition Half wheelbase (half the distance between the front and rear axles) The track width is half the distance between the left and right wheels. Therefore, the rotational speeds of the four wheels in each rotation center pattern are: In mode a, the rotation radius of each wheel is... The rotational speeds of each wheel are: ; ; Mode b, front wheel turning radius Rear wheel rotation radius The rotational speeds of each wheel are: ; ; Mode C, left front wheel radius Right front wheel radius left rear wheel radius The radius of the right rear wheel is 0, and the rotational speed of each wheel is: ; .

[0072] Step S106: Perform longitudinal speed planning based on a fifth-order polynomial to obtain the desired longitudinal vehicle speed, and perform path tracking control on the omnidirectional mobile transport platform according to the target rotation speed of the four wheels.

[0073] As an optional implementation, step S106 can be further divided into the following steps S1061 to S1064: Step S1061: Establish a fifth-order polynomial based on the desired longitudinal vehicle speed and design boundary conditions; Step S1062: Solve the fifth-degree polynomial according to the boundary conditions to obtain the desired longitudinal vehicle speed; Specifically, the desired longitudinal speed is pre-planned based on the reference path curvature and platform safety constraints. To eliminate the impact of sudden velocity changes on vehicle attitude, a fifth-order polynomial is used for time-domain smoothing programming of the desired longitudinal velocity. Given the boundary conditions such as velocity and acceleration at the start and end times, the coefficients of the fifth-order polynomial are solved: ; The planned desired vehicle speed and the four-wheel speed commands calculated by S5 are sent to the underlying drive system.

[0074] Step S1063: Using the target speed of the four wheels as the given command for the speed loop, the current speed of each wheel motor is collected, the speed error is calculated based on the current speed, and then the expected current command is output through the PI controller based on the speed error. Step S1064: Track the desired current command through the current loop, collect the current of each wheel motor, calculate the current error based on the current current, and then adjust the motor torque output according to the current error through the PI controller.

[0075] Specifically, the underlying driver adopts a series dual-closed-loop architecture of "speed closed loop - current closed loop": Outer loop (speed loop): Taking the target speed of the four wheels calculated in step S105 as the given command, the actual speed of the motor is collected by Hall sensor or encoder, and the desired current command is output by PI controller.

[0076] Inner loop (current loop): Tracks the current command output by the speed loop, collects the actual current of the motor through a current sensor, and adjusts the motor torque output through a high-bandwidth PI controller.

[0077] The dual closed-loop structure effectively suppresses nonlinear interference factors such as motor dead zone, wheel machining error, ground friction variation, and load fluctuation. Through the complete control link of the above mode decision (step S103) - yaw control (step S104) - kinematic allocation (step S105) - low-level execution (step S106), the embodiment of this application realizes the precise decoupling and synchronous tracking of the four-wheel speed in the multi-rotation center mode, ensuring the accurate realization of the kinematic model from the physical execution level, and finally achieving high-precision trajectory tracking under the variable curvature path.

[0078] The adaptive path tracking control method for an omnidirectional mobile transport platform based on multi-mode switching, as described in this application, will be explained in detail below with specific examples.

[0079] The adaptive path tracking control method for an omnidirectional mobile transport platform based on multi-mode switching in this application includes the following steps: Step S101, Specific implementation of the three-mode target steering kinematic model: Mode A – Turning around the vehicle's geometric center: Mode A is the default mode for conventional cargo transportation. In this mode, the platform rotates clockwise around its geometric center (center of mass), with the four speeds symmetrically distributed. Specifically, the two left wheels (left front FL, left rear RL) move forward, while the two right wheels (right front FR, right rear RR) move backward, and the speed amplitudes of the front and rear wheels on the same side are equal. This mode generates a uniform yaw moment, suitable for smooth cargo transportation conditions with small curvature at high speeds, maximizing cargo transportation stability and load safety.

