Mobile device control method, mobile device, electronic device and medium

Abstract

The present application provides a mobile device control method, a mobile device, an electronic device and a medium, wherein the mobile device control method comprises: obtaining current state parameters of the mobile device, wherein the current state parameters include current posture parameters and current speed parameters of the mobile device; determining expected state parameters of the mobile device based on the current state parameters; optimizing the expected state parameters so that the expected state parameters are close to the target state parameters of the mobile device, thereby obtaining an acceleration control instruction of the mobile device; and controlling the mobile device according to the acceleration control instruction and the current state parameters. The technical solution of the present application can improve the intelligence level of the mobile device movement process.

Description

Technical Field

[0001] The present application relates to the technical field of mobile device control, and in particular to a mobile device control method, a mobile device, an electronic device and a medium. Background Art

[0002] With the continuous advancement of science and technology, mobile devices can be driven to a target location point according to a planned path during driving. The current mobile device control method can control the mobile device to drive to the target location point according to the planned path, but it is difficult to intelligently control the change of the speed of the mobile device during driving. Summary of the invention

[0003] In view of this, the embodiments of the present application provide a mobile device control method, a mobile device, an electronic device and a medium, which can improve the intelligence level of the mobile device movement process.

[0004] In a first aspect, an embodiment of the present application provides a method for controlling a mobile device, comprising: obtaining current state parameters of the mobile device, wherein the current state parameters include current posture parameters and current speed parameters of the mobile device; determining expected state parameters of the mobile device based on the current state parameters; optimizing the expected state parameters so that the expected state parameters are close to target state parameters of the mobile device, thereby obtaining an acceleration control instruction of the mobile device; and controlling the mobile device according to the acceleration control instruction and the current state parameters.

[0005] In a second aspect, an embodiment of the present application provides a mobile device control device, comprising: an acquisition module, used to acquire current state parameters of the mobile device, wherein the current state parameters include current posture parameters and current speed parameters of the mobile device; a determination module, used to determine expected state parameters of the mobile device based on the current state parameters; an optimization module, used to optimize the expected state parameters so that the expected state parameters are close to the target state parameters of the mobile device, thereby obtaining an acceleration control instruction of the mobile device; and a control module, used to control the mobile device according to the acceleration control instruction and the current state parameters.

[0006] In a third aspect, an embodiment of the present application provides a mobile device, including a control module, wherein the control module is used to execute the mobile device control method described in the first aspect.

[0007] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a processor; and a memory for storing processor executable instructions, wherein the processor is used to execute the mobile device control method described in the first aspect above.

[0008] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the mobile device control method described in the first aspect above.

[0009] In a sixth aspect, an embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor of a computer device, the computer device is enabled to execute the mobile device control method described in the first aspect above.

[0010] In a seventh aspect, an embodiment of the present application provides a chip, comprising: a processor; and a memory for storing processor executable instructions, wherein the processor is used to execute the mobile device control method described in the first aspect above.

[0011] The embodiments of the present application provide a mobile device control method, a mobile device, an electronic device, and a medium. The desired state parameters of the mobile device are determined according to the current state parameters of the mobile device, and the desired state parameters are optimized so that the desired state parameters are close to the target state parameters of the mobile device, thereby obtaining an acceleration control instruction of the mobile device, and then the mobile device can be controlled according to the acceleration control instruction and the current state parameters. In the embodiments of the present application, the state parameters of the mobile device may include posture parameters and speed parameters. The embodiments of the present application obtain the acceleration control instruction through the optimization process, so that the planned state parameters can be obtained based on the current state parameters and the acceleration control instruction, and then the movement of the mobile device can be controlled based on the planned state parameters, thereby improving the intelligence of the mobile device movement process. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 Shown is a schematic diagram of the system architecture of a mobile device control system provided by an exemplary embodiment of the present application.

[0013] Figure 2 Shown is a flow chart of a mobile device control method provided by an exemplary embodiment of the present application.

[0014] Figure 3 Shown is a schematic diagram of a mobile device provided by an exemplary embodiment of the present application.

[0015] Figure 4 Shown is a flow chart of a mobile device control method provided by another exemplary embodiment of the present application.

[0016] Figure 5 Shown is a schematic structural diagram of a mobile device control device provided by an exemplary embodiment of the present application.

[0017] Figure 6FIG. 1 is a block diagram of an electronic device for executing a mobile device control method provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] Application Overview

[0020] At present, when controlling a mobile device to travel to a target location, some algorithms can be used to plan the travel path of the mobile device in advance, and then the mobile device can be controlled to move to the target location according to the planned path. In the process of controlling the mobile device to move to the target location, the mobile device can be controlled to travel according to the speed set by the user or technician, and this speed control process has a low degree of intelligence.

[0021] In response to the above technical problems, an embodiment of the present application provides a method for controlling a mobile device, by determining the expected state parameters of the mobile device according to the current state parameters of the mobile device, and optimizing the expected state parameters so that the expected state parameters are close to the target state parameters of the mobile device, thereby obtaining an acceleration control instruction of the mobile device, and then controlling the mobile device according to the acceleration control instruction and the current state parameters. In an embodiment of the present application, the state parameters of the mobile device may include posture parameters and speed parameters. The embodiment of the present application obtains the acceleration control instruction through an optimization process, so that the planned state parameters can be obtained based on the current state parameters and the acceleration control instruction, and then the movement of the mobile device can be controlled based on the planned state parameters, thereby improving the intelligence of the mobile device movement process.

[0022] Furthermore, since the state parameters may include posture parameters and speed parameters, the embodiments of the present application may integrate the acceleration planning process and the path planning process, which is equivalent to integrating the speed planning process and the path planning process. In this way, the structural characteristics of the mobile device itself can be fully utilized, such as combining the curvature of the planned path with the speed threshold that can be achieved by the mobile device under the curvature, to give a reasonable posture parameter sequence and speed parameter sequence.

[0023] Exemplary Systems

[0024] Figure 1 FIG. 1 is a schematic diagram of a system architecture of a mobile device control system provided by an exemplary embodiment of the present application. Figure 1As shown, the mobile device control system 100 may include a mobile device 110, and the mobile device 110 may be a robot with a steering wheel, or a vehicle or other device that uses a steering wheel to move. The mobile device 110 may be used to move from a current position point in a target area 120 to a target position point in the target area 120. The target area 120 may be set according to a specific scenario, and the range of the target area 120 may be fixed or may change in real time as required.

