阅读说明：本技术 一种基于梯度下降的轮式移动机器人状态调整方法 (Wheeled mobile robot state adjusting method based on gradient descent ) 是由 郭东生 李煊鲜 刘庆平 黎子豪 殷海波 于 2021-09-28 设计创作，主要内容包括：本发明公开了一种基于梯度下降的轮式移动机器人状态调整方法,包括：根据最小化轮式移动机器人当前状态与期望调整状态之间的偏差,基于梯度下降公式,设计在速度层上描述的新型性能指标；最小化新型性能指标,建立速度层状态调整方案；将速度层状态调整方案转化为二次型优化问题；采用递归神经网络对二次型优化问题进行求解,输出轮式移动机器人期望调整状态的求解结果；根据求解结果,下位机控制器驱动移动平台的双轮和机器人的关节使得轮式移动机器人调整到期望的状态。该方法可以同时调整移动平台和机器人,使其快速、准确的到达期望调整状态,操作方便、工作量少、作业规范且调整效果准确高效。(The invention discloses a wheeled mobile robot state adjusting method based on gradient descent, which comprises the following steps: designing a novel performance index described on a speed layer based on a gradient descent formula according to the deviation between the current state and the expected adjustment state of the minimized wheeled mobile robot; minimizing a novel performance index, and establishing a speed layer state adjustment scheme; converting a speed layer state adjustment scheme into a quadratic optimization problem; solving the quadratic optimization problem by adopting a recurrent neural network, and outputting a solving result of the expected adjustment state of the wheeled mobile robot; and according to the solution result, the lower computer controller drives the double wheels of the mobile platform and the joints of the robot so that the wheel type mobile robot is adjusted to a desired state. The method can adjust the mobile platform and the robot at the same time, so that the mobile platform and the robot can quickly and accurately reach the expected adjustment state, and the method is convenient to operate, low in workload, standard in operation and accurate and efficient in adjustment effect.)

1. A wheeled mobile robot state adjusting method based on gradient descent is characterized by comprising the following steps:

s1, designing a novel performance index described on a speed layer based on a gradient descent formula according to the deviation between the current state and the expected adjustment state of the minimized wheeled mobile robot;

s2, minimizing the novel performance index, and establishing a speed layer state adjustment scheme;

s3, converting the speed layer state adjustment scheme into a quadratic optimization problem;

s4, solving the quadratic optimization problem by adopting a recurrent neural network, and outputting a solving result of the expected adjustment state of the wheeled mobile robot;

and S5, driving the double wheels of the mobile platform and the joints of the robot by the lower computer controller according to the solving result to adjust the wheeled mobile robot to a desired state.

2. The method for adjusting the state of a wheeled mobile robot based on gradient descent according to claim 1, wherein the wheeled mobile robot comprises a mobile platform driven by two wheels and a robot having n degrees of freedom mounted on the mobile platform; in step S1, based on the gradient descent formula, the new performance index described in the velocity layer is designed as follows:

wherein | · | purple sweet₂A two-norm representation of a vector;represents an augmented position vector of the wheeled mobile robot,P_xand P_yRespectively representing the position of the mobile platform on a horizontal ground in the X-axis and Y-axis directions, p_x∈R，p_yE is R; phi represents the orientation angle of the mobile platform, and phi belongs to R; theta represents a joint angle of the wheeled mobile robot, and theta is equal to Rⁿ；Represents an augmented velocity vector of the wheeled mobile robot,andrespectively represents p_x、p_yAnd the time derivative of phi, and,indicates the joint speed of the wheeled mobile robot,k represents the adjustment performance index parameter, and k is more than 0 and belongs to R;a non-linear mapping is represented that is,P_xdindicating the desired position of the mobile platform on level ground in the direction of the X-axis, p_xd∈R；P_ydIndicating the desired position of the mobile platform on level ground in the direction of the Y-axis, p_yd∈R；φ_dRepresenting a desired orientation angle, phi, of the mobile platform on a level ground_d∈R；θ_dIndicating a desired joint angle, theta, of the wheeled mobile robot_d∈Rⁿ。

3. The method as claimed in claim 1, wherein in step S2, the new performance index is minimized, and the speed layer status adjustment scheme is established as follows:

and (3) minimizing:

constraint conditions are as follows:

θ^-≤θ≤θ⁺ (5)

wherein A represents the structure parameter of the mobile platform, and A belongs to R^3×2；A＝[rcos(φ)/2,rcos(φ)/2；rsin(φ)/2,rsin(φ)/2；-r/l,r/l]Phi represents the orientation angle of the mobile platform, and phi belongs to R; r represents the radius of the driving wheel of the mobile platform, and R is more than 0 and belongs to R; l represents the distance between the central points of the two driving wheels of the mobile platform, and l is more than 0 and belongs to R;the rotation angle of the dual-drive wheels of the mobile platform is shown, indicating the angular velocity of rotation of the dual drive wheels of the mobile platform, θ^±andrespectively representing the rotation angles of the dual driving wheels of the mobile platformAngular velocity of rotation of dual drive wheels of mobile platformJoint angle θ of wheeled mobile robot and joint speed of wheeled mobile robotThe limit of (c).

4. The wheeled mobile robot status adjusting method based on gradient descent according to claim 1, wherein in step S3, the velocity layer status adjusting scheme is converted into a quadratic optimization problem as follows:

and (3) minimizing: x is the number of^TQx/2+p^Tx (7)

Constraint conditions are as follows: x is the number of^-≤x≤x⁺ (8)

Wherein Q ═ D^TD∈R^(2+n)×(2+n)，D＝[A,0；0,I]∈R^(3+n)×(2+n)；k represents the parameter of adjusting performance index, k is more than 0 and belongs to R,representing a non-linear mapping; upper label^TRepresenting the transpose of a matrix or vector, A representing the structural parameters of the mobile platform, I representing the identity matrix, I ∈ R^n×n(ii) a x represents the variable to be solved for,x^±the limit of x is expressed as a function of, lambda represents a limit conversion parameter, and lambda is larger than 0 and belongs to R; u denotes an extended angle vector of the wheeled mobile robot, represents an augmented velocity vector of the wheeled mobile robot,u^±andrespectively represent u andin the case of the above-mentioned (c),

5. the wheeled mobile robot state adjustment method based on gradient descent according to claim 1, wherein the quadratic optimization problem is solved by using a recurrent neural network in step S4 as follows: and converting the quadratic optimization problem into a piecewise linear projection equation, and solving the piecewise linear projection equation by adopting a recurrent neural network.

6. The wheeled mobile robot state adjustment method based on gradient descent as claimed in claim 5, wherein the quadratic optimization problem is converted into a piecewise linear projection equation as follows:

P_Ω(x-(Qx+p))-x＝0∈R²⁺ⁿ， (9)

wherein x represents a variable to be solved; q ═ D^TD∈R^(2+n)×(2+n)，D＝[A,0；0,I]∈R^(3+n)×(2+n)A represents the structure parameter of the mobile platform, I represents the unit matrix;k represents the parameter of adjusting performance index, k is more than 0 and belongs to R,representing a non-linear mapping; p_Ω(. cndot.) represents a piecewise linear projection operator.

7. The wheeled mobile robot state adjusting method based on gradient descent as claimed in claim 5, wherein the solving of the piecewise linear projection equation by using a recurrent neural network is as follows:

wherein the content of the first and second substances,representing the time derivative of x, x representing the variable to be solved; mu represents a design parameter, mu is more than 0 and belongs to R; i represents an identity matrix, I ∈ R^(2+n)×(2+n)；P_Ω(. h) represents a piecewise linear projection operator; q ═ D^TD∈R^(2+n)×(2+n)，D＝[A,0；0,I]∈R^{(3 +n)×(2+n)}A represents the structure parameter of the mobile platform, I represents the unit matrix;k represents the parameter of adjusting performance index, k is more than 0 and belongs to R,representing a non-linear mapping; upper label^TRepresenting a transpose of a matrix or vector.

Technical Field

The invention relates to the technical field of motion planning of wheeled mobile robots, in particular to a state adjustment method of a wheeled mobile robot based on gradient descent.

Background

The wheel type mobile robot consists of a mobile platform driven by double wheels and a robot with n degrees of freedom. The device has great flexibility and mobility, so that the device has great operation space and is widely applied to various fields such as article carrying, fire scene search and rescue, space exploration and the like. When a mobile robot performs different planning tasks in a workspace, it is often necessary to adjust from the current structural state to a specified/desired state after a task is completed. I.e. the start state when the next task is executed.