[0080] Mode b – Steering around the rear axle center: Mode b is suitable for medium curvature conditions. In this mode, the center of rotation is located at the center point of the rear axle, the rear wheel trajectory remains fixed, and the front wheels achieve steering through differential. Specifically, the left front wheel (FL) moves forward, the right front wheel (FR) moves backward, and the rear wheel speed amplitude is relatively small. This mode can improve steering response speed and trajectory conformity, and is suitable for medium-speed, medium-curvature curve cargo transportation.

[0081] Mode C – Steering around the right rear wheel's contact point: Mode C is suitable for low-speed, rapid turns with high curvature. In this mode, the center of rotation is the right rear wheel's contact point. The right rear wheel (RR) remains stationary, while the other three wheels rotate around it: the left front wheel (FL) moves at maximum linear speed, and the right front wheel (FR) and left rear wheel (RL) move at medium linear speed. This mode achieves the platform's minimum right-hand turn radius, significantly improving maneuverability in tight spaces.

[0082] In the left-hand rotation mode, the rotation centers of each mode are symmetrically distributed, and the wheel speed distribution is adjusted accordingly according to the principle of symmetry. The specific control process is the same as that in the right-hand rotation mode.

[0083] Step S102: Specific design and parameter configuration of the fuzzy inference system; Step 1: Define the universe of discourse and membership function for the input variables: In this example, the platform's longitudinal speed The universe of discourse is set to [0, 10] m / s, and is divided into the following three fuzzy subsets: S (small): Triangle membership function, with the center point taking [0,2,4], corresponding to low-speed fine operation conditions; M (Medium): Triangle membership function, with the center point taking [3,5,7], corresponding to medium-speed conventional tracking conditions; B (large): Triangle membership function, with the center point set to [6,8,10], corresponding to high-speed and rapid movement conditions.

[0084] Road curvature The universe of discourse is set to [0, 0.2]m. -1 It is divided into the following three fuzzy subsets: S (small): Membership function of triangle, with the center point taking the values ​​[0, 0.05, 0.10], corresponding to a straight line or a large-radius gentle curve; M (middle): Membership function of the triangle, with the center point set to [0.06, 0.1, 0.14], corresponding to a regular curve; B (large): Membership function of triangle, with the center point set to [0.12, 0.16, 0.2], for emergency bends or spiral bends.

[0085] Step 2: Divide the fuzzy subset of the lateral motion intensity factor μ: Lateral motion intensity factor The domain of discourse is divided into the following three fuzzy subsets: S (small) fuzzy subset: The range of values ​​is This is suitable for straight-line cargo transportation or gentle curves. Lateral forces are small within this range, cargo swaying is negligible, and the platform attitude is stable. Mode a (rotation around the center of mass) is applicable, with the control strategy prioritizing smoothness and cargo safety. M (middle) fuzzy subset: The range of values ​​is This is suitable for medium-curvature curves or medium-to-high-speed cornering conditions. The lateral force can be clearly felt in this range, and the cargo tends to sway slightly. Mode b (rotation mode around the rear axle center) is applicable. The control strategy ensures tracking accuracy while also taking into account the stability of cargo transportation. B (large) fuzzy subset: The range of values ​​is For emergency curves or high-speed cornering situations, where lateral forces are significant and cargo is at risk of sliding or overturning, mode c (rotation around the right rear wheel) is applicable, with the control strategy prioritizing cargo anti-slip and anti-overturning.

[0086] The above boundaries are determined based on the principle of graded control of lateral acceleration in vehicle dynamics. 0.20g is the upper limit of the linear zone, and 0.55g is the threshold for cargo overturning risk, taking into account both the lateral sensitivity of the Mecanum wheel skew roller structure and the safety requirements of the transported cargo.

[0087] Step 3: Design the membership function: The input variables are fuzzified using a triangular membership function.

[0088] Velocity membership function: S (small): ; M (middle): ; B (Big): ; Curvature membership function: S (small): ; M (middle): ; B (Big): ; Step 4: Construct a fuzzy rule table: Based on the basic input variable (vehicle speed) curvature Based on the three levels of fuzzy labels, a 3×3 inference rule matrix is ​​defined as follows, where each element in the matrix represents the output of the fuzzy inference. Sub-region labels (S / M / B):

[0089] Step 5: Verify the output mapping: For each rule combination, calculate the typical value. Verify the correctness of the output:

[0090] All rule combinations The subset output is completely consistent with the rule table, indicating that the fuzzy inference system is designed correctly.