[0025] The mobile device 110 may be equipped with a sensor 111 and a controller 112, and the controller 112 may be used to control the mobile device 110 to execute the mobile device control method provided in the embodiment of the present application. In some embodiments, the steering wheels of the mobile device 110 are active wheels, that is, steering wheels that can control both direction and speed.

[0026] In one example, the state parameters of the mobile device 110 may include a posture parameter and a speed parameter. The sensor 111 may be used to obtain the current state parameters of the mobile device 110, and the current state parameters include the current posture parameters and the current speed parameters of the mobile device 110. The controller 112 may determine the current state parameters of the mobile device 110 through the sensor 111, and perform an optimization process or a state parameter planning process according to the current state parameters of the mobile device 110 and the target state parameters, thereby controlling the mobile device 110 to change from the current state parameters to the target state parameters according to the optimization results or the planning results.

[0027] In one example, the controller 112 may determine the expected state parameters of the mobile device 110 based on the current state parameters, and the expected state parameters may be state parameters obtained by the controller 112 based on the current state parameters and a specific function or rule. The goal of the expected state parameters is to approach the target state parameters. The target state parameters may be state parameters of the mobile device 110 at the target location point. The target state parameters may be preset. The controller 112 may optimize the expected state parameters so that the expected state parameters are close to the target state parameters of the mobile device 110, thereby obtaining an acceleration control instruction for the mobile device 110. The acceleration parameter corresponding to the acceleration control instruction may be an optimal acceleration parameter, or an acceleration parameter that meets a preset optimization condition.

[0028] Furthermore, the controller 112 may control the speed of the mobile device 110 according to the acceleration control instruction and the current state parameter, and thereby control the mobile device 110 to move toward the target location point, that is, control the state parameter of the mobile device 110 to approach the target state parameter.

[0029] In other examples, the controller 112 may be located outside the mobile device 110, such as a device independent of the mobile device 110. Exemplarily, the controller 112 may include at least one of a logic controller, a machine vision controller, a motion controller, etc. Exemplarily, the sensor 111 may be at least one of a lidar sensor, an ultrasonic sensor, a visual sensor, etc.

[0030] It should be understood that the above application scenario examples are only shown to facilitate understanding of the spirit and principle of the present application, and the embodiments of the present application are not limited thereto. On the contrary, the embodiments of the present application can be applied to any scenario that may be applicable.

[0031] Exemplary Methods

[0032] Figure 2 Shown is a flow chart of a mobile device control method provided by an exemplary embodiment of the present application. Figure 2 The method can be Figure 1 The controller in is executed. Figure 2 As shown, the mobile device control method may include the following contents.

[0033] 210: Acquire current state parameters of the mobile device, wherein the current state parameters include current posture parameters and current speed parameters of the mobile device.

[0034] Specifically, the current state parameter may be a state parameter of the mobile device at the current moment, or an initial state parameter preset for the mobile device.

[0035] In one example, the state parameters include posture parameters and speed parameters. The posture parameters may include position parameters and attitude parameters. For example, the position parameter may characterize the position of the mobile device in the coordinate system space, and the attitude parameter may characterize the attitude of the mobile device at a specific position, such as the orientation of the mobile device. The speed parameter may include at least one of a linear speed parameter, an angular speed parameter, and an acceleration parameter. In one example, the linear speed parameter may include a longitudinal speed parameter and a transverse speed parameter of the mobile device, and the longitudinal speed parameter may characterize the speed of the mobile device in the first direction, and the transverse speed parameter may characterize the speed of the mobile device in the second direction. The first direction and the second direction may be perpendicular, and the first direction may be the forward direction of the front end of the mobile device. For example, when the mobile device is a vehicle, the first direction may be the forward direction of the front of the vehicle, and the second direction may be a direction perpendicular to the forward direction of the front of the vehicle and extending to the left or right side of the vehicle body. In other examples, the linear speed parameter may characterize the sum of the linear speed of the mobile device in the first direction and the linear speed in the second direction, that is, characterize a combined speed. In one example, the angular speed parameter may characterize the angular speed of the mobile device during movement, and the direction of the angular speed may be perpendicular to the mobile device (such as a vehicle) and upward. In one example, the number of acceleration parameters may be equal to the sum of the number of linear velocity parameters and the number of angular velocity parameters. For example, when the linear velocity parameters include longitudinal velocity parameters and lateral velocity parameters, the number of acceleration parameters may be 3, that is, the acceleration parameters may include longitudinal acceleration parameters, lateral acceleration parameters and angular acceleration parameters.

[0036] 220: Determine expected state parameters of the mobile device based on current state parameters.

[0037] Specifically, the expected state parameter may be a state parameter obtained based on the current state parameter and a specific function or rule. The goal of the expected state parameter is to approach the target state parameter. The target state parameter may be a state parameter of the mobile device at the target location. The target state parameter may be preset.

[0038] In one example, a function of the speed of a mobile device over a preset period of time can be preset, that is, the speed function over time can directly show the speed parameters of the mobile device at different times. Since the speed is a vector and has directionality, the posture parameters of the mobile device at different times can be obtained. The posture parameters and speed parameters of the mobile device at the end of the preset period are the state parameters of the mobile device at the end, and the state parameters are the expected state parameters.

[0039] In other examples, the expected state parameters may be determined based on the current state parameters according to other functions or rules, and the embodiments of the present application do not limit the specific form and content of the functions or rules.

[0040] It should be understood that the preset time period can be set according to actual needs, and different preset time periods can be set according to different functions or rules.

[0041] 230: Optimize the expected state parameter so that the expected state parameter is close to the target state parameter of the mobile device, thereby obtaining an acceleration control instruction of the mobile device.

