A driving margin-based intelligent robot motion control method

Abstract

The application discloses a kind of intelligent robot motion control methods based on driving margin.The method comprises: the relationship curve between the traction of each wheel in intelligent robot and slip rate is constructed in advance;Driving margin function is determined based on the relationship curve;The traction optimization model of intelligent robot is constructed in advance, and the standard deviation of each wheel driving margin is minimum as objective function with the traction of each wheel in the force of forward direction and the moment of force along the yaw direction as constraint condition;The traction of each wheel in robot is obtained at the current time, and the slip rate of each wheel at the current time is inversely solved based on the relationship curve;The driving margin of corresponding wheel at the current time is determined based on the slip rate of each wheel at the current time and the driving margin function;Based on the motion instruction at the current time, the slip rate and driving margin of each wheel at the current time are used as initial value, and the optimization model is iteratively calculated to obtain optimization result.The application can reduce the slippage or subsidence of robot.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of motion planning, in particular to a motion control method of intelligent robot based on driving margin. BACKGROUND

[0002] Due to the original natural terrain of the surface of extraterrestrial celestial bodies, the surface of extraterrestrial celestial bodies is uneven, and the mechanical properties of rocks and sand change, so when the extraterrestrial exploration wheeled intelligent robot moves on the surface of the celestial body, it is easy to slip and slide. In order to ensure the stable work of the robot, the traction force of each wheel of the robot needs to be controlled.

[0003] At present, the traction control of the extraterrestrial exploration wheeled intelligent robot mainly adopts the kinematic control method, which mainly distributes the wheel speed based on the kinematic model. However, in extraterrestrial exploration, the environment and contact force of each wheel of the robot are different, so the traction force of each wheel determined by this method is not coordinated, which cannot make the whole vehicle traction force optimal, and is easy to cause the robot to slip or sink on the soft and uneven ground.

[0004] Therefore, at present, an intelligent robot motion control method based on driving margin is urgently needed to solve the above problems. SUMMARY

[0005] The present application provides an intelligent robot motion control method based on driving margin, which can prevent and reduce the robot from slipping or sinking on the soft and uneven ground. The technical scheme is as follows:

[0006] On the one hand, an intelligent robot motion control method based on driving margin is provided, the method comprising:

[0007] previously constructing a relationship curve between the traction force and the slip rate of each wheel in the intelligent robot;

[0008] determining a driving margin function based on the relationship curve; the driving margin function is used to determine the driving margin of each wheel based on the slip rate of each wheel, and the driving margin is used to represent the remaining controllable ability of the wheel from the maximum traction force;

[0009] previously constructing a traction force optimization model of the intelligent robot, the optimization model taking the standard deviation of the driving margin of each wheel as the objective function, and the traction force of each wheel in the forward direction and the moment of the traction force along the yaw direction as the constraint condition;

[0010] obtaining the traction force of each wheel of the robot at the current time, and inversely solving the slip rate of each wheel at the current time based on the relationship curve;

[0011] determine the driving margin of each wheel at the current moment based on the slip ratio of each wheel at the current moment and the driving margin function;

[0012] perform iterative calculation on the optimization model based on the motion instruction at the current moment, with the slip ratio and the driving margin of each wheel at the current moment as initial values, to obtain the optimized result of the traction force of each wheel.

[0013] In another aspect, an intelligent robot motion control device based on driving margin is provided, and the device comprises:

[0014] a first construction unit configured to pre-construct a relationship curve between the traction force and the slip ratio of each wheel in the intelligent robot;

[0015] a first determination unit configured to determine a driving margin function based on the relationship curve, the driving margin function being configured to determine the driving margin of each wheel based on the slip ratio of each wheel, the driving margin being configured to represent the remaining controllable ability of each wheel from which the maximum traction force is generated;

[0016] a second construction unit configured to pre-construct an optimization model of the traction force of the intelligent robot, the optimization model having a standard deviation of the driving margin of each wheel as an objective function and having the resultant force of the traction force of each wheel in the forward direction and the resultant moment of the traction force of each wheel in the yaw direction as constraint conditions;

[0017] an acquisition unit configured to acquire the traction force of each wheel in the robot at the current moment and inversely solve the slip ratio of each wheel at the current moment based on the relationship curve;

[0018] a second determination unit configured to determine the driving margin of each wheel at the current moment based on the slip ratio of each wheel at the current moment and the driving margin function;

[0019] an optimization unit configured to perform iterative calculation on the optimization model based on the motion instruction at the current moment, with the slip ratio and the driving margin of each wheel at the current moment as initial values, to obtain the optimized result of the traction force of each wheel.

