Robot movement control method, device, apparatus and storage medium

Abstract

The application discloses a robot movement control method and device, equipment and storage medium, and relates to the technical field of mobile robots. The technical scheme provided by the application comprises the following steps: determining the distance of a sensor module relative to a reference object according to an electric signal generated by the sensor module; determining the target attitude angle and the target lateral offset of the mobile robot relative to the reference object according to the distance; calculating a preset nonlinear control model according to the target lateral offset and the target attitude angle to obtain the movement control parameter of the mobile robot, and controlling the movement of the mobile robot according to the movement control parameter; and the nonlinear control model is obtained based on an error dynamic model and a Lyapunov function. Through the above technical means, the problem of low movement control precision of the mobile robot in the prior art is solved, the navigation accuracy of the mobile robot is improved, and the working precision of the mobile robot is further improved.

Description

Technical Field

[0001] This application relates to the field of mobile robot technology, and in particular to a robot mobile control method, device, equipment and storage medium. Background Technology

[0002] With the rapid development of mobile robot autonomous navigation technology, mobile robots are widely used in various scenarios, such as cleaning indoors or carrying out tasks in factory workshops. When cleaning indoors, mobile robots typically use laser SLAM (Simultaneous Localization and Mapping) technology for autonomous navigation. However, due to the open and varied environment of factory workshops, laser SLAM is not suitable for such scenarios. Therefore, mobile robots often rely on line-following navigation technology for autonomous navigation.

[0003] In existing line-following navigation technologies, magnetic strips are laid on the factory floor. A sensor mounted on the magnetic strips on a mobile robot collects electrical signals, and the robot is then controlled to move along the magnetic strips based on these signals. The control scheme uses traditional PID (Proportional Integral Derivative) control to move the robot, which results in low control precision, leading to poor navigation accuracy and impacting the robot's operational accuracy. Summary of the Invention

[0004] This application provides a robot motion control method, apparatus, device, and storage medium to solve the problem of low motion control accuracy of mobile robots in the prior art, improve the navigation accuracy of mobile robots, and thus improve the operational accuracy of mobile robots.

[0005] In a first aspect, this application provides a robot movement control method, including:

[0006] The distance between the sensor module and the reference object is determined based on the electrical signal generated by the sensor module.

[0007] The target attitude angle and target lateral offset of the mobile robot relative to the reference object are determined based on the distance.

[0008] The mobile robot's movement control parameters are obtained by solving a preset nonlinear control model based on the target's lateral offset and the target's attitude angle, and the mobile robot's movement is controlled according to the movement control parameters; the nonlinear control model is constructed based on an error dynamic model and Lyapunov functions.

[0009] Secondly, this application provides a robot movement control device, comprising:

[0010] The distance determination module is configured to determine the distance of the sensor module relative to a reference object based on the electrical signal generated by the sensor module.

[0011] The target parameter determination module is configured to determine the target attitude angle and target lateral offset of the mobile robot relative to the reference object based on the distance.

[0012] The motion control module is configured to calculate a preset nonlinear control model based on the target lateral offset and the target attitude angle to obtain the motion control parameters of the mobile robot, and control the movement of the mobile robot according to the motion control parameters; the nonlinear control model is constructed based on the error dynamic model and Lyapunov function.

[0013] Thirdly, this application provides a robot movement control device, comprising:

[0014] One or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the robot movement control method as described in the first aspect.

[0015] Fourthly, this application provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the robot movement control method as described in the first aspect.

[0016] In this application, the distance between the sensor module and a reference object is determined based on the electrical signal generated by the sensor module; the target attitude angle and target lateral offset of the mobile robot relative to the reference object are determined based on the distance; a preset nonlinear control model is calculated based on the target lateral offset and target attitude angle to obtain the mobile robot's movement control parameters; and the mobile robot is controlled to move based on the movement control parameters. The nonlinear control model is constructed based on an error dynamic model and a Lyapunov function. Through the above technical means, the target attitude angle and target lateral offset of the mobile robot relative to the reference object are estimated based on the distance measured by the sensor module. The target attitude angle and target lateral offset can be regarded as the error between the current pose and the desired pose of the mobile robot. When designing the nonlinear control model using the error dynamic model and the Lyapunov function, the pose error is used as an unknown, and the error dynamic model is obtained by differentiating the pose error model. The pose deviation and error dynamic equations are then substituted into the derivative of the Lyapunov function to construct the nonlinear control model. The nonlinear control model designed based on Lyapunov functions can control the pose deviation of the mobile robot to converge asymptotically, with high control accuracy and stability. It can control the mobile robot to move accurately along the reference object, thereby improving the navigation accuracy of the mobile robot and thus improving the operation accuracy of the mobile robot. Attached Figure Description

[0017] Figure 1 This is a flowchart of a robot movement control method provided in an embodiment of this application;

[0018] Figure 2 This is a schematic diagram of the structure of the mobile robot provided in the embodiments of this application;

[0019] Figure 3 This is a flowchart of determining the target attitude angle provided in an embodiment of this application;

[0020] Figure 4 This is a schematic diagram of the target attitude angle and the target lateral offset provided in the embodiments of this application;

[0021] Figure 5 This is a flowchart of determining the target attitude angle provided in an embodiment of this application;

[0022] Figure 6 This is a schematic diagram of the mobile robot provided in the embodiments of this application in the world coordinate system;

[0023] Figure 7 This is a flowchart of determining movement control parameters provided in an embodiment of this application;

[0024] Figure 8 This is a flowchart of controlling the movement of a mobile robot based on a linear control model, provided in an embodiment of this application.