[0042] In one example, the desired state parameters can be obtained according to a preset speed-time variation function, and a new speed-time variation function can be obtained by continuously adjusting the speed-time variation function based on a preset first function variation rule. New desired state parameters can be obtained based on the new speed-time variation function. In this way, the speed-time variation function can be continuously adjusted based on the preset first function variation rule to achieve an optimization process for the desired state parameters, thereby obtaining an optimal speed-time variation function. Based on the optimal speed-time variation function, an optimal acceleration-time variation function can be obtained, and the optimal acceleration-time variation function can be used as an acceleration control instruction.

[0043] In one example, the function of acceleration changing over time can be represented by an acceleration parameter sequence, which may include multiple acceleration parameters, each of which may correspond to a certain time period. In other examples, the function of acceleration changing over time can be represented by a linear function, a nonlinear function, or other complex functions.

[0044] In one example, the preset first function variation rule can be set according to actual needs. For example, the function of the speed variation over time can be continuously adjusted according to a certain gradient.

[0045] 240: Control the mobile device according to the acceleration control instruction and the current state parameter.

[0046] Specifically, the speed parameters and posture parameters of the mobile device at different times can be adjusted according to the acceleration control instruction, thereby controlling the mobile device to move toward the target position point, that is, controlling the state parameters of the mobile device to move from the current state parameters to the target state parameters.

[0047] The embodiment of the present application provides a method for controlling a mobile device, by determining the expected state parameters of the mobile device according to the current state parameters of the mobile device, and optimizing the expected state parameters so that the expected state parameters are close to the target state parameters of the mobile device, thereby obtaining an acceleration control instruction of the mobile device, and then controlling the mobile device according to the acceleration control instruction and the current state parameters. In the embodiment of the present application, the state parameters of the mobile device may include posture parameters and speed parameters. The embodiment of the present application obtains the acceleration control instruction through the optimization process, so that the planned state parameters can be obtained based on the current state parameters and the acceleration control instruction, and then the movement of the mobile device can be controlled based on the planned state parameters, thereby improving the intelligence of the mobile device movement process.

[0048] Furthermore, since the state parameters may include posture parameters and speed parameters, the embodiments of the present application may integrate the acceleration planning process and the path planning process, which is equivalent to integrating the speed planning process and the path planning process. In this way, the structural characteristics of the mobile device itself can be fully utilized, such as combining the curvature of the planned path with the speed threshold that the mobile device can achieve under the curvature, to give a reasonable posture parameter sequence and speed parameter sequence. The posture parameter sequence here can be a sequence composed of the posture parameters of the mobile device at different times. Similarly, the speed parameter sequence can be a sequence composed of the speed parameters of the mobile device at different times.

[0049] According to an embodiment of the present application, step 220 may include: determining expected state parameters based on current state parameters and a preset acceleration change function.

[0050] In one example, the preset acceleration change function may be a preset function of the acceleration change of the mobile device over time within a preset time period.

[0051] In one example, the speed of the mobile device at different times can be calculated by using the preset acceleration change function and the current state parameters. Since the speed is a vector and has directionality, the posture parameters and speed parameters of the mobile device at different times can be obtained. The posture parameters and speed parameters of the mobile device at the end of the preset time period are the state parameters of the mobile device at the end, which are the expected state parameters.

[0052] In one example, the preset period can be set according to actual needs, and different preset periods can be set according to different acceleration change functions.

[0053] In one example, the desired state parameters can be obtained according to a preset acceleration change function, and then the preset acceleration change function can be adjusted according to a preset second function change rule to obtain a new acceleration change function. New desired state parameters can be obtained based on the new acceleration change function. In this way, the acceleration change function can be continuously adjusted based on the preset second function change rule to achieve an optimization process for the desired state parameters, thereby obtaining an optimal acceleration change function. The optimal acceleration change function can be used as an acceleration control instruction.

[0054] In one example, a constraint condition may be set for the optimization process of the desired state parameters, and the constraint condition may be set according to the structural parameters or performance parameters of the mobile device. For example, the structural parameters may include a limited range of attitude parameters that can be achieved by the mobile device, and the performance parameters may include at least one of a linear velocity limited range, an angular velocity limited range, and an acceleration limited range of the mobile device. By setting a constraint condition for the optimization process of the desired state parameters, it is possible to avoid the obtained acceleration change function being difficult to apply to the mobile device.

[0055] In one example, an optimization condition may be set for the optimization process of the expected state parameter, and the optimization condition may include at least one of the number of iterations of the optimization process meeting a preset number threshold, the expected state parameter in the current iteration meeting a preset parameter condition, etc. When the optimization process meets the optimization condition, the optimization process may be stopped, i.e., the iteration process may be stopped, and the expected state parameter in the current iteration may be used as the optimal expected state parameter.

[0056] In this embodiment, by setting the acceleration change function within a preset time period and determining the expected state parameter based on the current state parameter and the acceleration change function, a corresponding relationship between the expected state parameter and the acceleration change function can be established. When the expected state parameter is optimized so that the expected state parameter is close to the target state parameter of the mobile device, the optimal expected state parameter can be obtained, and the optimal acceleration change function can be obtained at the same time. The optimal expected state parameter can be the state parameter closest to the target state parameter among the historical expected state parameters. The optimal acceleration change function can be used as an acceleration control instruction of the mobile device, or the acceleration control instruction of the mobile device can be determined based on the optimal acceleration change function. According to the acceleration control instruction, the mobile device can be controlled to move to the target position point, thereby improving the intelligence of the mobile device movement process. In addition, by setting constraints on the optimization process of the expected state parameter, an acceleration change function suitable for the mobile device can be obtained.

[0057] According to an embodiment of the present application, expected state parameters are determined based on current state parameters and a preset acceleration change function, including: determining a state parameter change trend function based on the current state parameters and a preset acceleration change function, wherein the state parameter change trend function is used to characterize the change trend of the state parameters of the mobile device over time; and determining the expected state parameters based on the state parameter change trend function.

[0058] Specifically, the preset acceleration change function may be a preset acceleration change function over time. A state parameter change trend function is determined based on the current state parameter and the preset acceleration change function, and the expected state parameter is determined according to the state parameter change trend function. For example, state parameters at different times may be obtained according to the state parameter change trend function, and the state parameter closest to the target state parameter among the state parameters at different times is used as the expected state parameter.