[0020] In another aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, the computer program being executed by a processor to implement the steps of the intelligent robot motion control method based on driving margin.

[0021] In another aspect, a computer program product is provided, and the computer program product comprises a computer program, the computer program being executed by a processor to implement the steps of the intelligent robot motion control method based on driving margin.

[0022] The embodiment of the present application provides a kind of intelligent robot motion control method based on drive margin, the method first proposes the concept of drive margin, to reflect the remaining controllable ability of wheel distance from its maximum traction force based on drive margin.Then, when carrying out traction force optimization, with the standard deviation of each wheel drive margin minimum as objective function, it can guarantee the balance of the drive margin of each wheel, i.e. each wheel gets effective traction;With the resultant force of the traction force of each wheel in the forward direction and the resultant moment along the yaw direction as constraint condition, it can guarantee the optimal comprehensive traction force of each wheel.So, it can optimize the traction force of each wheel according to the slip condition of different wheels at present, and then improve the passing capacity of robot, prevent and reduce the occurrence of slip and sinking on soft undulating ground in motion from the perspective of active control. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0024] Figure 1 It is a flow chart of the intelligent robot motion control method based on drive margin provided by an embodiment of the present application;

[0025] Figure 2 It is a structural diagram of the intelligent robot motion control device based on drive margin provided by an embodiment of the present application;

[0026] Figure 3 It is a hardware architecture diagram of a computer device provided by an embodiment of the present application;

[0027] Figure 4 It is a schematic diagram of the relationship curve between the traction force and the slip rate of each wheel of the robot provided by an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0029] The specific implementation of the above concept will be described below.

[0030] Please refer to Figure 1The embodiment of the application provides a driving margin-based intelligent robot motion control method, which comprises the following steps:

[0031] In step 100, a relationship curve between the traction force and the slip rate of each wheel of the intelligent robot is constructed in advance.

[0032] In step 102, a driving margin function is determined based on the relationship curve; the driving margin function is used to determine the driving margin of each wheel based on the slip rate of the wheel, and the driving margin is used to represent the remaining controllable ability of the wheel from the maximum traction force.

[0033] In step 104, a traction force optimization model of the intelligent robot is constructed in advance, the optimization model takes the minimum standard deviation of the driving margins of the wheels as an objective function, and the traction forces of the wheels in the forward direction and the moment of the traction forces of the wheels in the yaw direction are taken as constraint conditions.

[0034] In step 106, the traction force of each wheel of the robot at the current time is obtained, and the slip rate of each wheel at the current time is inversely solved based on the relationship curve.

[0035] In step 108, the driving margin of each wheel at the current time is determined based on the slip rate of each wheel at the current time and the driving margin function.

[0036] In step 110, the traction force optimization result of each wheel is obtained by iteratively calculating the optimization model based on the motion instruction at the current time, and taking the slip rate and the driving margin of each wheel at the current time as initial values.

[0037] In this embodiment, the concept of driving margin is first proposed to reflect the remaining controllable ability of each wheel from the maximum traction force. Then, when the traction force is optimized, the minimum standard deviation of the driving margins of the wheels is taken as the objective function, so that the driving margins of the wheels are balanced, that is, the effective traction of each wheel is obtained; the traction forces of the wheels in the forward direction and the moment of the traction forces of the wheels in the yaw direction are taken as constraint conditions, so that the comprehensive traction force of each wheel is optimized. In this way, the traction force of each wheel can be optimized according to the slip of each wheel at the current time, and the passing ability of the robot is improved, and the occurrence of slip and sinking in the motion on the soft and undulating ground is prevented and reduced from the perspective of active control.