Generally, adjusting a wheeled mobile robot from a current state to a desired state is performed in steps: the state of the mobile platform is adjusted first, and then the state of the robot is adjusted. However, this method is cumbersome, time consuming and has significant drawbacks. Each adjustment requires multiple measurements of the position and orientation angle of the mobile platform on a horizontal surface and the respective joint angles of the robot in the working space in order for the mobile robot to accurately reach the specified/desired state to perform the relevant task. Further, inaccurate state adjustment may also result in the mobile robot failing to successfully complete the specified task.

Therefore, in addition to the conventional motion adjustment of the wheeled mobile robot, how to provide a method for adjusting the state of the wheeled mobile robot based on gradient descent so as to quickly and accurately adjust the wheeled mobile robot from the current state to a desired state is a problem that needs to be solved by those skilled in the art.

Disclosure of Invention

In view of the above problems, the present invention provides a method for adjusting a state of a wheeled mobile robot based on gradient descent, which at least solves some of the above technical problems, and the method can efficiently achieve automatic adjustment of the wheeled mobile robot between different states, and is fast, accurate, convenient to operate, low in workload, and standard in operation.

The embodiment of the invention provides a wheeled mobile robot state adjusting method based on gradient descent, which comprises the following steps:

s1, designing a novel performance index described on a speed layer based on a gradient descent formula according to the deviation between the current state and the expected adjustment state of the minimized wheeled mobile robot;

s2, minimizing the novel performance index, and establishing a speed layer state adjustment scheme;

s3, converting the speed layer state adjustment scheme into a quadratic optimization problem;

s4, solving the quadratic optimization problem by adopting a recurrent neural network, and outputting a solving result of the expected adjustment state of the wheeled mobile robot;

and S5, driving the double wheels of the mobile platform and the joints of the robot by the lower computer controller according to the solving result to adjust the wheeled mobile robot to a desired state.

Furthermore, the wheel type mobile robot consists of a mobile platform driven by double wheels and a robot which is arranged on the mobile platform and has n degrees of freedom; in step S1, based on the gradient descent formula, the new performance index described in the velocity layer is designed as follows:

wherein | · | purple sweet₂A two-norm representation of a vector;represents an augmented position vector of the wheeled mobile robot,P_xand P_yRespectively representing the position of the mobile platform on a horizontal ground in the X-axis and Y-axis directions, p_x∈R，p_yE is R; phi represents the orientation angle of the mobile platform, and phi belongs to R; theta represents a joint angle of the wheeled mobile robot, and theta is equal to Rⁿ；Represents an augmented velocity vector of the wheeled mobile robot, andrespectively represents p_x、p_yAnd the time derivative of phi, and,indicates the joint speed of the wheeled mobile robot,k represents the adjustment performance index parameter, and k is more than 0 and belongs to R;a non-linear mapping is represented that is,P_xdindicating the desired position of the mobile platform on level ground in the direction of the X-axis, p_xd∈R；P_ydIndicating the desired position of the mobile platform on level ground in the direction of the Y-axis, p_yd∈R；φ_dRepresenting a desired orientation angle, phi, of the mobile platform on a level ground_d∈R；θ_dIndicating a desired joint angle, theta, of the wheeled mobile robot_d∈Rⁿ。

Further, in step S2, minimizing the new performance index, and establishing a speed layer state adjustment scheme is:

and (3) minimizing:

restraint stripA piece:

wherein A represents the structure parameter of the mobile platform, and A belongs to R^3×2；A＝[rcos(φ)/2,rcos(φ)/2；rsin(φ)/2,rsin(φ)/2；-r/l,r/l]Phi represents the orientation angle of the mobile platform, and phi belongs to R; r represents the radius of the driving wheel of the mobile platform, and R is more than 0 and belongs to R; l represents the distance between the central points of the two driving wheels of the mobile platform, and l is more than 0 and belongs to R;the rotation angle of the dual-drive wheels of the mobile platform is shown, indicating the angular velocity of rotation of the dual drive wheels of the mobile platform, θ^±andrespectively representing the rotation angles of the dual driving wheels of the mobile platformAngular velocity of rotation of dual drive wheels of mobile platformJoint angle θ of wheeled mobile robot and joint speed of wheeled mobile robotThe limit of (c).