[0059] In the process of optimizing the expected state parameters, the preset acceleration change function can be adjusted according to certain function change rules to obtain a new acceleration change function. For each acceleration change function, the corresponding state parameter change trend function can be obtained based on the current state parameters and the acceleration change function, and then the corresponding expected state parameters can be obtained.

[0060] In one example, the state parameter can be expressed as X=[x, y, θ, v x ,v y ,w z ] T In a two-dimensional coordinate system, x can represent the horizontal coordinate, y can represent the vertical coordinate, and θ can represent the posture. These three can represent the posture parameters. x It can represent the velocity in the first direction (such as the x direction of a two-dimensional coordinate system), v y It can represent the velocity in the second direction (such as the y direction in a two-dimensional coordinate system), w z It can represent angular velocity (the direction of the angular velocity can be perpendicular to the plane where the two-dimensional coordinate system is located), and these three can represent velocity parameters.

[0061] In one example, the posture parameter may be a parameter in a world coordinate system, and the speed parameter may be a parameter in a mobile device coordinate system.

[0062] In one example, the posture parameter and the velocity parameter may represent the posture and velocity of the geometric center point of the mobile device.

[0063] In one example, the acceleration change function can be expressed as u=[a x ,a y ,w z ] T .a x It can represent the acceleration change function in the first direction, ay It can represent the acceleration change function in the second direction, w z It can represent the acceleration change function corresponding to the angular velocity. In this example, the acceleration change function u can be represented in a matrix form, that is, the acceleration change function u can also be called an acceleration change matrix.

[0064] In one example, the state parameter change trend function can be expressed as X=AX0+Bu. X0 can represent the current state parameter. A and B can be parameter matrices, both of which can be set according to actual needs. For example, the function of parameter matrix A can be to project the velocity parameters in the mobile device coordinate system to the world coordinate system; the function of parameter matrix B can be to adjust the dimension of the acceleration change matrix to facilitate subsequent calculations. In one example, parameter matrix A and parameter matrix B can be expressed as follows:

[0065]

[0066] In one example, the state parameter change trend function is the derivative of the state parameter X. The change of the state parameter is affected by at least two factors, one is the current state parameter and the other is the acceleration change function. For example, the state parameter at any time can be obtained by integrating the state parameter change trend function.

[0067] In one example, the desired state parameter can be expressed as X f , the target state parameter can be expressed as X target The expected state parameters can be obtained by integrating the trend function of the state parameter change.

[0068] In one example, the desired state parameter can be optimized based on the constraint condition so that the desired state parameter is close to the target state parameter of the mobile device, thereby obtaining the acceleration control instruction of the mobile device. The constraint condition can be set according to actual needs. The specific content of the constraint condition can be found in the description of other embodiments of the present application.

[0069] In this embodiment, by determining the state parameter change trend function based on the current state parameter and the acceleration change function, and determining the expected state parameter according to the state parameter change trend function, the state parameter closest to the target state parameter under the acceleration change function can be found as the expected state parameter, thereby improving the optimization effect.

[0070] According to an embodiment of the present application, step 230 may include: optimizing the expected state parameter based on a cost function so that the expected state parameter is close to the target state parameter of the mobile device, thereby obtaining an acceleration control instruction, wherein the cost function includes a state parameter difference term, and the state parameter difference term is expressed as follows:

[0071] j=(X f ―Xtarget ) T H (X f ―X target )

[0072] Among them, j represents the cost value of the cost function, X target represents the target state parameter, X f represents the expected state parameter, and H represents the first preset parameter matrix.

[0073] Specifically, the expected state parameter can be optimized based on the cost function. For a specific acceleration change function, the expected state parameter X0 can be obtained according to the current state parameter X0 and the acceleration change function u. f For example, the expected state parameter is obtained by integrating the acceleration change function. The expected state parameter and the target state parameter are substituted into the above cost function to obtain the cost value corresponding to the acceleration change function. It can be seen from the above cost function that the greater the difference between the expected state parameter and the target state parameter, the greater the value of the state parameter difference term, that is, the cost value of the cost function. By adjusting the acceleration change function, the expected state parameter with the optimal cost value can be found, and the acceleration change function corresponding to the expected state parameter is the optimal acceleration change function. Here, the expected state parameter with the optimal cost value can be the expected state parameter with the smaller or smallest cost value. That is, through continuous optimization, the difference between the expected state parameter and the target state parameter can be made as small as possible.

[0074] In one example, the first preset parameter matrix may be a positive definite matrix, which can ensure that the value of the state parameter difference term is greater than or equal to 0. The specific value of each element in the first preset parameter matrix can be set according to actual needs.

[0075] In this embodiment, by setting the cost function to realize the optimization process of the expected state parameters, the optimization effect can be improved to a certain extent. In addition, for different models or types of mobile devices, the optimization process corresponding to the specific model or type of mobile device can be performed by changing some parameters in the cost function (such as the first preset parameter matrix), which can improve the flexibility of the mobile device control method.

[0076] According to an embodiment of the present application, a preset acceleration change function includes an acceleration change sequence, wherein the expected state parameters are optimized based on a cost function so that the expected state parameters are close to the target state parameters of the mobile device, thereby obtaining an acceleration control instruction, including: optimizing the expected state parameters based on the cost function so that the expected state parameters are close to the target state parameters of the mobile device, and obtaining a state parameter change gradient; obtaining an updated acceleration change sequence based on the state parameter change gradient; obtaining an updated expected state parameter based on the updated acceleration change sequence and the current state parameter; and iteratively executing the optimization process according to the updated expected state parameter and the cost function to obtain an optimized acceleration change sequence as an acceleration control instruction.

[0077] Specifically, the acceleration change function may include an acceleration change sequence, and the acceleration change sequence may include multiple acceleration parameters, each acceleration parameter may correspond to a certain time period, and each acceleration parameter here may be a specific value. In some cases, the acceleration change sequence may be represented by a matrix, that is, it may be the above-mentioned acceleration change matrix u.