[0038] The execution mode of each step is described below. Figure 1

[0039] For step 100, the relationship curve between the traction force and the slip rate of each wheel of the intelligent robot is constructed in advance.

[0040] In this step, the relationship curve is determined based on ground tests, and the determination method is as follows: ​

[0041] For each wheel, perform the following:

[0042] Multiple data points were identified, with the slip ratio of each data point ranging from 0 to s. max Between, and the slip ratio intervals of each data point are evenly distributed; s max The slip ratio at which the wheel provides maximum traction;

[0043] Based on the normal force of the wheel and the ground mechanics formula, calculate the traction force corresponding to the slip ratio at each data point;

[0044] Based on the slip ratio and traction force of each data point, the relationship curve between the traction force and slip ratio of the corresponding wheel is determined using linear interpolation.

[0045] In this step, by studying the effects of slippage and normal force on the traction force under single-wheel-terrain interaction, the different normal forces F can be determined. n Traction force F under the condition d The relationship curve between F and slip ratio s, i.e., F d -s relationship curve. For example... Figure 4 The figure shows a schematic diagram of the relationship between the traction force and slip ratio of each wheel of a robot. Different curves represent different wheels, each bearing a different normal force. From the results in the figure, the following conclusions can be drawn:

[0046] First, increasing slippage is beneficial to traction F d While slippage generates traction, excessive slippage reduces the wheel's traction. If the normal load on the wheel is determined, the traction value initially increases with increasing slippage. This is because the wheel must maintain a moderate slippage value to generate sufficient traction. However, when slippage exceeds a certain value, the traction value begins to decrease with increasing slippage. This is because larger slippage leads to greater sinking, which is detrimental to traction generation and can trap the wheel in extreme situations.

[0047] Second, if a wheel has a large normal force, its maximum traction capacity is large, but it is prone to excessive slippage. For F d The -s relationship curve shows the maximum traction force value between moderate and excessive slip, represented by F. dm The maximum value is represented by s. m This represents the corresponding slip value, i.e., the slip ratio s when the wheel provides maximum traction. max F under different normal force conditions d In the -s curve, the maximum traction force F dm1 >F dm2 >F dm3 The corresponding slip value is s m1 <sm2 <s m3 Therefore, when the slip increases from zero, the wheel with the largest normal force will reach the maximum traction state first and encounter excessive slip first.

[0048] It can be seen that only through an appropriate slip value can the maximum traction of the wheel be obtained. This value can be affected by the normal load and the terrain material properties. When the slip of the wheel is less than this value, it can increase the traction by increasing the slip (for example, by increasing the rotation speed). When the slip of the wheel is greater than this value, excessive slip occurs, and increasing the speed or driving torque will reduce the traction. The latter case is harmful to the passing ability of the robot and should be avoided by limiting the slip to a moderate value. Therefore, the traction capability of the wheel is directly related to the current slip rate and the maximum slip rate. Based on this conclusion, the inventors propose the concept of driving margin to evaluate the traction capability of the wheel.

[0049] For step 102, the calculation formula of the driving margin function is:

[0050]

[0051] In the formula, λ is the driving margin of the wheel, s is the current slip rate of the wheel, s0 is the slip rate when the wheel provides zero traction, and s max is the slip rate when the wheel provides maximum traction.

[0052] In this step, the driving margin is a dimensionless index, and the value is between 0 and 1. The higher the driving margin index, the greater the remaining controllable ability to generate maximum traction, and vice versa. In practical applications, s0 and s max may be determined according to the constructed F d -s curve.

[0053] For step 104, the target function is:

[0054]

[0055]

[0056] In the formula, σ λ is the standard deviation of the driving margins of the wheels; n is the total number of wheels of the intelligent robot; λ k is the driving margin of the kth wheel; is the average driving margin of the wheels; k = 1, 2, … n.

[0057] In this step, the standard deviation of the driving margin of each wheel is taken as the objective function, so that the driving margin of each wheel can be kept as close as possible, thereby improving the traction of the robot in the following two aspects: first, the traction of each wheel is utilized equally, which enables the wheel that is less likely to slip to bear more load than other wheels. Second, all wheels can reach the maximum traction state at the same time, thereby improving the maximum traction capacity of the entire robot. That is, the goal of the traction control of the optimization model is to maximize the traction while avoiding large slip.

