Intelligent robot motion control method based on driving margin

Abstract

The invention discloses an intelligent robot motion control method based on driving margin. The method comprises the following steps: pre-constructing a relation curve between traction force and slip rate of each wheel in the intelligent robot; determining a driving margin function based on the relation curve; a traction optimization model of the intelligent robot is constructed in advance, and the optimization model takes the minimum standard deviation of the driving margin of each wheel as a target function and takes the resultant force of the traction of each wheel in the advancing direction and the resultant moment in the yaw direction as constraint conditions; the traction force of each wheel in the robot at the current moment is obtained, and the slip rate of each wheel at the current moment is inversely solved based on the relation curve; determining the driving margin of the corresponding wheel at the current moment based on the slip rate of each wheel at the current moment and the driving margin function; and based on the motion instruction at the current moment, taking the slip rate and the driving margin of each wheel at the current moment as initial values, and carrying out iterative calculation on the optimization model to obtain an optimization result. The sliding or sinking of the robot can be reduced.

Description

Technical Field

[0001] The invention relates to the technical field of motion planning, and in particular to an intelligent robot motion control method based on drive margin. Background Art

[0002] Due to the undulating and uneven original natural terrain on the surface of extraterrestrial bodies, and the changing mechanical properties of rocks and sand, it is very easy for an extraterrestrial wheeled intelligent robot to slip and turn when moving on the surface of a celestial body. In order to ensure the stable operation of the robot, it is necessary to control the traction of each wheel of the robot.

[0003] At present, the traction control of wheeled intelligent robots for extraterrestrial exploration mainly adopts the kinematic control method, which mainly distributes wheel speed based on the kinematic model. However, in extraterrestrial exploration, since the environment and contact force of each wheel of the robot are different, the traction generated by each wheel determined by this method is not coordinated, and the traction of the entire vehicle body cannot be optimized, which easily causes the robot to slip or sink on the soft and undulating ground.

[0004] Therefore, there is an urgent need for an intelligent robot motion control method based on drive margin to solve the above problems. Summary of the invention

[0005] The present invention provides an intelligent robot motion control method based on drive margin, which can prevent and reduce the robot from slipping or sinking on soft and undulating ground when moving. The technical solution is as follows:

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

[0007] Pre-build the relationship curve between the traction and slip rate of each wheel in the intelligent robot;

[0008] Based on the relationship curve, a driving margin function is determined; the driving margin function is used to determine the driving margin of the corresponding wheel based on the slip rate of each wheel, and the driving margin is used to characterize the remaining controllable capacity of the wheel to generate maximum traction;

[0009] Pre-constructing a traction optimization model of the intelligent robot, wherein the optimization model takes the minimum standard deviation of the driving margin of each wheel as the objective function, and takes the resultant force of the traction of each wheel in the forward direction and the resultant moment in the yaw direction as the constraint conditions;

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

[0011] Determining the driving margin of the corresponding wheel at the current moment based on the slip rate of each wheel at the current moment and the driving margin function;

[0012] Based on the motion command at the current moment, the optimization model is iteratively calculated with the slip rate and driving margin of each wheel at the current moment as initial values ​​to obtain the traction optimization result of each wheel.

[0013] On the other hand, an intelligent robot motion control device based on drive margin is provided, the device comprising:

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

[0015] A first determining unit is used to determine a 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 rate of each wheel, and the driving margin is used to characterize the remaining controllable capacity of the wheel to generate maximum traction;

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

[0017] an acquisition unit, used for acquiring the traction force of each wheel of the robot at a current moment, and inversely solving the slip rate of each wheel at a current moment based on the relationship curve;

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

[0019] The optimization unit is used to iteratively calculate the optimization model based on the motion command at the current moment and take the slip rate and driving margin of each wheel at the current moment as initial values ​​to obtain the traction optimization result of each wheel.

[0020] On the other hand, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned intelligent robot motion control method based on drive margin are implemented.

[0021] On the other hand, a computer program product is provided, including a computer program, which implements the steps of the above-mentioned intelligent robot motion control method based on drive margin when executed by a processor.