Further, in step S3, the velocity layer state adjustment scheme is converted into a quadratic optimization problem as follows:

and (3) minimizing: x is the number of^TQx/2+p^Tx (7)

Constraint conditions are as follows: x is the number of^-≤x≤x⁺ (8)

Wherein Q ═ D^TD∈R^(2+n)×(2+n)，D＝[A,0；0,I]∈R^(3+n)×(2+n)；k represents the parameter of adjusting performance index, k is more than 0 and belongs to R,representing a non-linear mapping; upper label^TRepresenting the transpose of a matrix or vector, A representing the structural parameters of the mobile platform, I representing the identity matrix, I ∈ R^n×n(ii) a x represents the variable to be solved for,x^±the limit of x is expressed as a function of, lambda represents a limit conversion parameter, and lambda is larger than 0 and belongs to R; u denotes an extended angle vector of the wheeled mobile robot, represents an augmented velocity vector of the wheeled mobile robot,u^±andrespectively represent u andin the case of the above-mentioned (c),

further, in step S4, the recurrent neural network is used to solve the quadratic optimization problem as follows: and converting the quadratic optimization problem into a piecewise linear projection equation, and solving the piecewise linear projection equation by adopting a recurrent neural network.

Further, converting the quadratic optimization problem into a piecewise linear projection equation is as follows:

P_Ω(x-(Qx+p))-x＝0∈R²⁺ⁿ， (9)

wherein x represents a variable to be solved; q ═ D^TD∈R^(2+n)×(2+n)，D＝[A,0；0,I]∈R^(3+n)×(2+n)A represents the structure parameter of the mobile platform, I represents the unit matrix;k represents the parameter of adjusting performance index, k is more than 0 and belongs to R,representing a non-linear mapping; p_Ω(. cndot.) represents a piecewise linear projection operator.

Further, the solving the piecewise linear projection equation by using the recurrent neural network is as follows:

wherein the content of the first and second substances,representing the time derivative of x, x representing the variable to be solved; mu represents a design parameter, mu is more than 0 and belongs to R; i represents an identity matrix, I ∈ R^(2+n)×(2+n)；P_Ω(. h) represents a piecewise linear projection operator; q ═ D^TD∈R^(2+n)×(2+n)，D＝[A,0；0,I]∈R^(3+n)×(2+n)A represents the structure parameter of the mobile platform, I represents the unit matrix;k represents the parameter of adjusting performance index, k is more than 0 and belongs to R,representing a non-linear mapping; upper label^TRepresenting a transpose of a matrix or vector.

The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:

the embodiment of the invention provides a wheeled mobile robot state adjusting method based on gradient descent, which comprises the following steps: designing a novel performance index described on a speed layer based on a gradient descent formula according to the deviation between the current state and the expected adjustment state of the minimized wheeled mobile robot; minimizing the novel performance index, and establishing a speed layer state adjustment scheme; converting the speed layer state adjustment scheme into a quadratic optimization problem; solving the quadratic optimization problem by adopting a recurrent neural network, and outputting a solving result of the expected adjustment state of the wheeled mobile robot; and according to the solving result, the lower computer controller drives the double wheels of the mobile platform and the joints of the robot so that the wheeled mobile robot is adjusted to a desired state. The method can adjust the mobile platform and the robot at the same time, so that the mobile platform and the robot can quickly and accurately reach the expected adjustment state, and the method is convenient to operate, low in workload, standard in operation and accurate and efficient in adjustment effect.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:

fig. 1 is a flowchart of a method for adjusting a state of a wheeled mobile robot based on gradient descent according to an embodiment of the present invention;

fig. 2 is a schematic diagram of an adjustment method according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

The embodiment of the invention provides a wheeled mobile robot state adjusting method based on gradient descent, which is shown in figure 1 and comprises the following steps:

s1, designing a novel performance index described on a speed layer based on a gradient descent formula according to the deviation between the current state and the expected adjustment state of the minimized wheeled mobile robot;

s2, minimizing the novel performance index, and establishing a speed layer state adjustment scheme;

s3, converting the speed layer state adjustment scheme into a quadratic optimization problem;

s4, solving the quadratic optimization problem by adopting a recurrent neural network, and outputting a solving result of the expected adjustment state of the wheeled mobile robot;

and S5, driving the double wheels of the mobile platform and the joints of the robot by the lower computer controller according to the solution result so that the wheel type mobile robot is adjusted to a desired state.