[0058] In some embodiments, the constraint condition is:

[0059]

[0060] In the formula, F dsum is the resultant force of the traction of each wheel in the forward direction; T dsum is the resultant moment of each wheel in the yaw direction; n is the total number of wheels of the intelligent robot, k = 1, 2, … n; F dk is the traction of the kth wheel; x k and p k are the unit direction vector and the unit position vector of the kth wheel, respectively; x R and z R are the unit direction vectors of the intelligent robot in the forward direction and the vertical upward direction, respectively.

[0061] In this step, the above constraint can ensure the optimal comprehensive traction of each wheel.

[0062] Finally, for steps 106-110, in extraterrestrial actual exploration, as long as the real traction and normal force of each wheel of the robot are obtained, the corresponding slip ratio of each wheel can be inversely solved according to the relationship curve. Then, based on the slip ratio, the driving margin function is used to calculate the driving margin of the corresponding wheel. Based on the received motion instruction, the optimization model is iteratively calculated until the driving margins of each wheel are balanced, i.e., the difference between the driving margins of each wheel is less than a preset threshold, and the traction optimization result of each wheel is obtained. In addition, the preset threshold is determined according to actual needs, which is not limited in the present application.

[0063] It should be noted that the motion instruction includes the forward speed, lateral speed and yaw speed of the robot.

[0064] In order to verify the effect of the method, the inventors carried out a large number of test analyses on a soft and undulating terrain slope test site by a ground prototype, and the test results show that, compared with the test without using the method, the average traction of the front wheel of the intelligent robot is increased by 14.8%, the average traction of the rear wheel of the intelligent robot is increased by 59.7%, and the overall passing performance of the intelligent robot is greatly improved.

[0065] As shown in Figure 2 , Figure 3 , the embodiment of the application provides an intelligent robot motion control device based on driving margin. The device embodiment can be realized by software, or realized by hardware or a combination of software and hardware. From the hardware layer, as shown in Figure 2 , a hardware architecture diagram of a computing device where the intelligent robot motion control device based on driving margin provided by the embodiment of the application is located, in addition to the processor, memory, network interface, and non-volatile memory shown in Figure 2 , the computing device where the device in the embodiment is usually also composed of other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, as shown in Figure 3 , as a logically meaningful device, it is formed by the CPU of the computing device where it is located reading the corresponding computer program in the non-volatile memory into the memory for running.

[0066] Please refer to Figure 3 , the embodiment of the application provides an intelligent robot motion control device based on driving margin, which comprises:

[0067] The first construction unit 300 is used for pre-construction of the relationship curve between the traction force and the slip ratio of each wheel of the intelligent robot;

[0068] The first determination unit 302 is used for determining the driving margin function based on the relationship curve; the driving margin function is used for determining the driving margin of the corresponding wheel based on the slip ratio of each wheel, and the driving margin is used for representing the remaining controllable ability of the wheel from the maximum traction force;

[0069] The second construction unit 304 is used for pre-construction of the traction force optimization model of the intelligent robot, and the optimization model takes the standard deviation of the driving margin of each wheel as the objective function, and takes the resultant force of the traction force of each wheel in the forward direction and the resultant moment along the yaw direction as the constraint condition;

[0070] The acquisition unit 306 is used for acquiring the traction force of each wheel of the robot at the current time, and inversely solving the slip ratio of each wheel at the current time based on the relationship curve;

[0071] The second determination unit 308 is configured to determine the driving margin of each wheel at the current time based on the slip ratio of each wheel at the current time and the driving margin function.

[0072] The optimization unit 310 is configured to perform iterative calculation on the optimization model based on the motion instruction at the current time, and take the slip ratio and the driving margin of each wheel at the current time as initial values, to obtain the traction force optimization result of each wheel.

