Sliding steering robot and control method thereof

Abstract

The application provides a sliding steering robot control method, comprising the following steps: S1, obtaining target tire forces required by each wheel through a positive dynamics model according to target longitudinal acceleration and target yaw angle acceleration; obtaining vertical loads of each wheel according to current longitudinal acceleration, yaw angle acceleration and attitude angle; S2, inputting the target tire forces and the vertical loads into a tire model to obtain target slip rates; S3, obtaining target rotating speeds of each wheel according to the target slip rates and current vehicle speed, and performing motor rotating speed control. The application combines the dynamics principle and the kinematics principle, establishes a motion relationship by using vehicle slip rates and driving forces, and controls according to power requirements, so as to achieve the purpose of stable and effective operation of a complex terrain sliding steering vehicle.

Description

Technical Field

[0001] This invention belongs to the field of sliding steering vehicle technology, specifically relating to a sliding steering robot and its control method. Background Technology

[0002] Currently, most sliding steering vehicles use pure kinematic principles or combine machine vision and GPS information for motion control. However, on uneven terrain or sandy roads, if the robot's tires cannot make firm contact with the ground, they cannot provide enough driving force, causing the robot to be unable to move forward stably. Or, when the robot is traveling on a steep hillside or climbing high steps, the robot's tires cannot provide enough lateral force to stabilize the vehicle, making it difficult to control the robot's posture, and it may even get stuck or overturn. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention combines the principles of dynamics and kinematics, utilizes the vehicle's slip ratio and driving force to establish a motion relationship, and controls the vehicle according to power requirements, thereby achieving stable and effective operation of the sliding steering vehicle in complex terrain.

[0004] The first aspect of the present invention provides:

[0005] A control method for a sliding steering robot includes:

[0006] The target longitudinal acceleration and target yaw acceleration are calculated using a positive dynamics model to obtain the target tire force required for each wheel; the vertical load on each wheel is obtained based on the current longitudinal acceleration, yaw acceleration, and attitude angle.

[0007] The target tire force and the vertical load are input into the tire model to obtain the target slip ratio;

[0008] The target rotational speed of each wheel is obtained based on the target slip ratio and the current vehicle speed.

[0009] The second aspect of the present invention provides:

[0010] A sliding steering robot includes a plurality of wheels and a plurality of drive motors for driving the wheels to rotate, the drive motors being electrically connected to a power source, a controller including at least one processor, and a memory storing instructions that, when executed by the at least one processor, implement the steps of the method provided according to the first aspect of the invention.

[0011] The beneficial effect of this invention is that it combines the principles of dynamics and kinematics, and achieves the purpose of a sliding steering robot walking on complex terrain with a small number of parameters. Attached Figure Description

[0012] Figure 1 Flowchart of the driving force algorithm according to an embodiment of the present invention;

[0013] Figure 2 Flowchart of the driving force algorithm for determining the driving force according to an embodiment of the present invention;

[0014] Figure 3 Flowchart of the slip ratio algorithm according to an embodiment of the present invention;

[0015] Figure 4 Flowchart of the speed closed-loop control algorithm according to an embodiment of the present invention;

[0016] Figure 5 Flowchart of the centroid velocity control algorithm according to an embodiment of the present invention;

[0017] Figure 6 Flowchart of the centroid velocity closed-loop control algorithm in this invention embodiment Figure 1 ;

[0018] Figure 7 Flowchart of the centroid velocity closed-loop control algorithm in this invention embodiment Figure 2 ;

[0019] Figure 8 Control response curves under pure driving conditions according to embodiments of the present invention; (a) Slip rate response curve and longitudinal velocity response curve; (b) Slip rate response curve according to embodiments of the present invention;

[0020] Figure 9 Control response curves under steering conditions according to embodiments of the present invention; (a) longitudinal speed response curve according to embodiments of the present invention; (b) yaw rate response curve according to embodiments of the present invention; (c) slip rate response curve according to embodiments of the present invention. Detailed Implementation

[0021] The following embodiments further illustrate the content of the present invention, but should not be construed as limiting the present invention. Any modifications or substitutions made to the methods, steps, or conditions of the present invention without departing from the spirit and essence of the invention are within the scope of the present invention.

[0022] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] This invention achieves good control results for a sliding steering robot with geometric symmetry and whose center of mass and geometric center are basically coincident. The embodiments of this invention mainly introduce a four-wheel sliding steering robot with its center of mass and geometric center basically coincident.