[0078] In one example, the expected state parameters can be obtained based on the current state parameters and the preset acceleration change sequence. For example, the expected state parameters can be obtained by integration. Here, the preset acceleration change sequence can be an initial acceleration change sequence, which can be set manually. Substituting the expected state parameters into the cost function can obtain the corresponding cost value, so that the first iteration process can be completed. According to the size of the cost value, the preset acceleration change sequence can be adjusted to reduce the cost value obtained in the subsequent iteration process. For example, the state parameter change gradient can be obtained according to the size of the cost value. This process can be achieved through certain operation rules, or the state parameter change gradient can be determined artificially according to the size of the cost value.

[0079] The acceleration change sequence can be updated based on the state parameter change gradient to obtain an updated acceleration change sequence. In one example, since there is a certain correlation between the state parameter and the acceleration change sequence, that is, the acceleration change sequence can affect the change of the state parameter, the acceleration change sequence can be updated based on the state parameter change gradient based on certain rules. For example, the acceleration change gradient can be obtained based on the state parameter change gradient, and the acceleration change sequence is updated based on the acceleration change gradient. In one example, the Taylor expansion method or other methods can be used for local nonlinear optimization to obtain the state parameter change gradient. Of course, the method for updating the acceleration change sequence based on the state parameter change gradient can be set according to actual needs, and the embodiments of the present application are not limited to this.

[0080] Based on the updated acceleration change sequence and the current state parameters, the updated expected state parameters can be obtained. Substituting the updated expected state parameters into the cost function can obtain the corresponding cost value, that is, the second iteration process is completed; this can be repeated to complete multiple iterations. Each iteration can make the difference between the expected state parameters and the target state parameters tend to become smaller.

[0081] In one example, an optimization condition can be set for the optimization process of the expected state parameter. When the optimization process meets the optimization condition, the optimization process can be stopped, that is, the iterative process can be stopped, and the expected state parameter in the current iteration can be used as the optimal expected state parameter. The specific content of the optimization condition can be found in the description in the above embodiment. In another example, when the optimization process meets the optimization condition, the expected state parameter with the smallest cost value in multiple iterations can be used as the optimal expected state parameter. The acceleration change sequence corresponding to the optimal expected state parameter is the optimal acceleration change sequence. The optimal acceleration change sequence can be used as an acceleration control instruction.

[0082] In this embodiment, the desired state parameters are optimized through the cost function to obtain the state parameter change gradient, and an updated acceleration change sequence is obtained based on the state parameter change gradient. Then, the optimization process is iteratively performed based on the updated acceleration change sequence, so that the optimal acceleration change sequence can be obtained, thereby improving the optimization effect.

[0083] According to an embodiment of the present application, step 230 may include: optimizing the desired state parameter based on a cost function so that the desired state parameter is close to the target state parameter of the mobile device, thereby obtaining an acceleration control instruction, wherein the cost function is expressed as follows:

[0084]

[0085] Among them, J represents the cost value of the cost function, X target represents the target state parameter, X f represents the expected state parameter, H represents the first preset parameter matrix, t0 represents the current time corresponding to the current state parameter, t f represents the moment corresponding to the expected state parameter, u represents the preset acceleration change function, and R represents the second preset parameter matrix.

[0086] Specifically, the first item of the cost function in this embodiment is the above-mentioned state parameter difference item. The specific content of this item can be found in the description of the above-mentioned embodiment. To avoid repetition, it will not be repeated here. The second item of the cost function in this embodiment is an integral function related to the acceleration change function, which can be used to characterize the power consumption of the mobile device.

[0087] In one example, the second preset parameter matrix can also be a positive definite matrix, so that the value of the second term of the cost function can be guaranteed to be greater than or equal to 0. The specific values ​​of each element in the second preset parameter matrix can be set according to actual needs. In one example, the acceleration change function can include the acceleration change sequence mentioned in the above embodiment.

[0088] In one example, the expected state parameters can be obtained based on the current state parameters and the preset acceleration change function. For example, the expected state parameters can be obtained by integration. Here, the preset acceleration change function can be an initial acceleration change function, which can be set manually. Substituting the expected state parameters and the acceleration change function into the cost function can obtain the corresponding cost value, so that the first iteration process can be completed. According to the size of the cost value, the preset acceleration change function can be adjusted to reduce the cost value obtained in the subsequent iteration process. For example, the state parameter change gradient can be obtained according to the size of the cost value, and the process of obtaining the state parameter change gradient can refer to the description in the above embodiment.

[0089] The acceleration change function can be updated based on the state parameter change gradient to obtain an updated acceleration change function. In one example, the acceleration change gradient can be obtained based on the state parameter change gradient, and the acceleration change function can be updated based on the acceleration change gradient. In one example, the Taylor expansion method or other methods can be used for local nonlinear optimization to obtain the state parameter change gradient. Of course, the method for updating the acceleration change function based on the state parameter change gradient can be set according to actual needs, and the embodiments of the present application are not limited to this.

[0090] Based on the updated acceleration change function and the current state parameters, the updated expected state parameters can be obtained. Substituting the updated expected state parameters and the updated acceleration change function into the cost function can obtain the corresponding cost value, that is, the second iteration process is completed; this can be repeated to complete multiple iterations. Each iteration can make the difference between the expected state parameters and the target state parameters tend to decrease and the power consumption of the mobile device tend to decrease.

[0091] In one example, an optimization condition can be set for the optimization process of the desired state parameter. When the optimization process meets the optimization condition, the optimization process can be stopped, that is, the iterative process can be stopped, and the acceleration change function in the current iteration can be used as the optimal acceleration change function. The specific content of the optimization condition can be found in the description of the above embodiment. In another example, when the optimization process meets the optimization condition, the acceleration change function with the smallest cost value in multiple iterations can be used as the optimal acceleration change function. The acceleration control instruction can be obtained based on the optimal acceleration change function.

[0092] In this embodiment, by setting the second item related to the acceleration change function in the cost function, the optimal acceleration change function can be found through continuous optimization, and the optimal acceleration change function can achieve the effect of minimizing power consumption and minimizing the difference between the expected state parameter and the target state parameter. In addition, by setting appropriate time constraints for the cost function, it is also convenient to achieve a balance between power consumption and time consumption, that is, the optimal acceleration change function obtained by optimization can achieve the effect of minimizing power consumption and minimizing time consumption. For example, when the mobile device is a vehicle, based on the optimal acceleration change function, the mobile device can be controlled to be more fuel-efficient and move to the target position point in a shorter time, so that the intelligence level of the mobile device movement process can be further improved.