[0073] In some embodiments, the relationship curve is determined based on a ground test, and the determination method is as follows:

[0074] For each wheel, the following is performed:

[0075] A plurality of data points are determined, and the slip ratio of each data point is between 0 and s max , and the slip ratio of each data point is uniformly distributed; s max is the slip ratio when the wheel provides the maximum traction force.

[0076] Based on the normal force of the wheel and the ground mechanics formula, the traction force corresponding to the slip ratio of each data point is calculated.

[0077] Based on the slip ratio and the traction force of each data point, a linear interpolation method is used to determine the relationship curve between the traction force and the slip ratio of the corresponding wheel.

[0078] In some embodiments, the calculation formula of the driving margin function is as follows:

[0079]

[0080] In the formula, λ is the driving margin of the wheel, s is the current slip ratio of the wheel, s0 is the slip ratio when the wheel provides zero traction force, and s max is the slip ratio when the wheel provides the maximum traction force.

[0081] In some embodiments, the objective function is as follows:

[0082]

[0083]

[0084] In the formula, σ λ is the standard deviation of the driving margins of the wheels; n is the total number of wheels of the intelligent robot; λ k is the driving margin of the kth wheel; is the average driving margin of the wheels; k = 1, 2, …, n.

[0085] In some embodiments, the constraint condition is as follows:

[0086]

[0087] wherein F dsum is the resultant force of the traction forces of each wheel in the forward direction; T dsum is the resultant moment of each wheel in the yaw direction; n is the total number of wheels of the intelligent robot, k = 1, 2, … n; F dk is the traction force of the kth wheel; x k and p k are the unit direction vector and the unit position vector of the kth wheel, respectively; x R and z R are the unit direction vectors of the intelligent robot in the forward direction and the vertical upward direction, respectively.

[0088] It should be noted that the above embodiment provides the intelligent robot motion control device based on the driving margin, which is only exemplified by the division of the above functional modules. In actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the intelligent robot motion control device based on the driving margin provided in the above embodiment and the intelligent robot motion control method based on the driving margin belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0089] The embodiment of the present application also provides a computer device, which refers to Figure 3 The computer device includes a processor and a memory, and the memory stores at least one instruction, at least one program, a code set or an instruction set. The at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to realize the intelligent robot motion control method based on the driving margin provided by each method embodiment.

[0090] The embodiment of the present application also provides a computer readable storage medium, which stores at least one instruction, at least one program, a code set or an instruction set. The at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to realize the intelligent robot motion control method based on the driving margin provided by each method embodiment.

[0091] The embodiment of the present application also provides a computer program product, which includes a computer program. The processor of the computer device reads the computer program from the computer readable storage medium. The processor executes the computer program, so that the computer device executes the intelligent robot motion control method based on the driving margin in any of the above embodiments.

[0092] For ease of description, the above system or apparatus is described in various modules or units respectively in terms of functions. Of course, in the implementation of the present application, the functions of each unit can be implemented in the same or more software and / or hardware.

[0093] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary universal hardware platforms. Based on such an understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in various embodiments or some parts of the embodiments of the present application.

[0094] Finally, it should be noted that in this document, relational terms such as first and second and third and fourth, and the like can merely be used to distinguish one entity or action from another, without necessarily requiring or implying any actual such relationship or order between or among the entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0095] The above description is only the preferred embodiments of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A motion control method for an intelligent robot based on drive margin, characterized in that, The method includes: Pre-construct the relationship curve between traction force and slip ratio for each wheel in the intelligent robot; Based on the relationship curve, a drive margin function is determined; the drive margin function is used to determine the drive margin of the corresponding wheel based on the slip ratio of each wheel, and the drive margin is used to characterize the remaining controllable capability of the wheel from the point where it generates maximum traction. A traction optimization model for the intelligent robot is pre-constructed. The optimization model takes the minimum standard deviation of the driving margin of each wheel as the objective function and the resultant force of the traction force of each wheel in the forward direction and the resultant torque in the yaw direction as the constraint conditions. Obtain the traction force of each wheel in the robot at the current moment, and solve the slip ratio of each wheel at the current moment based on the relationship curve; Based on the slip ratio of each wheel at the current moment and the drive margin function, the drive margin of the corresponding wheel at the current moment is determined. Based on the motion command at the current moment, and using the slip ratio and drive margin of each wheel at the current moment as initial values, the optimization model is iteratively calculated to obtain the traction optimization result for each wheel.