[0024] In some embodiments, such as Figure 1 As shown:

[0025] A control method for a sliding steering robot includes:

[0026] S1 calculates the target tire force required for each wheel by using the target longitudinal acceleration and target yaw acceleration through a positive dynamics model; and obtains the vertical load of each wheel based on the current longitudinal acceleration, yaw acceleration, and attitude angle.

[0027] S2 inputs the target tire force and the vertical load into the tire model to obtain the target slip ratio;

[0028] S3 obtains the target rotational speed of each wheel based on the target slip ratio and the current vehicle speed, and performs motor speed control.

[0029] In these embodiments:

[0030] The analysis of the sliding steering robot is divided into two main parts: kinematics and dynamics. Kinematics is further divided into tire kinematics and the robot's center-of-mass kinematics, while dynamics is divided into tire dynamics and the robot's center-of-mass dynamics. In kinematics, the relationship between the center-of-mass velocity, wheel center velocity, wheel speed, and slip ratio is analyzed. In dynamics, the influence of tire mechanical properties and tire models on the robot's driving performance and stability is analyzed, resulting in center-of-mass dynamic models for different road conditions and motion states. Finally, the conclusions from kinematics and dynamics are combined to provide theoretical support for subsequent driving force distribution and control algorithms.

[0031] Given the tire load and tire model, a unique mapping relationship exists between the tire's slip ratio and longitudinal force. Based on this relationship, a drive force distribution scheme based on slip ratio control, after providing a target acceleration, calculates the target drive force for each wheel using forward dynamics. Then, it calculates the target slip ratio corresponding to the target drive force using the Magic Tire formula model. Finally, it controls the slip ratio by controlling the rotational speed of individual wheels, thereby achieving drive force distribution control. This overcomes the shortcomings of control using a single kinematic or dynamic model, improves the slip ratio control effect, keeps the slip ratio within a more ideal range, resulting in superior acceleration performance and faster response to target speeds.

[0032] In some embodiments, such as Figure 1 As shown:

[0033] The positive dynamic model is shown in the following equation:

[0034]

[0035]

[0036]

[0037] In the above formula: F represents force, x represents the x-axis (longitudinal) direction, y represents the y-axis (lateral) direction, z represents the z-axis (vertical) direction, a represents acceleration, I represents moment of inertia, and F X F represents the longitudinal force of the tire. y F represents the lateral force of the tire. z Indicates the vertical load on the tire, a x a represents longitudinal acceleration. y α represents lateral acceleration, m represents mass, g represents gravitational acceleration, α, β, γ represent attitude angles, A represents left and right wheel track, and B represents front and rear wheel track.

[0038] In this embodiment:

[0039] Assuming the vehicle body has a known attitude angle [γ, α, β]T, acceleration [ax, ay, az]T, and yaw acceleration αz, the rotation matrix calculates that the vehicle body is stationary at this point, and the rotation is in the direction of gravity. This results in gravitational components of different magnitudes in the XYZ axes, which are related to the vehicle body's attitude. Treating the vehicle body as an inertial frame of reference, the sum of all external forces should be zero; therefore:

[0040]

[0041] The rotation matrix is ​​defined in the following order: first rotate around the Z-axis, then around the Y-axis, and finally around the X-axis, where j represents the number of tires;

[0042] The gravitational component G after rotation by the rotation matrix is:

[0043]

[0044] Analyze the corresponding torque F with the contact points of the left and right wheels as the center. fa The relationship can be obtained as follows:

[0045]

[0046] By combining with planar dynamic analysis, a vertical load distribution model (Equation V) and forward and inverse kinematic models (Equations I-III) are obtained.

[0047] Taking into account the slope and the force on each tire, the combination of dynamic and kinematic models overcomes the shortcomings of single kinematic or dynamic model control, improves the slip ratio control effect, keeps the slip ratio within a more ideal range, has better acceleration performance, can respond to target speed more quickly, and improves acceleration performance and control effect on slopes.

[0048] In some embodiments, such as Figure 2 As shown:

[0049] A sliding steering robot control method, the method further comprising:

[0050] Determine if the current vehicle speed (center of gravity speed) is greater than the minimum starting value;

[0051] If the vehicle speed is less than the minimum starting value, the slip ratio is calculated and obtained to obtain the target rotational speed; if the vehicle speed is greater than the minimum starting value, step S1 is performed.