[0093] According to an embodiment of the present application, step 230 may include: optimizing the desired state parameter based on the constraint condition so that the desired state parameter is close to the target state parameter of the mobile device, thereby obtaining the acceleration control instruction of the mobile device. The constraint condition includes at least one of the following: the linear velocity parameter of the mobile device is less than or equal to the preset velocity threshold, the angular velocity parameter of the mobile device is less than or equal to the first preset angular velocity threshold, the acceleration parameter of the mobile device is less than or equal to the preset acceleration threshold, the steering center of the mobile device is within a preset range, and the angular velocity parameter of the steering wheel of the mobile device is less than or equal to the second preset angular velocity threshold.

[0094] Specifically, the specific values ​​of the preset speed threshold, the first preset angular velocity threshold, the preset acceleration threshold and the second preset angular velocity threshold, and the preset range corresponding to the turning center of the mobile device can be determined based on at least one of the structural parameters, performance parameters, scene characteristics and other factors of the mobile device.

[0095] In one example, the linear velocity parameter v of the mobile device may be less than or equal to a preset velocity threshold v max , that is, v≤v max Here, the linear velocity parameter v can represent the sum of the linear velocity of the mobile device in the first direction and the linear velocity in the second direction, that is, represent a combined velocity, such as Figure 3 As shown, Figure 3 The point O in is the geometric center of the mobile device.

[0096] In one example, the angular velocity parameter w of the mobile device is z can be less than or equal to the first preset angular velocity threshold w max , that is, w z ≤w max .

[0097] In one example, the acceleration parameter a of the mobile device may be less than or equal to a preset acceleration threshold a. max , that is, a≤amax Here, the acceleration parameter a can represent the sum of the acceleration of the mobile device in the first direction and the acceleration in the second direction, that is, represent a combined acceleration, such as Figure 3 shown.

[0098] In one example, the turning center of the mobile device is within a preset range, and the preset range can be determined by the structural parameters of the mobile device itself. Figure 3 As shown, the turning radius corresponding to the turning center of the mobile device can be represented by R'. In a specific linear velocity and angular velocity scenario, the preset range corresponding to the turning center D' of the mobile device is Figure 3 If the normal movement of the mobile device is to be ensured, the turning center of the mobile device during the movement should be limited to be within a preset range. In one example, the following formula can be used to limit the turning center of the mobile device to be within the preset range:

[0099] Cv+Dw z ≤0

[0100] Among them, C and D are parameter matrices, such as constant matrices, and C and D can be determined according to the structural parameters of the mobile device, that is, C and D are related to the specific structure of the mobile device. Specifically, C and D can be determined according to the position of the steering wheel of the mobile device, such as the position of the axis of the steering wheel. It should be understood that when the steering center of the mobile device is located on different sides of the mobile device (such as the left side or the right side), the expression of the above formula is different, and it can be set according to the actual situation and actual needs.

[0101] In one example, the angular velocity parameter w of the steering wheel of the mobile device is a Less than or equal to the second preset angular velocity threshold w wheel_max , that is, w a ≤w wheel_max In one example, the number of steering wheels of the mobile device may be greater than or equal to 3. For example, the number of steering wheels of the mobile device may be 3, 4, 5 or more. For each steering wheel, the angular velocity parameter w of the steering wheel is a can correspond to a second preset angular velocity threshold w wheel_max The second preset angular velocity threshold corresponding to each steering wheel may be the same or different.

[0102] In one example, the angular velocity parameter w of the steering wheel is a It can be obtained according to the kinematic principle or the mathematical limit principle. For example, the angular velocity parameter w of the steering wheel can be obtained by the following formula a .

[0103] v a =v+w z × a

[0104]

[0105] v a is the linear velocity parameter of the steering wheel, that is, the translation velocity parameter at the axis of the steering wheel, which is related to the linear velocity parameter v and angular velocity parameter w of the mobile device z Related. a is a system parameter, which can be determined by the structural parameters of the mobile device, and can be the offset vector of the axis of the steering wheel relative to the geometric center of the mobile device, such as Figure 3 shown. is the derivative of the linear velocity parameter of the steering wheel, is the derivative of the linear velocity parameter of the mobile device, w z is the derivative of the angular velocity parameter of the mobile device, is the derivative of the offset vector.

[0106] In this embodiment, the desired state parameters are optimized based on the constraints, which can ensure the effectiveness of the optimal acceleration change function finally obtained. The acceleration control instructions obtained based on the optimal acceleration change function are used to control the mobile device, which can improve the safety of the mobile device's movement process.

[0107] According to an embodiment of the present application, step 240 may include: determining the speed parameter of the steering wheel of the mobile device according to the acceleration control instruction and the current state parameter; and controlling the steering wheel motor corresponding to the steering wheel according to the speed parameter of the steering wheel to control the mobile device.

[0108] In one example, the state parameters of the mobile device at each moment can be determined according to the acceleration control instruction and the current state parameters, that is, the speed parameters and posture parameters of the mobile device at each moment can be determined. According to the speed parameters of the mobile device at each moment, the speed parameters of the steering wheel at each moment can be determined, so that the steering wheel motors corresponding to each steering wheel can be controlled to work, thereby controlling the mobile device to move to the target position point.

[0109] In this embodiment, the speed parameters of the steering wheel of the mobile device are determined according to the acceleration control instruction and the current state parameters. In this way, the movement of the mobile device can be controlled by controlling the movement of the steering wheel. Therefore, the embodiment of the present application can be applied to a mobile device with an active wheel to achieve precise control of the movement process of the mobile device.

[0110] Figure 4 Shown is a flow chart of a mobile device control method provided by another exemplary embodiment of the present application. Figure 4 The embodiment is Figure 2 For the example of the embodiment, in order to avoid repetition, the same points can be referred to the description in the above embodiment, which will not be repeated here. Figure 4As shown, the mobile device control method may include the following contents.

[0111] 410: Get current status parameters of the mobile device.

[0112] The current state parameters include the current position parameters and current speed parameters of the mobile device.