2. The method according to claim 1, characterized in that, The relationship curves were determined based on ground-based experiments, and the determination method is as follows: For each wheel, perform the following: Multiple data points are defined, and the slip rate of each data point is between 0 and s. max Between, and the slip ratio intervals of each data point are evenly distributed; s max The slip ratio at which the wheel provides maximum traction; Based on the normal force of the wheel and the ground mechanics formula, calculate the traction force corresponding to the slip ratio for each of the data points; Based on the slip ratio and traction force of each data point, the relationship curve between the traction force and slip ratio of the corresponding wheel is determined using linear interpolation.

3. The method according to claim 1, characterized in that, The formula for calculating the driving margin function is as follows: In the formula, λ is the driving margin of the wheel, s is the current slip ratio of the wheel, s0 is the slip ratio when the wheel provides zero traction, and s max The slip ratio at which the wheel provides maximum traction.

4. The method according to claim 3, characterized in that, The objective function is: In the formula, σ λ λ represents the standard deviation of the drive margin of each wheel; n is the total number of wheels of the intelligent robot; λ k This represents the drive margin of the k-th wheel; Let k be the average drive margin of each wheel; k = 1, 2, ..., n.

5. The method according to claim 4, characterized in that, The constraints are as follows: In the formula, F dsum T is the resultant force of the traction force of each wheel in the forward direction; dsum Let F be the resultant torque of each wheel along the yaw direction; n is the total number of wheels of the intelligent robot, k = 1, 2, ..., n; dk x is the traction force of the k-th wheel; k and p k Let x be the unit direction vector and unit position vector of the k-th wheel, respectively. R and z R These are the unit direction vectors for the intelligent robot along its forward direction and vertically upward direction, respectively.

6. A motion control device for an intelligent robot based on drive margin, characterized in that, The device includes: The first building unit is used to pre-build the relationship curve between the traction force and slip ratio of each wheel in the intelligent robot; The first determining unit is used to determine the driving margin function based on the relationship curve; the driving margin function is used to determine the driving margin of the corresponding wheel based on the slip ratio of each wheel, and the driving margin is used to characterize the remaining controllable capability of the wheel from the point where it generates maximum traction. The second construction unit is used to pre-build the traction optimization model of the intelligent robot. The optimization model takes the minimum standard deviation of the driving margin of each wheel as the objective function and the resultant force of the traction force of each wheel in the forward direction and the resultant torque in the yaw direction as the constraint conditions. The acquisition unit is used to acquire the traction force of each wheel in the robot at the current moment, and to solve the slip ratio of each wheel at the current moment based on the relationship curve. The second determining unit is used to determine the driving margin of the corresponding wheel at the current moment based on the slip ratio of each wheel at the current moment and the driving margin function. The optimization unit is used to iteratively calculate the optimization model based on the motion command at the current moment, using the slip ratio and drive margin of each wheel at the current moment as initial values, to obtain the traction optimization result for each wheel.

7. The apparatus according to claim 6, characterized in that, The relationship curves were determined based on ground-based experiments, and the determination method is as follows: For each wheel, perform the following: Multiple data points are defined, and the slip rate of each data point is between 0 and s. max Between, and the slip ratio intervals of each data point are evenly distributed; s max The slip ratio at which the wheel provides maximum traction; Based on the normal force of the wheel and the ground mechanics formula, calculate the traction force corresponding to the slip ratio for each of the data points; Based on the slip ratio and traction force of each data point, the relationship curve between the traction force and slip ratio of the corresponding wheel is determined using linear interpolation.

8. The apparatus according to claim 6, characterized in that, The formula for calculating the driving margin function is as follows: In the formula, λ is the driving margin of the wheel, s is the current slip ratio of the wheel, s0 is the slip ratio when the wheel provides zero traction, and s max The slip ratio at which the wheel provides maximum traction.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the steps of the method according to any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1-5.

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