[0091] Step S104: Perform a complete working condition example calculation: Take a typical medium-speed, medium-curve working condition: , .

[0092] Step 1: Calculate the lateral motion intensity factor : ; It belongs to the fuzzy subset of M.

[0093] Step 2: Calculate the input membership degree: Speed : , , ; curvature : , , .

[0094] Step 3, Rule Activation: Rule 5 ( ): Activation strength The output is M. The activation strength of the remaining rules is 0.

[0095] Step 4: Deblurring: ; Rounding to the nearest integer , corresponding to mode b (rotation mode around the rear axis center).

[0096] Result verification: It belongs to sub-region M, the rule table output is M, and the deblurring result is pattern b.

[0097] Step S104, Detailed calculation process of the path tracking error model: Step 1, Discretize the reference path: The reference path is spaced at equal arc length intervals. Discretize into a series of path points. Each path point contains coordinates. Tangent direction angle curvature and cumulative arc length .

[0098] Step 2, Nearest Point Search and Error Calculation: Based on the platform's current pose in each control cycle A nearest neighbor search algorithm based on arc length index is used to find the point on the reference path that is closest to the platform's centroid. And obtain the tangent direction angle at that point. With curvature .

[0099] lateral error Calculate using the following formula: ; Heading error Calculate using the following formula: .

[0100] Step 3, Pre-aiming error calculation: The aiming distance L is a known quantity and is adaptively adjusted according to vehicle speed. In this example, we take... The unit is meters. The path tracking error is minimized when the aiming distance coefficient is in the range of 0.3-0.8. This example uses the median value of 0.5, which is based on a combined tuning of a platform wheelbase of 0.5m and a response delay of 0.02s.

[0101] Pre-aiming error The calculation formula is: ; Linearize the error dynamics. Under the small angle assumption... Linearizing the error dynamics yields: ; ; ; Step 4, Slipform Surface Design: In this example, the sliding surface parameters are: , , : ; In this example, the integral limit value is taken as... The integration process stops when the absolute value of the integral term exceeds 0.5.

[0102] Step 5: Improved multi-power reaching law design: The convergence law parameters are determined based on the convergence condition of the multi-power convergence law, and are set as follows: Smoothing factor: Fixed gain: Power exponent: ; Initial value of the reaching law gain: ; The expression for the approach law is: ; in, Ensure that the reaching law has good smoothing properties when the error is large.

[0103] Step 6, Desired yaw rate control law: Substituting the error dynamics into the sliding surface derivative, the desired yaw rate control law is obtained: ; In engineering applications, A simplified form can be adopted. At this point, the control law simplifies to: ; The control law in each control cycle Perform one calculation within the time limit and output the desired yaw rate. The unit is rad / s.

[0104] Step 7, Adaptive Law Design: To avoid difficulties in parameter tuning, an adaptive law is designed for online estimation of the reaching law parameters. : ; Among them, adaptive gain This is to ensure convergence speed and stability. for The estimated value can guarantee that the parameter estimation error asymptotically converges.

[0105] Step 8, Proof of Stability: The integral sliding surface design employed in this controller ensures the steady-state tracking accuracy of the system even with model uncertainties and external disturbances. The error integral term included in the sliding surface effectively eliminates the steady-state error present in traditional sliding mode control. Simultaneously, the adaptive law online estimates the reaching law gain, avoiding the difficulties of manual parameter tuning, and the introduction of the hyperbolic tangent function ensures the smoothness and continuity of the control output.

[0106] Step S105, Detailed calculation of multi-mode four-wheel speed distribution: Step 1, Vehicle geometric parameters: In this example, the geometric parameters of the omnidirectional mobile platform are as follows: Wheelbase (distance between front and rear axles) That is, half wheelbase ; Wheelbase (distance between left and right wheels) That is, half wheel track Mecanum wheel radius .