[0113] 420: Determine a state parameter change trend function based on the current state parameter and a preset acceleration change function, and determine an expected state parameter according to the state parameter change trend function.

[0114] The state parameter change trend function is used to characterize the change trend of the state parameters of the mobile device over time. The specific content of the state parameter change trend function and the specific process of determining the expected state parameters based on the state parameter change trend function can be found in the description of the above embodiment, and will not be repeated here to avoid repetition.

[0115] 430: Based on the cost function and the corresponding constraint conditions, the expected state parameters are optimized so that the expected state parameters are close to the target state parameters of the mobile device, thereby obtaining an acceleration control instruction.

[0116] The cost function may include a first item and a second item, the first item may be a state parameter difference item, and the second item may be an integral function related to the acceleration change function. It should be understood that the specific content of the cost function, the specific content of the constraint conditions, and the specific optimization process can be found in the description of the above embodiment, and will not be repeated here to avoid repetition.

[0117] 440: Control the mobile device according to the acceleration control instruction and the current state parameter.

[0118] Exemplary Devices

[0119] Figure 5 FIG. 1 is a schematic diagram of the structure of a mobile device control device provided by an exemplary embodiment of the present application. Figure 5 As shown, the mobile device control apparatus 500 includes: an acquisition module 510 , a determination module 520 , an optimization module 530 , and a control module 540 .

[0120] The acquisition module 510 is used to acquire the current state parameters of the mobile device, wherein the current state parameters include the current posture parameters and the current speed parameters of the mobile device. The determination module 520 is used to determine the expected state parameters of the mobile device based on the current state parameters. The optimization module 530 is used to optimize the expected state parameters so that the expected state parameters are close to the target state parameters of the mobile device, thereby obtaining the acceleration control instructions of the mobile device. The control module 540 is used to control the mobile device according to the acceleration control instructions and the current state parameters.

[0121] The embodiment of the present application provides a mobile device control device, which determines the expected state parameters of the mobile device according to the current state parameters of the mobile device, and optimizes the expected state parameters so that the expected state parameters are close to the target state parameters of the mobile device, thereby obtaining an acceleration control instruction of the mobile device, and then the mobile device can be controlled according to the acceleration control instruction and the current state parameters. In the embodiment of the present application, the state parameters of the mobile device may include posture parameters and speed parameters. The embodiment of the present application obtains the acceleration control instruction through the optimization process, so that the planned state parameters can be obtained based on the current state parameters and the acceleration control instruction, and then the movement of the mobile device can be controlled based on the planned state parameters, which can improve the intelligence of the mobile device movement process.

[0122] According to an embodiment of the present application, the determination module 520 is used to determine the expected state parameters based on the current state parameters and a preset acceleration change function.

[0123] According to one embodiment of the present application, the determination module 520 is used to: determine a state parameter change trend function based on current state parameters and a preset acceleration change function, wherein the state parameter change trend function is used to characterize the change trend of the state parameters of the mobile device over time; and determine the expected state parameters according to the state parameter change trend function.

[0124] According to an embodiment of the present application, the optimization module 530 is used to optimize the expected state parameters based on the cost function so that the expected state parameters are close to the target state parameters of the mobile device, thereby obtaining the acceleration control instruction, wherein the cost function includes a state parameter difference term, and the state parameter difference term is expressed as follows:

[0125] j=(X f ―X target )TH(X f ―X target )

[0126] Among them, j represents the cost value of the cost function, X target represents the target state parameter, X f represents the expected state parameter, and H represents the first preset parameter matrix.

[0127] According to an embodiment of the present application, the preset acceleration change function includes an acceleration change sequence, wherein the optimization module 530 is used to: optimize the expected state parameters based on the cost function so that the expected state parameters are close to the target state parameters of the mobile device, and obtain the state parameter change gradient; obtain an updated acceleration change sequence based on the state parameter change gradient; obtain an updated expected state parameter based on the updated acceleration change sequence and the current state parameter; iteratively execute the optimization process according to the updated expected state parameters and the cost function to obtain the optimized acceleration change sequence as the acceleration control instruction.

[0128] According to an embodiment of the present application, the optimization module 530 is used to optimize the desired state parameters based on the cost function so that the desired state parameters are close to the target state parameters of the mobile device, thereby obtaining the acceleration control instruction, wherein the cost function is expressed as follows:

[0129]

[0130] Among them, J represents the cost value of the cost function, X target represents the target state parameter, X f represents the expected state parameter, H represents the first preset parameter matrix, t0 represents the current time corresponding to the current state parameter, t f represents the moment corresponding to the expected state parameter, u represents the preset acceleration change function, and R represents the second preset parameter matrix.

[0131] According to an embodiment of the present application, the optimization module 530 is used to optimize the expected state parameters based on the constraint conditions so that the expected state parameters are close to the target state parameters of the mobile device, thereby obtaining the acceleration control instruction of the mobile device. The constraint conditions include at least one of the following: the linear velocity parameter of the mobile device is less than or equal to the preset velocity threshold, the angular velocity parameter of the mobile device is less than or equal to the first preset angular velocity threshold, the acceleration parameter of the mobile device is less than or equal to the preset acceleration threshold, the steering center of the mobile device is within the preset range, and the angular velocity parameter of the steering wheel of the mobile device is less than or equal to the second preset angular velocity threshold.

[0132] According to an embodiment of the present application, the control module 540 is used to: determine the speed parameter of the steering wheel of the mobile device according to the acceleration control instruction and the current state parameter; and control the steering wheel motor corresponding to the steering wheel according to the speed parameter of the steering wheel to control the mobile device.

[0133] It should be understood that the operations and functions of the acquisition module 510, the determination module 520, the optimization module 530, and the control module 540 in the above embodiment can refer to the above Figure 2 or Figure 4The description of the mobile device control method provided in the embodiment will not be repeated here to avoid repetition.

[0134] An embodiment of the present application further provides a mobile device, which includes a control module, and the control module is used to execute the mobile device control method provided in any of the above embodiments.

[0135] In some embodiments, the mobile device may be a robot with a steering wheel, or a vehicle or other device that uses a steering wheel to move. Specifically, the mobile device further includes at least three steering wheels, and the at least three steering wheels are all active wheels.