[0022] The embodiment of the present invention provides a motion control method for an intelligent robot based on a driving margin. The method first proposes the concept of a driving margin, and reflects the remaining controllable capacity of the wheel to generate the maximum traction based on the driving margin. Then, when optimizing the traction, the standard deviation of the driving margin of each wheel is minimized as the objective function, so that the driving margin of each wheel can be balanced, that is, each wheel can obtain effective traction; the resultant force of the traction of each wheel in the forward direction and the resultant torque along the yaw direction are used as constraints, so that the comprehensive traction of each wheel can be guaranteed to be optimal. In this way, the traction of each wheel can be optimized according to the current slip conditions of different wheels, thereby improving the robot's passing ability, and preventing and reducing the occurrence of slipping and sinking on soft and undulating ground during movement from the perspective of active control. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

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

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

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

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

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

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

[0030] Please refer to Figure 1, an embodiment of the present invention provides an intelligent robot motion control method based on drive margin, the method comprising:

[0031] Step 100, pre-constructing a relationship curve between the traction and slip rate of each wheel in the intelligent robot;

[0032] Step 102, determining a 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 rate of each wheel, and the driving margin is used to characterize the remaining controllable capacity of the wheel to generate maximum traction;

[0033] Step 104, pre-constructing a traction optimization model of the intelligent robot, wherein the optimization model takes the minimum standard deviation of the driving margin of each wheel as the objective function, and takes the resultant force of the traction of each wheel in the forward direction and the resultant torque in the yaw direction as the constraint conditions;

[0034] Step 106, obtaining the traction force of each wheel of the robot at the current moment, and inversely solving the slip rate of each wheel at the current moment based on the relationship curve;

[0035] Step 108, determining the driving margin of the corresponding wheel at the current moment based on the slip rate of each wheel at the current moment and the driving margin function;

[0036] Step 110 , based on the motion command at the current moment, taking the slip rate and driving margin of each wheel at the current moment as initial values, iteratively calculate the optimization model to obtain the traction optimization result of each wheel.

[0037] In this embodiment, the concept of driving margin is first proposed to reflect the remaining controllable capacity of the wheel to generate the maximum traction based on the driving margin. Then, when optimizing the traction, the standard deviation of the driving margin of each wheel is minimized as the objective function, which can ensure the balance of the driving margin of each wheel, that is, each wheel has effective traction; the resultant force of the traction of each wheel in the forward direction and the resultant torque along the yaw direction are used as constraints to ensure the optimal comprehensive traction of each wheel. In this way, the traction of each wheel can be optimized according to the current slip conditions of different wheels, thereby improving the robot's passing ability and preventing and reducing the occurrence of slipping and sinking on soft and undulating ground during movement from the perspective of active control.

[0038] Described below Figure 1 How the various steps are performed.

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

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

[0041] For each wheel, execute:

[0042] Determine multiple data points, the slip rate of each data point is between 0 and s max and the slip rate interval of each data point is evenly distributed; s max The slip ratio that provides maximum traction to the wheel;

[0043] Based on the normal force of the wheel and the ground mechanics formula, the traction force corresponding to the slip rate of each data point is calculated;

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

[0045] In this step, the effects of slip and normal force on the traction force under single wheel-terrain interaction can be determined for different normal forces F n Traction force F under conditions d The relationship curve between the slip rate s, that is, F d -s relationship curve. Figure 4 The figure shows the relationship between the traction and slip rate of each wheel of a robot. In the figure, different curves represent different wheels, and each wheel bears different normal forces. The following conclusions can be drawn from the results in the figure:

[0046] First, increasing slip is beneficial to traction F d However, excessive slip will reduce the traction capacity of the wheel. If the normal load of the wheel is determined, the value of traction will first increase with the increase of slip value. This is because the wheel must maintain a moderate slip value to generate sufficient traction. But when the slip is greater than a certain value, the value of traction begins to decrease with the increase of slip value. This is because a larger slip will cause a larger sinking, which is not conducive to the generation of traction and causes the wheel to be trapped in extreme situations.

[0047] Second, if the wheel has a large normal force value, its maximum traction capacity is large, but it is easy to slip excessively. d -s relationship curve, there is a maximum traction value between medium slip and excessive slip, expressed as F dm The maximum value is represented by s m Indicates the corresponding slip value, that is, the slip rate s when the wheel provides maximum traction max . F under different normal force conditions d - In the s curve, the maximum traction force value F dm1 >F dm2 >F dm3 , the corresponding slip value is s m1 <sm2 m3 Therefore, as slip increases from zero, the wheel with the largest normal force will reach the maximum traction state first and experience excessive slip first.

[0048] It can be seen that the maximum traction of the wheel can only be obtained with an appropriate slip value. This value may be affected by the normal load and the material properties of the terrain. 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 situation is harmful to the robot's passing ability and should be avoided by limiting the slip to a moderate value. Therefore, the traction capacity of the wheel is directly related to the current slip rate and the maximum slip rate. Based on this conclusion, the inventor proposed the concept of drive margin to evaluate the traction capacity of the wheel.

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

[0050]

[0051] Where λ is the driving margin of the wheel, s is the current slip rate of the wheel, and s 0 The slip ratio when zero traction is provided to the wheel, s max The slip ratio that provides maximum traction to the wheel.

[0052] In this step, the driving margin is a dimensionless index with a value between 0 and 1. The higher the driving margin index, the greater the remaining controllable capacity to generate the maximum traction force, and vice versa. 0 and max According to the constructed F d -S curve determined.