[0052] In this embodiment:

[0053] Since the robot may experience a speed of 0 during actual operation, it's impossible to calculate the wheel speeds from the current vehicle speed and the target slip ratio. Therefore, in the actual control process, it's assumed that the tires will not slip when the center of gravity speed is extremely low. The drive force distribution algorithm is activated when the longitudinal speed of the center of gravity exceeds a certain threshold, such as 0.1 m / s or 0.05 m / s. This optimizes the control process to prevent tire slippage when the center of gravity speed is extremely low, making the drive force distribution more reasonable during vehicle startup or low-speed operation, improving drive force distribution efficiency, and shortening startup or acceleration time.

[0054] In some embodiments, such as Figure 3 As shown:

[0055] A sliding steering robot control method, wherein obtaining the target slip ratio includes:

[0056] Calculate the longitudinal force value based on the parameters of the Magic Tire;

[0057] Determine if the maximum longitudinal force value is greater than the maximum longitudinal force. If it is not greater, look up the table to obtain the slip ratio corresponding to the current longitudinal force.

[0058] If the force is greater than the maximum longitudinal force, then the slip ratio corresponding to the maximum longitudinal force is output.

[0059] In this embodiment:

[0060] In the longitudinal force formula of the magic formula, each slip ratio corresponds to a unique longitudinal force. However, a longitudinal force may have one, two, or even no corresponding slip ratios. This method solves the problem of the correspondence between longitudinal force and slip ratio, ensuring a one-to-one correspondence between them and improving the matching rate and data security of longitudinal force and slip ratio.

[0061] In some embodiments, such as Figure 3 As shown:

[0062] The sliding steering robot control method, wherein the magic tire parameters are determined according to the following formula:

[0063]

[0064] In the above formula: x represents the tire slip ratio, z represents the current wheel load, F is the corresponding wheel load when fitting the parameters, D represents the maximum value of the curve, which is the peak factor; B is the stiffness factor; E represents the shape near the maximum value of the curve, which is the curve curvature factor; C represents whether the curve shape is biased towards lateral force, longitudinal force or self-correcting torque, which is the curve shape factor; S is the drift in the vertical direction of the curve.

[0065] In this embodiment:

[0066] The Magic Formula is an empirical formula describing the mechanical properties of automobile tires under different road surface friction coefficients. It was proposed by American engineer Hans Pacejka in 1984. Compared to other models, its fitting accuracy is superior; it can accurately describe the tire's response even under conditions of strong nonlinearity, and therefore it has been widely used in studies of vehicle dynamics and handling stability.

[0067] The Magic Tire Formula uses a combination of trigonometric functions to fit test tire data. A single set of formulas can comprehensively express operating conditions such as longitudinal force, lateral force, self-aligning torque, and the combined action of longitudinal and lateral forces. The general expression of the Magic Tire Formula is as follows:

[0068] y=Dsin(Carctan(Bx-E(Bx-arctan(Bx))))+S

[0069] Where D is the peak factor, representing the maximum value of the curve; B is the stiffness factor; E is the curve curvature factor, representing the shape near the maximum value of the curve; C is the curve shape factor, i.e., whether the curve shape is biased towards lateral force, longitudinal force, or normalizing moment; and S is the vertical drift of the curve.

[0070] By substituting the tire characteristic data obtained from experiments into the above formula, the parameters in the formula are fitted. Based on different parameter relationships and independent variables, a single formula can be developed to fully express the mechanical characteristics of the tire related to longitudinal force, lateral force, and self-aligning torque.

[0071] However, the above formula is too complex. While the parameters of the magic tire are closely related to the load, fitting all the coefficients is quite complicated. The original formula is modified as follows:

[0072]

[0073] It simplifies the complexity of fitting parameters while taking into account parameter changes caused by load, thus ensuring fitting accuracy.

[0074] In some embodiments, such as Figure 3 As shown:

[0075] The sliding steering robot control method according to claim 1 is characterized in that the vertical load of each wheel is obtained according to the following formula based on the current longitudinal acceleration and yaw acceleration:

[0076]

[0077] In the above formula: F z F represents the vertical load on the tire. zn This represents the vertical load corresponding to different tires, where m represents mass, g represents gravitational acceleration, and a represents the vertical load corresponding to different tires. x a represents longitudinal acceleration. y α represents lateral acceleration, h represents the height of the center of mass, α, β, and γ represent attitude angles, A represents the left and right wheel track, and B represents the front and rear wheel track.