[0107] Step 2, Calculation of rotational speed in mode a: When the fuzzy inference rule base outputs pattern a, the four-wheel speed is calculated as follows: Front left wheel: ; Right front wheel: ; Left rear wheel: ; Right rear wheel: .

[0108] Step 3, Calculation of rotational speed in mode b: When the fuzzy inference rule base outputs pattern b, the four-wheel speed is calculated as follows: Front left wheel: ; Right front wheel: ; Left rear wheel: ; Right rear wheel: .

[0109] Step 4, Calculation of rotational speed in mode c: When the fuzzy inference rule base outputs pattern C, the four-wheel speed is calculated as follows: Front left wheel: ; Right front wheel: ; Left rear wheel: ; Right rear wheel: .

[0110] Step S106, Low-level motion control execution strategy: Step 1: Vertical velocity quintic polynomial programming: Desired longitudinal velocity Time-domain smoothing programming is performed using a fifth-order polynomial. Let the starting time be... initial speed Initial acceleration Termination time Termination speed Termination acceleration Termination of jerk Solving for the coefficients : ; Substitute the boundary conditions: ; Solving for the given information, we get: , , The speed curve is The transition is smooth with no abrupt changes.

[0111] Step 2: Four-wheel independent dual closed-loop tracking control: The underlying drive system adopts a dual closed-loop control architecture consisting of a speed closed loop and a current closed loop: Outer Loop Speed ​​Closed-Loop: Using the target speed of the four wheels calculated in step S105 as the given command, the actual speed of each wheel motor is collected in real time through Hall sensors or encoders, and the speed error is calculated. The desired current command is output via the PI controller. The PI parameters for the speed loop are taken as follows: To ensure response speed Eliminate steady-state error.

[0112] Inner loop current closed-loop: Tracks the current command output from the outer loop, collects the actual motor current through a current sensor, calculates the current error, and adjusts the motor torque output via a PI controller. Current loop PI parameters are taken as follows: , .

[0113] Step 3, Control Cycle and Communication Interface: In this example, the control cycle The clock speed is 50 Hz. The main computer sends the four-wheel speed commands to the drivers of each wheel motor via communication. The drivers perform dual closed-loop control internally and feed back the actual speed and current to the main computer, forming a closed loop.

[0114] The above describes the adaptive path tracking control method for an omnidirectional mobile transport platform based on multi-mode switching according to embodiments of this application. It can be recognized that the embodiments of this application have the following advantages: I. Multi-mode adaptive switching, balancing smoothness and flexibility. A fuzzy mapping is constructed from vehicle speed-curvature to rotation mode, using lateral motion intensity factors. As a mediating variable, The fuzzy subsets S, M, and B correspond to mode a (rotation mode around the center of mass), mode b (rotation mode around the rear axle center), and mode c (rotation mode around the right rear wheel center), respectively. The three modes switch smoothly and continuously, which solves the contradiction that a single rotation center cannot simultaneously achieve high-speed smoothness and low-speed agility.

[0115] II. Significantly improved tracking accuracy and effective elimination of steady-state error. The integral sliding mode controller introduces an error integral term to eliminate the steady-state error of traditional sliding mode; the hyperbolic tangent function replaces the sign function to suppress chattering. Combined with fuzzy output mode commands, it achieves precise mapping from the desired yaw rate to the speeds of all four wheels.

[0116] III. Seamless control execution and efficient, precise command transmission. A complete control chain is constructed, encompassing mode decision-making, yaw control, kinematic allocation, and underlying execution. Fuzzy output mode commands directly drive the kinematic model to calculate the target speeds of the four wheels, avoiding empirical allocation errors; the underlying dual-closed-loop drive architecture effectively suppresses nonlinear interferences such as ground friction and load fluctuations.

[0117] IV. Strong robustness and disturbance rejection capability, adaptable to extreme cornering conditions. The integral sliding mode controller exhibits strong robustness to model parameters and external disturbances. Lateral motion intensity factor. It comprehensively reflects the coupled influence of vehicle speed and curvature on lateral stability, enabling the control strategy to actively adapt to changes in road surface adhesion and sudden changes in path curvature. Even under extreme conditions such as high-speed double lane change and large curvature bends, it can still maintain good tracking accuracy and cargo transportation safety.