The state adjustment method for the wheeled mobile robot based on gradient descent provided by the embodiment effectively realizes automatic adjustment of the wheeled mobile robot among different states on a speed layer, and avoids a complex process that the wheeled mobile robot needs to measure the states of the mobile platform and the robot for many times when executing different planning tasks. The mobile platform and the robot can be adjusted simultaneously, so that the robot can quickly and accurately reach an expected adjustment state. The method has the advantages of convenient operation, less workload, standard operation and accurate and efficient adjustment effect.

Referring to fig. 2, the state adjustment method for the wheel-type mobile robot based on gradient descent mainly comprises six parts, namely designing a novel performance index 1 based on gradient descent, establishing a speed layer state adjustment scheme 2, converting into a quadratic optimization problem 3, a recurrent neural network solver 4, a lower computer controller 5 and a wheel-type mobile robot 6.

Firstly, designing a novel performance index described on a speed layer based on a gradient descent formula according to the idea of minimizing the deviation between the current state and the expected state of the mobile robot; then, a corresponding speed layer state adjustment scheme is established by combining with a novel performance index to be optimized, and the scheme is converted into a quadratic optimization problem, so that the quadratic optimization problem is solved by adopting a corresponding recurrent neural network; and finally, the solution result is used for driving the double wheels of the mobile platform and the joints of the robot, so that the mobile robot is quickly and accurately adjusted to a desired state. I.e. the starting state when different planning tasks are performed.

Specifically, the wheel type mobile robot consists of a double-wheel driven mobile platform and a robot which is arranged on the mobile platform and has n degrees of freedom.

The following is a detailed description of the above technical solution:

firstly, according to the idea of minimizing the deviation between the current state and the expected state of the wheeled mobile robot, a novel performance index described on a speed layer is designed based on a gradient descent formula:

wherein | · | purple sweet₂A two-norm representation of a vector;represents an augmented position vector of the wheeled mobile robot, P_xand P_yRespectively shows the positions of the mobile platform on the horizontal ground along the X-axis and Y-axis directions (and the positions of the base of the wheeled mobile robot fixed on the mobile platform), p_x∈R，p_yE is R; phi represents the orientation angle of the mobile platform, and phi belongs to R; theta represents a joint angle of the wheeled mobile robot, and theta is equal to Rⁿ；Represents an augmented velocity vector of the wheeled mobile robot, andrespectively represents p_x、p_yAnd the time derivative of phi, and,indicates the joint speed of the wheeled mobile robot,k represents a parameter for adjusting the performance index, the parameter is designed to be used for adjusting the performance index so as to enable the wheeled mobile robot to realize self-adjustment of the state, and k is greater than 0 and belongs to R;a non-linear mapping is represented that is,P_xdindicating the desired position of the mobile platform on level ground in the direction of the X-axis, p_xd∈R；P_ydIndicating the desired position of the mobile platform on level ground in the direction of the Y-axis, p_yd∈R；φ_dRepresenting a desired orientation angle, phi, of the mobile platform on a level ground_d∈R；θ_dIndicating a desired joint angle, theta, of the wheeled mobile robot_d∈Rⁿ。

Accordingly, the number of the first and second electrodes,indicating a desired state of the wheeled mobile robot. I.e. the starting state when different planning tasks are performed.

Secondly, minimizing the novel performance indexes, and establishing a corresponding speed layer state adjustment scheme:

and (3) minimizing:

constraint conditions are as follows:

wherein the equality constrainsRepresenting the kinematic equations of the moving platform,is formed byThe first three elements of (a) are,represents an augmented velocity vector of the wheeled mobile robot,namely, it isA represents the structure parameter of the mobile platform, and A belongs to R^3×2The method is composed of structural parameters based on a mobile platform:

A＝[rcos(φ)/2,rcos(φ)/2；rsin(φ)/2,rsin(φ)/2；-r/l,r/l]phi represents the orientation angle of the mobile platform, and phi belongs to R; r represents the radius of the driving wheel of the mobile platform, and R is more than 0 and belongs to R; l represents the distance between the central points of the two driving wheels of the mobile platform, and l is more than 0 and belongs to R;the rotation angle of the dual-drive wheels of the mobile platform is shown, indicating the angular velocity of rotation of the dual drive wheels of the mobile platform, θ^±andrespectively representing the rotation angles of the dual driving wheels of the mobile platformAngular velocity of rotation of dual drive wheels of mobile platformJoint angle θ of wheeled mobile robot and joint speed of wheeled mobile robotThe limit of (c).