[0078] In this embodiment:

[0079] The load distribution model under known attitude and acceleration conditions considers the motion of planes and inclined planes, and the resultant external force, which increases the accuracy and applicability of the model, improves the model output accuracy, and enhances the control effect of sliding steering and driving.

[0080] In some embodiments of the present invention, such as Figure 1 As shown:

[0081] The sliding steering robot control method also includes:

[0082] The longitudinal velocity and yaw rate of the center of mass are input into the longitudinal velocity controller and the yaw rate controller, respectively, to obtain the target longitudinal acceleration and the target yaw rate.

[0083] In this embodiment:

[0084] The algorithm framework of this invention is mainly divided into two parts: driving force allocation algorithm and PID controller.

[0085] The PID controller consists of two separate PI controllers: the longitudinal velocity PI controller result is used as the target longitudinal acceleration, and the yaw rate PI controller result is used as the target yaw rate acceleration. The target acceleration is used as input to the driving force distribution algorithm, which ultimately calculates the optimal driving force for each wheel at the current attitude angle and outputs the target rotational speed of each wheel for control, thus achieving closed-loop speed control of the sliding steering robot.

[0086] In some embodiments, such as Figure 2 As shown:

[0087] The control method for a sliding steering robot uses PID controllers for both the longitudinal speed controller and the yaw rate controller.

[0088] In this embodiment:

[0089] Table 1. Specific parameters for the PID control of the center of mass velocity.

[0090]

[0091] In some embodiments, the sliding steering robot control method further includes:

[0092] The target rotational speed is input into the motor control module to update the longitudinal velocity and yaw rate of the center of mass, thereby performing closed-loop speed control.

[0093] In this embodiment: the longitudinal velocity and yaw rate of the current center of mass are used as feedback; the result of the longitudinal velocity PI controller is used as the target longitudinal acceleration; and the result of the yaw rate PI controller is used as the target yaw rate acceleration. This achieves closed-loop velocity control.

[0094] Example:

[0095] The physical simulation model of the sliding steering robot based on the Adams platform is established. The relevant model parameters of the sliding steering robot built in the Adams platform are given in Table 2.

[0096] Table 2. Relevant parameters of the physical model of the sliding steering robot

[0097]

[0098] Note: The track widths of the front and rear axles are the same on both sides.

[0099] Adams software offers a rich library of tire model files. Adams Car software also integrates functions for testing the dynamic characteristics of tire models. By selecting to test longitudinal, lateral, or self-aligning characteristics and applying different tire loads, the corresponding characteristic curve data can be obtained.

[0100] The Adams Car algorithm was used to test the dynamic characteristics of the selected tire model. The test data was then imported into Matlab. Using the cftool toolbox, the parameters in the Magic Tire formula were identified, and the simulated tire model parameters were obtained as shown in Table 3.

[0101] Table 3. Fitted parameters of the Magic Tire

[0102]

[0103] A model of a magic tire is built using the identified parameters.

[0104] Simulink has wide applications in robot simulation. It is commonly used for modeling robot systems, designing control algorithms, building simulation environments, and establishing real-time connections with hardware devices. This paper will utilize Simulink as a platform for building models and algorithms, and conduct co-simulation with Adams to verify the feasibility of the models and algorithms. Considering the working conditions of a sliding steering robot, the following four working conditions are designed for the verification of the load distribution calculation module.

[0105] (1) Static load distribution of different robot loads on flat ground

[0106] The robot was tested on flat ground with payloads of 223kg, 323kg, and 423kg for the robot body and 30kg for each tire. The results are shown in Tables 4, 5, and 6.

[0107] Table 4 Simulation values ​​of load distribution under different vehicle body loads

[0108]

[0109] Table 5 Simulation values ​​of load distribution under different vehicle body loads

[0110]

[0111] The error table obtained by analyzing the data in the two tables is shown in Table 6.

[0112] Table 6 Simulation values ​​of load distribution under different vehicle body loads

[0113]

[0114] In this table, we can see that when the vehicle is stationary on the ground, the error between the model and the simulation value is within 1N, indicating that the model matches the simulation value.

[0115] (2) Static load distribution of the robot on a ramp at different attitude angles

[0116] Inclines with gradients of 0.1 rad, 0.2 rad, and 0.3 rad were designed, and the robot was placed at different heading angles on the inclines for verification. The results are shown in Tables 7, 8, and 9.