[0118] Reference Figure 5 This application also provides an adaptive path tracking control system for an omnidirectional mobile transport platform based on multi-mode switching, comprising: The three-mode motion model establishment module is used to establish a three-mode target steering kinematic model for an omnidirectional mobile transport platform. The fuzzy inference rule base construction module is used to build a fuzzy inference rule base based on the lateral motion intensity factor; The rotation mode determination module is used to obtain the current longitudinal speed and current path curvature of the omnidirectional mobile platform, and determine the optimal rotation mode based on the current longitudinal speed, current path curvature and fuzzy inference rule base. The yaw rate calculation module is used to establish the aiming error model and error dynamics equation, design an integral sliding mode lateral controller based on the aiming error model and error dynamics equation, and then obtain the desired yaw rate based on the integral sliding mode lateral controller. The four-wheel speed calculation module is used to obtain the target speed of the four wheels based on the optimal rotation mode and the desired yaw rate. The path tracking control module is used to perform longitudinal speed planning based on a fifth-order polynomial to obtain the desired longitudinal vehicle speed, and to perform path tracking control on the omnidirectional mobile transport platform according to the target rotation speed of the four wheels.

[0119] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0120] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0121] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0122] Please see Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1002 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1002 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 using the methods described in the embodiments of this application. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0123] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0124] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0125] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0126] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0127] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0128] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0129] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0130] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0131] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0132] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0133] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0135] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0137] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0138] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. An adaptive path tracking control method for an omnidirectional mobile transport platform based on multi-mode switching, characterized in that, The method includes the following steps: Establish a three-mode target steering kinematic model for an omnidirectional mobile transport platform; Construct a fuzzy inference rule base based on lateral motion intensity factors; The current longitudinal velocity and current path curvature of the omnidirectional mobile platform are obtained, and the optimal rotation mode is determined based on the current longitudinal velocity, the current path curvature, and the fuzzy inference rule base. Establish a pre-aiming error model and an error dynamics equation, design an integral sliding mode lateral controller based on the pre-aiming error model and the error dynamics equation, and then obtain the desired yaw rate based on the integral sliding mode lateral controller; Based on the optimal rotation mode and the desired yaw rate, the target rotational speed of the four wheels is obtained; Based on the fifth-order polynomial, longitudinal speed planning is performed to obtain the desired longitudinal vehicle speed, and the omnidirectional mobile transport platform is path-tracking control is performed according to the target rotational speed of the four wheels.

2. The method according to claim 1, characterized in that, The establishment of the three-mode target steering kinematic model for the omnidirectional mobile transport platform is specifically as follows: A three-mode target steering kinematic model is established, including a rotation mode around the center of mass, a rotation mode around the rear axle center, and a rotation mode around the right rear wheel center.

3. The method according to claim 1, characterized in that, The construction of the fuzzy inference rule base based on the lateral motion intensity factor specifically includes: Using the platform's longitudinal velocity and path curvature as input variables, the universe of discourse of the lateral motion intensity factor is divided into multiple fuzzy subsets; Define the membership function of each fuzzy subset, and construct the fuzzy inference rule base according to the fuzzy subset and the fuzzy label corresponding to each fuzzy subset; Based on the membership function, sub-region labels in the fuzzy inference rule base are determined, and the mapping rules between the sub-region labels and the three-mode target steering kinematic model are determined.

4. The method according to claim 3, characterized in that, The step of determining the optimal rotation mode based on the current platform longitudinal speed, the current path curvature, and the fuzzy inference rule base specifically includes: The current lateral motion intensity factor is calculated based on the current platform longitudinal velocity and the current path curvature. Determine the target fuzzy subset corresponding to the current lateral motion intensity factor; Calculate the input membership degree of the current lateral motion intensity factor in each of the fuzzy subsets based on the membership function; Based on the input membership degree and the target fuzzy subset, determine the target sub-region label corresponding to the current lateral motion intensity factor; Based on the input membership degree and the mapping rule, the target sub-region label is deblurred using the centroid method to obtain the optimal rotation mode.