[0136] The operation and functions of the mobile device provided in the embodiments of the present application can refer to the above Figure 2 or Figure 4 The description of the mobile device control method provided in the embodiment will not be repeated here to avoid repetition.

[0137] Figure 6 The block diagram of an electronic device 600 for executing a mobile device control method provided by an exemplary embodiment of the present application is shown. The electronic device 600 may be a mobile device, a control device of a mobile device, a server interacting with a mobile device, or other devices.

[0138] Reference Figure 6 , the electronic device 600 includes a processing component 610, which further includes one or more processors, and a memory resource represented by a memory 620 for storing instructions executable by the processing component 610, such as an application. The application stored in the memory 620 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 610 is configured to execute the instructions to perform the above-mentioned mobile device control method.

[0139] The electronic device 600 may also include a power supply component configured to perform power management of the electronic device 600, a wired or wireless network interface configured to connect the electronic device 600 to a network, and an input / output (I / O) interface. The electronic device 600 may be operated based on an operating system stored in the memory 620, such as Windows Server 2000. TM , Mac OSX TM , Unix TM , Linux TM , FreeBSD TM or similar.

[0140] A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of the electronic device 600, enables the electronic device 600 to execute a mobile device control method.

[0141] A computer program product includes a computer program. When the computer program is executed by a processor of a computer device, the computer device is enabled to execute the mobile device control method provided in any of the above embodiments.

[0142] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present application, and will not be described one by one here.

[0143] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0144] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0145] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0146] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0147] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0148] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program check codes.

[0149] It should be noted that, in the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" is two or more.

[0150] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0151] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for controlling a mobile device, characterized in that: include: Acquire current state parameters of the mobile device, wherein the current state parameters include current posture parameters and current speed parameters of the mobile device; determining a desired state parameter of the mobile device based on the current state parameter; Optimizing the expected state parameter so that the expected state parameter is close to the target state parameter of the mobile device, thereby obtaining an acceleration control instruction of the mobile device; The mobile device is controlled according to the acceleration control instruction and the current state parameter.

2. The mobile device control method according to claim 1, characterized in that: The determining the expected state parameter of the mobile device based on the current state parameter comprises: The expected state parameter is determined based on the current state parameter and a preset acceleration change function.

3. The mobile device control method according to claim 2, characterized in that: The determining the expected state parameter based on the current state parameter and a preset acceleration change function includes: Determine a state parameter change trend function based on the current state parameter and the preset acceleration change function, wherein the state parameter change trend function is used to characterize a change trend of the state parameter of the mobile device over time; The expected state parameter is determined according to the state parameter change trend function.

4. The mobile device control method according to claim 2, characterized in that: The optimizing the expected state parameter so that the expected state parameter is close to the target state parameter of the mobile device, thereby obtaining the acceleration control instruction of the mobile device, includes: The desired state parameter is optimized based on a cost function so that the desired state parameter is close to a target state parameter of the mobile device, thereby obtaining the acceleration control instruction, wherein the cost function includes a state parameter difference term, and the state parameter difference term is expressed as follows: j=(X f -X target ) T H(X f -X target ) Wherein, j represents the cost value of the cost function, X target represents the target state parameter, X f represents the expected state parameter, and H represents the first preset parameter matrix.

5. The mobile device control method according to claim 4, characterized in that: The preset acceleration change function includes an acceleration change sequence, wherein the optimizing the expected state parameter based on the cost function so that the expected state parameter is close to the target state parameter of the mobile device, thereby obtaining the acceleration control instruction, includes: Optimizing the expected state parameter based on the cost function so that the expected state parameter is close to the target state parameter of the mobile device, and obtaining a state parameter change gradient; Obtaining an updated acceleration change sequence based on the state parameter change gradient; Obtaining updated expected state parameters based on the updated acceleration change sequence and the current state parameters; An optimization process is iteratively performed according to the updated desired state parameters and the cost function to obtain an optimized acceleration change sequence as the acceleration control instruction.

6. The mobile device control method according to claim 2, characterized in that: The optimizing the expected state parameter so that the expected state parameter is close to the target state parameter of the mobile device, thereby obtaining the acceleration control instruction of the mobile device, includes: The desired state parameter is optimized based on a cost function so that the desired state parameter is close to the target state parameter of the mobile device, thereby obtaining the acceleration control instruction, wherein the cost function is expressed as follows: Wherein, J represents the cost value of the cost function, X target represents the target state parameter, X f represents the expected state parameter, H represents the first preset parameter matrix, t0 represents the current time corresponding to the current state parameter, t f represents the moment corresponding to the expected state parameter, u represents the preset acceleration change function, and R represents the second preset parameter matrix.

7. The mobile device control method according to claim 1, wherein the optimizing the expected state parameter so that the expected state parameter is close to the target state parameter of the mobile device, thereby obtaining the acceleration control instruction of the mobile device, comprises: The desired state parameter is optimized based on the constraint condition so that the desired state parameter is close to the target state parameter of the mobile device, thereby obtaining an acceleration control instruction of the mobile device. The constraint condition includes at least one of the following: The linear speed parameter of the mobile device is less than or equal to a preset speed threshold, The angular velocity parameter of the mobile device is less than or equal to a first preset angular velocity threshold, The acceleration parameter of the mobile device is less than or equal to a preset acceleration threshold, The turning center of the mobile device is within a preset range, The angular velocity parameter of the steering wheel of the mobile device is less than or equal to a second preset angular velocity threshold.

8. The mobile device control method according to any one of claims 1 to 7, characterized in that: The step of controlling the mobile device according to the acceleration control instruction and the current state parameter includes: Determining a speed parameter of a steering wheel of the mobile device according to the acceleration control instruction and the current state parameter; The steering wheel motor corresponding to the steering wheel is controlled according to the speed parameter of the steering wheel to control the mobile device.

9. A mobile device, characterized in that: It comprises a control module, wherein the control module is used to execute the mobile device control method according to any one of claims 1 to 8.

10. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor, The processor is used to execute the mobile device control method described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the mobile device control method according to any one of claims 1 to 8.

12. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor of a computer device, the computer device is enabled to execute the mobile device control method according to any one of claims 1 to 8.

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