[0025] Figure 9 This is a schematic diagram of the structure of a robot movement control device provided in an embodiment of this application;

[0026] Figure 10 This is a schematic diagram of the structure of a robot mobile control device provided in an embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. A process can be terminated when its operation is completed, but it may also have additional steps not included in the drawings. A process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0028] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0029] The robot mobility control method provided in this embodiment can be executed by a robot mobility control device, which can be implemented through software and / or hardware. The robot mobility control device can consist of two or more physical entities, or it can consist of a single physical entity. For example, the robot mobility control device can be a mobile robot or a processor of a mobile robot.

[0030] The robot mobile control device is equipped with at least one operating system, including but not limited to Android, Linux, and Windows. The robot mobile control device can install at least one application based on the operating system; the application can be a built-in application of the operating system or an application downloaded from a third-party device or server. In this embodiment, the robot mobile control device has at least one application capable of executing robot mobile control methods.

[0031] For ease of understanding, this embodiment uses a mobile robot as the main body for executing the robot movement control method as an example for description.

[0032] In one embodiment, a mobile robot performs material handling operations in a factory workshop. Magnetic strips are laid on the workshop floor, and the mobile robot is equipped with two magnetic strip sensors. The robot determines the distance between the magnetic strip sensors and the magnetic strips using these sensors. Based on the distance and the robot's structure, the robot's pose error relative to the magnetic strips is estimated. This pose error is input into a PID controller, which outputs motion control parameters. The robot is then controlled to move along the direction of the magnetic strips as much as possible according to these parameters. A PID controller is a linear controller that adjusts its control parameters based on the deviation between the desired and actual movement. It is simple, flexible, and easy to adjust. However, the motion control of a mobile robot is inherently nonlinear due to factors such as its mechanical structure. When using the linear control of a PID controller to approximate the nonlinear control of the mobile robot, the motion control accuracy decreases, resulting in poor navigation accuracy and affecting the robot's operational precision.

[0033] To address the aforementioned issues, this embodiment provides a robot movement control method to improve the navigation accuracy of the mobile robot, thereby enhancing its operational precision.

[0034] Figure 1 A flowchart of a robot movement control method provided in an embodiment of this application is given. (Reference) Figure 1 The robot movement control method specifically includes:

[0035] S110. Determine the distance between the sensor module and the reference object based on the electrical signal generated by the sensor module.

[0036] In this embodiment, the reference object refers to a navigation strip, such as a magnetic strip or a colored ribbon, used to guide the mobile robot's movement. The sensor module is a sensor installed on the mobile robot to measure the distance between it and the reference object. When the reference object is a magnetic strip, the sensor module is a magnetic strip sensor; when the reference object is a colored ribbon, the sensor module is a colored ribbon sensor.

[0037] This embodiment uses a magnetic strip as a reference object and a magnetic strip sensor as a sensor module for description. For example, suppose a mobile robot is performing a material handling operation in a factory workshop. Magnetic strips are laid on the floor of the workshop, and the mobile robot moves along the magnetic strips, either above or to the side, to transport items to a specific location. When the mobile robot moves, the magnetic strip sensor approaches the magnetic strip, generating an electrical signal. This electrical signal is then converted into the distance between the magnetic strip sensor and the magnetic strip.

[0038] In one embodiment, the sensor module includes two magnetic strip sensors, which are respectively positioned in front of and behind the reference line of the mobile robot. The distance between the two magnetic strip sensors and the magnetic strip is determined based on the electrical signals generated by the two sensors. The reference line is a line segment characterizing the mobile robot's posture, typically the center line connecting the front and rear wheels. The reference line and the magnetic strip form two similar triangles. Based on the principle of similar triangles, the posture angle and lateral offset of the mobile robot relative to the reference object can be estimated by the distance between the two magnetic strip sensors and the magnetic strip. However, when the magnetic strip is not a straight line, the similar triangle relationship between the reference line and the magnetic strip is no longer satisfied, and the posture angle and lateral offset estimated by the distance between the two magnetic strip sensors and the magnetic strip are not accurate enough, affecting the accuracy and stability of subsequent motion control.