5. The method according to claim 1, characterized in that, The establishment of the pre-aiming error model and error dynamics equations specifically includes: The reference path of the omnidirectional mobile transport platform is discretized to obtain the coordinates, tangent direction angle, and curvature of each path point; Calculate the lateral error from the reference path to the platform centroid based on the coordinates and the tangent direction angle; Based on the tangent direction angle, calculate the heading error between the reference path tangent direction and the platform heading; Based on the lateral error, the heading error, and the aiming distance, the aiming error model is established; The error dynamics equation is established based on the lateral error, the heading error, and the curvature, according to the vehicle kinematics relationship.

6. The method according to claim 5, characterized in that, The design of the integral sliding mode lateral controller based on the pre-aiming error model and the error dynamics equation specifically includes: The error dynamics equation is converted into standard state-space form; Establish a sliding surface function that includes a preview error term, a preview error differential term, and a preview error integral term; Based on the substitution of the sign function with the hyperbolic tangent function, and based on the sliding surface function, an improved multi-power reaching law is designed; Based on the error dynamics equation in standard state-space form and the improved multi-power approach law, the desired yaw rate control law is obtained. Based on the sliding surface function and the improved multi-power approach law, an adaptive law is designed. The stability of the sliding surface function, the improved multi-power approach law, the desired yaw rate control law, and the adaptive law are verified to obtain the integral sliding lateral controller.

7. The method according to claim 1, characterized in that, The process of obtaining the target rotational speed of the four wheels based on the optimal rotation mode and the desired yaw rate specifically includes: Obtain the wheelbase, track width, and Mecanum wheel radius of the omnidirectional mobile transport platform; Based on the optimal rotation mode, the four-wheel rotation speeds are calculated according to the desired yaw rate, the wheelbase, the track width, and the Mecanum wheel radius to obtain the target rotation speeds of the four wheels.

8. The method according to any one of claims 1 to 7, characterized in that, The process of longitudinal speed planning based on a fifth-order polynomial to obtain the desired longitudinal vehicle speed, and path tracking control of the omnidirectional mobile transport platform based on the target rotational speeds of the four wheels, specifically includes: Establish a fifth-order polynomial based on the desired longitudinal vehicle speed and design boundary conditions; The desired longitudinal vehicle speed is obtained by solving the fifth-degree polynomial based on the boundary conditions. Using the target speed of the four wheels as the given command for the speed loop, the current speed of each wheel motor is collected, the speed error is calculated based on the current speed, and then the PI controller outputs the desired current command based on the speed error; The desired current command is tracked by a current loop, the current of each wheel motor is collected, the current error is calculated based on the current current, and then the motor torque output is adjusted by a PI controller based on the current error.

9. An adaptive path tracking control system for an omnidirectional mobile transport platform based on multi-mode switching, characterized in that, The system includes: The three-mode motion model establishment module is used to establish a three-mode target steering kinematic model for an omnidirectional mobile transport platform. The fuzzy inference rule base construction module is used to build a fuzzy inference rule base based on the lateral motion intensity factor; The rotation mode determination module is used to obtain the current longitudinal speed and current path curvature of the omnidirectional mobile platform, and determine the optimal rotation mode based on the current longitudinal speed, the current path curvature and the fuzzy inference rule base. The yaw rate calculation module is used to establish a pre-aiming error model and an error dynamics equation, design an integral sliding mode lateral controller based on the pre-aiming error model and the error dynamics equation, and then obtain the desired yaw rate based on the integral sliding mode lateral controller. The four-wheel speed calculation module is used to obtain the target speed of the four wheels based on the optimal rotation mode and the desired yaw rate. The path tracking control module is used to perform longitudinal speed planning based on a fifth-order polynomial to obtain the desired longitudinal vehicle speed, and to perform path tracking control on the omnidirectional mobile transport platform according to the target rotational speed of the four wheels.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method of any one of claims 1 to 8.

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