[0117] Table 7 Simulated values ​​of load distribution under different slope and attitude angles

[0118]

[0119] Table 7 (Continued) Simulation values ​​of load distribution under different slope attitude angles

[0120] Table 8 Theoretical values ​​of load distribution under different slope and attitude angles

[0121]

[0122]

[0123] The error table obtained by analyzing the data in the two tables is shown in Table 9.

[0124] Table 9. Error Calculation Table of Load Distribution under Different Slope and Attitude Angles

[0125]

[0126] Operating conditions 4 and 5 are actually the attitude angles when the vehicle body yaw angle is 0.78 rad on slopes of 0.2 rad and 0.3 rad respectively. In the first three operating conditions, the maximum error for a single wheel is 13.5 N, and the minimum error is 0 N. The maximum error for the total mass is 22.5 N, and the minimum is 0.5 N. As we can see from this table, when the slope is within 0.2 rad, the errors are all in single digits. For operating conditions within 0.3 rad, the accuracy is sufficient to meet the requirements.

[0127] (3) Dynamic load distribution of different robot loads on flat ground

[0128] The robot was tested on flat ground under various conditions, including linear acceleration (ranging from 0.5 to 5 m / s²) and lateral acceleration (ranging from 0.25 to 0.66 m / s²), steady-state turning, accelerated turning, and braking. The results are shown in Tables 10, 11, and 12.

[0129] Table 10 Simulation values ​​of load distribution under different accelerations

[0130]

[0131] Table 11 Theoretical values ​​of load distribution under different accelerations

[0132]

[0133] The error table obtained by analyzing the data in the two tables is shown in Table 12:

[0134] Table 12 Error Table for Load Distribution Calculation at Different Accelerations

[0135]

[0136] Conditions 1 and 2 represent the robot's steady-state acceleration on a plane; Condition 3 represents the robot's variable acceleration on a plane; Conditions 4 and 5 represent the robot's steady-state turning and accelerated turning on a plane, respectively; and Condition 6 represents the robot's forced motion on a plane. Under the slope condition, all non-emergency braking scenarios meet the accuracy requirements.

[0137] like Figure 8 The control response curve shown is for the pure drive condition:

[0138] exist Figure 8 In diagram a, the blue signal represents the target value, the orange signal represents the velocity response after driving force distribution, and the purple signal represents the velocity response based on kinematic model control as a control. Figure 8 In diagram b, the solid line represents the slip ratio curves of the four wheels after the driving force is distributed, and the dashed line represents the slip ratio response of each wheel based on kinematic control. Since the robot's initial position in the Adams model is not parallel to the slope, there is a falling phase; therefore, in the diagram, the model begins to move after the robot stabilizes in 1 second.

[0139] exist Figure 8 From 1S to 2.5S and from 4S to 7S, the speed curve after driving force distribution has a steeper slope, resulting in superior acceleration performance and a faster response to target speed. Figure 8 In step b, the slip ratio values ​​after driving force distribution are consistently below 0.2. However, under kinematic model control, the slip ratio exceeds 1 during acceleration, indicating significant tire slippage and fluctuations in the longitudinal velocity curve, suggesting poor vehicle stability. From 7 to 8 seconds, the slope of the velocity curves after driving force distribution is also better. This is because the vehicle is on a slope, and gravity provides additional longitudinal resistance, resulting in a greater slope during braking than during acceleration.

[0140] In summary, it can be seen that in linear motion with a slope, the closed-loop control algorithm for driving force distribution can significantly improve driving performance.

[0141] like Figure 9 The control response curves under the steering conditions shown are as follows:

[0142] exist Figure 9 In diagram a, the blue signal represents the target value of the yaw rate, the orange signal represents the yaw rate response after the driving force is distributed, and the purple signal represents the yaw rate response based on kinematic model control as a control.

[0143] Figure 9 a and Figure 8 The results in group a are almost identical, indicating that even during turning, the drive force distribution algorithm can improve the robot's drive performance and stability. Figure 9 As can be seen in b, the yaw rate response is significantly improved after the driving force is redistributed. Because the longitudinal force of the tires accounts for a large portion of the adhesion force during acceleration or braking, there is insufficient force to overcome ground resistance and maintain steering motion. Therefore, it can be seen that... Figure 9 In step b, the yaw rate response decreases when the robot brakes or accelerates, and increases when the robot moves at a constant speed.