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

[0054]

[0055]

[0056] In the formula, σ λ is the standard deviation of the driving margin of each wheel; 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 each wheel; k=1, 2,...n.

[0057] ​In this step, the objective function is to minimize the standard deviation of the driving margin of each wheel, 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 used equally, so that the wheel that is not prone to excessive slip can 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. In other words, the goal of traction control in this optimization model is to maximize traction while avoiding large slip.

[0058] In some embodiments, the constraints are:

[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 torque of each wheel along 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 unit position vector of the kth wheel, respectively, x R and z R They are the unit direction vectors of the intelligent robot along the forward direction and the vertical upward direction respectively.

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

[0062] Finally, for steps 106-110, in actual extraterrestrial exploration, as long as the real traction and normal force of each wheel of the robot are obtained, the slip rate corresponding to each wheel can be inversely solved according to the relationship curve. Then, based on the slip rate, the drive margin function is used to calculate the drive margin of the corresponding wheel. And based on the received motion instructions, the optimization model is iteratively calculated until the drive margin of each wheel is balanced, that is, the difference in the drive margin between each wheel is less than the preset threshold, and the traction optimization result of each wheel is obtained. In addition, the preset threshold is determined according to actual needs, and this application does not make specific limitations.

[0063] It should be noted that the motion instructions include the robot's forward speed, lateral speed and yaw speed.

[0064] In order to verify the effectiveness of the method of the present application, the inventors carried out a large number of test analyses using a ground prototype in a slope test site with soft and undulating terrain. The test results show that compared with the tests without using the drive margin control method, the average traction of the front wheels of the intelligent robot increased by 14.8%, and the average traction of the rear wheels increased by 59.7%, and the overall passing performance of the intelligent robot was greatly improved.

[0065] like Figure 2 , Figure 3 As shown, an embodiment of the present invention provides an intelligent robot motion control device based on drive margin. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. From the hardware level, Figure 2 As shown in FIG. 1 , a hardware architecture diagram of a computing device where an intelligent robot motion control device based on drive margin is provided in an embodiment of the present invention is located. Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown in the figure, the computing device in which the device is located in the embodiment may also generally include other hardware, such as a forwarding chip responsible for processing messages, etc. Taking software implementation as an example, Figure 3 As shown, as a device in a logical sense, the CPU of the computing device in which it is located reads the corresponding computer program in the non-volatile memory into the internal memory and runs it.

[0066] Please refer to Figure 3 , an embodiment of the present invention provides an intelligent robot motion control device based on drive margin, the device comprising:

[0067] The first construction unit 300 is used to pre-construct a relationship curve between the traction force and the slip rate of each wheel in the intelligent robot;

[0068] A first determining unit 302 is used to determine a 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 rate of each wheel, and the driving margin is used to characterize the remaining controllable capacity of the wheel to generate the maximum traction force;

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

[0070] An acquisition unit 306 is used to acquire the traction force of each wheel of the robot at the current moment, and inversely solve the slip rate of each wheel at the current moment based on the relationship curve;

[0071] A second determining unit 308, configured to determine the driving margin of the corresponding wheel at the current moment based on the slip rate of each wheel at the current moment and the driving margin function;

[0072] The optimization unit 310 is used to iteratively calculate the optimization model based on the motion command at the current moment and take the slip rate and driving margin of each wheel at the current moment as initial values ​​to obtain the traction optimization result of each wheel.

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

[0074] For each wheel, execute:

[0075] Determine multiple data points, the slip rate of each data point is between 0 and s max and the slip rate interval of each data point is evenly distributed; s max The slip ratio that provides maximum traction to the wheel;

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

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

[0078] In some implementations, the calculation formula of the driving margin function is:

[0079]

[0080] Where λ is the driving margin of the wheel, s is the current slip rate of the wheel, and s 0 The slip ratio when zero traction is provided to the wheel, s max The slip ratio that provides maximum traction to the wheel.

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

[0082]

[0083]

[0084] In the formula, σ λ is the standard deviation of the driving margin of each wheel; 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 each wheel; k=1, 2,...n.

[0085] In some embodiments, the constraints are:

[0086]

[0087] In the formula, F dsum is the resultant force of the traction of each wheel in the forward direction; T dsum is 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; F dk is the traction of the kth wheel; x k and p k are the unit direction vector and unit position vector of the kth wheel, respectively, x R and z R They are the unit direction vectors of the intelligent robot along the forward direction and the vertical upward direction respectively.

[0088] It should be noted that the intelligent robot motion control device based on drive margin provided in the above embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, 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 drive margin provided in the above embodiment and the intelligent robot motion control method embodiment based on drive margin belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0089] The embodiment of the present application also provides a computer device, please refer to Figure 3 The computer device includes a processor and a memory, in which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the intelligent robot motion control method based on drive margin provided by the above-mentioned method embodiments.