Wherein the state adjustment scheme is constrained by a kinematic equation of the mobile platform, a rotation angle limit and a rotation angular velocity limit of a dual-drive wheel of the mobile platform, and an angle limit and a velocity limit of a robot joint.

Further, an augmented angle vector and an augmented speed vector of the wheeled mobile robot are definedAre respectively asAndaccordingly, the number of the first and second electrodes,andrespectively represent u andthe limit of (c).

Definition ofThe above speed layer state adjustment schemes (1) to (6) can be converted into the following quadratic optimization problem:

and (3) minimizing: x is the number of^TQx/2+p^Tx (7)

Constraint conditions are as follows: x is the number of^-≤x≤x⁺ (8)

Wherein Q ═ D^TD∈R^(2+n)×(2+n)，D＝[A,0；0,I]∈R^(3+n)×(2+n)；k represents the parameter of adjusting performance index, k is more than 0 and belongs to R,representing a non-linear mapping; upper label^TRepresenting the transpose of a matrix or vector, A representing the structural parameters of the mobile platform, I representing the identity matrix, I ∈ R^n×n(ii) a x represents the variable to be solved (i.e. the decision variable representing the quadratic optimization problem at this time),x^±pole representing xThe limit is that the temperature of the molten steel is limited,lambda represents a limit conversion parameter, and lambda is larger than 0 and belongs to R; u denotes an extended angle vector of the wheeled mobile robot, represents an augmented velocity vector of the wheeled mobile robot,u^±andrespectively represent u andin the case of the above-mentioned (c),

then, for the solution of quadratic optimization problems (7) - (8), it can be equivalent to the solution of the following piecewise linear projection equations:

P_Ω(x-(Qx+p))-x＝0∈R²⁺ⁿ， (9)

wherein x represents the variable to be solved (i.e. the variable to be solved for the projection equation at this time); q ═ D^TD∈R^{(2 +n)×(2+n)}，D＝[A,0；0,I]∈R^(3+n)×(2+n)A represents the structure parameter of the mobile platform, I represents the unit matrix;k represents the parameter of adjusting performance index, k is more than 0 and belongs to R,representing a non-linear mapping; p_Ω(. cndot.) represents a piecewise linear projection operator.

Further, for the piecewise-linear projection equation (9), the following recurrent neural network can be used for solving:

wherein the content of the first and second substances,represents the time derivative of x, which represents the variable to be solved (i.e. the state vector of the neural network at this time); mu represents a design parameter for adjusting the computational performance of the recurrent neural network (10), mu > 0 ∈ R; i represents an identity matrix, I ∈ R^{(2 +n)×(2+n)}；P_Ω(. h) represents a piecewise linear projection operator; q ═ D^TD∈R^(2+n)×(2+n)，D＝[A,0；0,I]∈R^(3+n)×(2+n)A represents the structure parameter of the mobile platform, I represents the unit matrix;k represents the parameter of adjusting performance index, k is more than 0 and belongs to R,representing a non-linear mapping; upper label^TRepresenting a transpose of a matrix or vector.

And finally, by giving an initial value and continuously calculating through a recurrent neural network (10), a numerical solution of a piecewise linear projection equation (9) can be obtained, so that the optimal solutions of quadratic optimization problems (7) - (8), namely the optimal solutions of speed layer state adjustment schemes (1) - (6) of the wheeled mobile robot are obtained, and finally the adjustment result of the expected adjustment state of the wheeled mobile robot is output.

Further, the lower computer controller drives the double wheels of the mobile platform and the joints of the robot according to a solution result of the quadratic optimization problem, namely a final output adjustment result of the expected adjustment state of the wheeled mobile robot, so that the mobile robot is quickly and accurately adjusted to the expected adjustment state, namely the initial state of executing the planning task, and the automatic adjustment of the wheeled mobile robot between different states is effectively realized.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.