[0144] In summary, the effects of the two classic operating conditions demonstrate that force distribution improves tire force efficiency, optimizing the robot's driving performance and vehicle stability on slopes under the same tire constraints. Furthermore, even with stringent step input signals, it ensures the robot responds using limited tire force, preventing slippage and loss of control as seen in pure kinematic control. The closed-loop control algorithm after force distribution significantly enhances the robot's driving performance and vehicle stability.

[0145] Although the present invention has been described in detail above with general descriptions, specific embodiments, and experiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A control method for a sliding steering robot, characterized in that, include: S1 calculates the target tire force required for each wheel by using the target longitudinal acceleration and target yaw acceleration through a positive dynamics model; and obtains the vertical load of each wheel based on the current longitudinal acceleration, yaw acceleration, and attitude angle. S2 inputs the target tire force and the vertical load into the tire model to obtain the target slip ratio; S3 obtains the target speed of each wheel based on the target slip ratio and the current vehicle speed, and performs motor speed control; The positive dynamic model is shown in the following equation: In the above formula: F X F represents the longitudinal force of the tire. y F represents the lateral force of the tire. z Indicates the vertical load on the tire, a x a represents longitudinal acceleration. y Indicates lateral acceleration. The yaw rate is represented by the acceleration due to gravity, where m represents mass and g represents gravitational acceleration. Indicates attitude angle, I Z Let A represent the moment of inertia about the Z-axis, A represent the left and right wheelbase, and B represent the front and rear wheelbase.

2. The sliding steering robot control method according to claim 1, characterized in that, The step of obtaining the vertical load on each wheel based on the current longitudinal acceleration, yaw acceleration, and attitude angle further includes: Determine if the current vehicle speed is greater than the minimum starting speed; If the vehicle speed is less than the minimum starting value, the slip ratio is calculated and obtained to obtain the target rotational speed; If the vehicle speed is greater than the minimum starting speed, proceed to step S1.

3. The sliding steering robot control method according to claim 1, characterized in that, Obtaining the target slip ratio includes: Calculate the longitudinal force value based on the parameters of the Magic Tire; Determine if the maximum longitudinal force value is greater than the maximum longitudinal force. If it is not greater, look up the table to obtain the slip ratio corresponding to the current longitudinal force. If the force is greater than the maximum longitudinal force, then the slip ratio corresponding to the maximum longitudinal force is output.

4. The sliding steering robot control method according to claim 3, characterized in that, The parameters of the magic tire are determined according to the following formula: In the above formula: x represents the tire slip ratio, z represents the current wheel load, F is the corresponding wheel load when fitting the parameters, D represents the maximum value of the curve, which is the peak factor; B is the stiffness factor; E represents the shape near the maximum value of the curve, which is the curve curvature factor; C represents whether the curve shape is biased towards lateral force, longitudinal force or self-correcting moment, which is the curve shape factor. S represents the vertical drift of the curve.

5. The sliding steering robot control method according to claim 1, characterized in that, The vertical load of each wheel is obtained based on the current longitudinal acceleration and yaw acceleration according to the following formula: In the above formula: F z The vertical load on the tire is represented by m, the mass is represented by g, and the acceleration due to gravity is represented by a. x a represents longitudinal acceleration. y Indicates lateral acceleration, a z The vertical acceleration is represented by h, and the height of the center of mass is represented by h. The angles represent attitude angles, A represents the left and right track widths, and B represents the front and rear track widths.

6. The sliding steering robot control method according to claim 1, characterized in that, Also includes: The longitudinal velocity and yaw rate of the center of mass are input into the longitudinal velocity controller and the yaw rate controller, respectively, to obtain the target longitudinal acceleration and the target yaw rate.

7. The sliding steering robot control method according to claim 6, characterized in that, The longitudinal speed controller and the yaw rate controller are PID controllers.

8. The sliding steering robot control method according to claim 6, characterized in that, Also includes: The target rotational speed is input into the motor control module to update the motor speed, and the longitudinal velocity and yaw rate of the center of mass are also updated. The updated longitudinal velocity and yaw rate of the center of mass are input into the longitudinal velocity controller and the yaw rate controller for closed-loop velocity control.

9. A sliding steering robot, characterized in that, The method includes a plurality of wheels and a plurality of drive motors for driving the wheels to rotate, the drive motors being electrically connected to a power source; a controller including at least one processor; and a memory storing instructions that, when executed by the at least one processor, implement the steps of the method according to any one of claims 1-8.

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