[0090] An embodiment of the present application also provides a computer-readable storage medium, on which is stored at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the intelligent robot motion control method based on drive margin provided in the above-mentioned method embodiments.

[0091] An embodiment of the present application also provides a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium, and the processor executes the computer program, so that the computer device executes any of the intelligent robot motion control methods based on drive margin in the above-mentioned embodiments.

[0092] For the convenience of description, the above system or device is described by dividing it into various modules or units according to its functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0093] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0094] Finally, it should be noted that, in this article, relational terms such as first, second, third and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0095] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An intelligent robot motion control method based on drive margin, characterized in that: The method comprises: Pre-build the relationship curve between the traction and slip rate of each wheel in the intelligent robot; Based on the relationship curve, a driving margin function is determined; the driving margin function is used to determine the driving margin of the corresponding wheel based on the slip rate of each wheel, and the driving margin is used to characterize the remaining controllable capacity of the wheel to generate maximum traction; Pre-constructing a traction optimization model of the intelligent robot, wherein the optimization model takes the minimum standard deviation of the driving margin of each wheel as the objective function, and takes the resultant force of the traction of each wheel in the forward direction and the resultant moment in the yaw direction as the constraint conditions; Obtaining the traction force of each wheel of the robot at the current moment, and inversely solving the slip rate of each wheel at the current moment based on the relationship curve; Determining the driving margin of the corresponding wheel at the current moment based on the slip rate of each wheel at the current moment and the driving margin function; Based on the motion command at the current moment, the optimization model is iteratively calculated with the slip rate and driving margin of each wheel at the current moment as initial values ​​to obtain the traction optimization result of each wheel.

2. The method according to claim 1, characterized in that: The relationship curve is determined based on ground tests, and the determination method is as follows: For each wheel, execute: Determine multiple data points, each of which has a slip rate between 0 and s max and the slip rate interval of each data point is evenly distributed; s max The slip ratio that provides maximum traction to the wheel; Calculating the traction force corresponding to the slip rate of each data point based on the normal force of the wheel and a ground mechanics formula; Based on the slip ratio and traction force of each data point, a relationship curve between the traction force and slip ratio of the corresponding wheel is determined by linear interpolation method.

3. The method according to claim 1, characterized in that The calculation formula of the driving margin function is: Where 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 The slip ratio that provides maximum traction to the wheel.

4. The method according to claim 3, characterized in that The objective function is: In the formula, σ λ is the standard deviation of the driving margin of each wheel; 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 each wheel; k=1, 2,...n.

5. The method according to claim 4, characterized in that The constraints are: In the formula, F dsum is the resultant force of the traction of each wheel in the forward direction; T dsum is 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; F dk is the traction of the kth wheel; x k and p k are the unit direction vector and unit position vector of the kth wheel, respectively, x R and z R They are the unit direction vectors of the intelligent robot along the forward direction and the vertical upward direction respectively.

6. An intelligent robot motion control device based on drive margin, characterized in that: The device comprises: A first construction unit is used to pre-construct a relationship curve between the traction force and slip rate of each wheel in the intelligent robot; A first determining unit is used to determine a 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 rate of each wheel, and the driving margin is used to characterize the remaining controllable capacity of the wheel to generate maximum traction; A second construction unit is used to pre-construct a traction optimization model of the intelligent robot, wherein the optimization model takes the minimum standard deviation of the driving margin of each wheel as the objective function, and takes the resultant force of the traction of each wheel in the forward direction and the resultant moment in the yaw direction as the constraint conditions; an acquisition unit, used for acquiring the traction force of each wheel of the robot at a current moment, and inversely solving the slip rate of each wheel at a current moment based on the relationship curve; a second determining unit, configured to determine the driving margin of the corresponding wheel at a current moment based on the slip rate of each wheel at a 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 and take the slip rate and driving margin of each wheel at the current moment as initial values ​​to obtain the traction optimization result of each wheel.

7. The device according to claim 6, characterized in that The relationship curve is determined based on ground tests, and the determination method is as follows: For each wheel, execute: Determine multiple data points, each of which has a slip rate between 0 and s max and the slip rate interval of each data point is evenly distributed; s max The slip ratio that provides maximum traction to the wheel; Calculating the traction force corresponding to the slip rate of each data point based on the normal force of the wheel and a ground mechanics formula; Based on the slip ratio and traction force of each data point, a relationship curve between the traction force and slip ratio of the corresponding wheel is determined by linear interpolation method.

8. The device according to claim 6, characterized in that The calculation formula of the driving margin function is: Where 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 The slip ratio that provides maximum traction to the wheel.

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

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 5 are implemented.

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