[0039] In another embodiment, the sensor module includes a first sensor, a second sensor, and a third sensor. Based on the electrical signals generated by the first, second, and third sensors, a first distance, a second distance, and a third distance relative to a reference object are determined, respectively. The three sensors are respectively positioned in front of, in the middle of, and behind the baseline. By detecting these distances, the robot's attitude angle and lateral offset relative to the reference object are estimated. This eliminates estimation errors caused by the non-linearity of the magnetic strip, improving the accuracy of the pose deviation and enhancing the precision and stability of subsequent motion control.

[0040] In this embodiment, the mobile robot includes a steering wheel and a driven wheel. The steering wheel is located at the front of the mobile robot, and the driven wheel is located at the rear. A first sensor is located in front of the steering wheel, a third sensor is located behind the driven wheel, and a second sensor is located between the steering wheel and the driven wheel. The center points of the first sensor, the second sensor, the third sensor, the steering wheel, and the driven wheel are all on the same straight line. For example, Figure 2 This is a schematic diagram of the structure of the mobile robot provided in an embodiment of this application. Figure 2 As shown, the driven wheels include a left driven wheel 15 and a right driven wheel 16. The center point O4 between the left and right driven wheels, the center point O1 of the first sensor 17, the center point O0 of the steering wheel 12, the center point O2 of the second sensor 13, and the center point O3 of the third sensor 14 are located on the centerline of the mobile robot. The line connecting the center points O4 and O0 is the reference line of the mobile robot.

[0041] S120. Determine the target attitude angle and target lateral offset of the mobile robot relative to the reference object based on the distance.

[0042] Here, the target attitude angle refers to the error between the current attitude angle and the desired attitude angle of the mobile robot, and the target lateral offset refers to the lateral error between the current position and the desired position of the mobile robot. (Reference) Figure 2 The mobile robot 11 should move along the reference object 18. Therefore, the position and tangent direction of each trajectory point on the reference object 18 represent the desired position and desired attitude angle of the mobile robot. Thus, the angle between the baseline and the reference object 18 can be considered the error between the current attitude angle and the desired attitude angle of the mobile robot, and the distance between the center point O4 and the reference object 18 can be considered the lateral error between the position and the desired position of the mobile robot. Correspondingly, the angle between the baseline and the reference object 18 is the target attitude angle θ. e The vertical distance from the center point O4 to the reference object 18 is the target's lateral offset y. e The target attitude angle θ can be estimated based on the distance measured by the sensor module and the principle of similar triangles. e and target lateral offset y e .

[0043] In one embodiment, Figure 3 This is a flowchart illustrating the determination of the target attitude angle provided in an embodiment of this application. For example... Figure 3 As shown, the steps for determining the target attitude angle specifically include S1201-S1204:

[0044] S1201. Obtain the fourth distance between the first sensor and the second sensor, calculate the preset first similar triangle model based on the first distance, the second distance and the fourth distance, and determine the first attitude angle based on the first calculation result of the first similar triangle model.

[0045] Figure 4 This is a schematic diagram of the target attitude angle and target lateral offset provided in an embodiment of this application. (Reference) Figure 2 and Figure 4 The fourth distance L1 between the first sensor 17 and the second sensor is measured in advance; the fifth distance L2 between the second sensor and the center point O4 is measured; and the sixth distance L3 between the third sensor and the center point O5 is measured. The distance from the center point O1 to the vertical point A on the reference object 18 measured by the first sensor 17 is the first distance d1; the distance from the center point O2 to the vertical point B on the reference object 18 measured by the second sensor 13 is the second distance d2; the distance from the center point O3 to the vertical point D on the reference object 18 measured by the third sensor 14 is the third distance; and the distance between the center point O4 and the vertical point C on the reference object 18 is the target lateral offset y. e The baseline and reference line intersect at point E. The perpendicular point A, center point O1, and point E form the first triangle; the perpendicular point B, center point O2, and point E form the second triangle; the perpendicular point C, center point O4, and point E form the third triangle; and the perpendicular point D, center point O3, and point E form the fourth triangle.

[0046] In this embodiment, the expression for the first similar triangle model is as follows:

[0047]

[0048] Here, x1 is the distance from the center point O1 to point E, which is the first solution result. It can be understood that the first and second triangles are considered as two similar triangles, and based on the properties of similar triangles, the first similar triangle model can be obtained. Substituting the first distance d1, the second distance d2, and the fourth distance L1 into the first similar triangle model, the first solution result x1 can be calculated.

[0049] Furthermore, substitute the first solution result x1 into the first inverse sine function θ. e1 =sin -1 (d1 / x1)=sin -1 (d2 / (L1-x1)) is used to calculate the first attitude angle θ. e1 .

[0050] S1202. Obtain the fifth distance between the second sensor and the driven wheel, obtain the sixth distance between the driven wheel and the third sensor, and determine the total distance of the fifth distance, the sixth distance and the fourth distance.

[0051] S1203. Calculate the preset second similar triangle model based on the first distance, the third distance and the total distance, and determine the second attitude angle based on the second calculation result of the second similar triangle model.

[0052] For example, the expression for the second similar triangle model is shown below:

[0053]

[0054] Here, x2 is the distance from the center point O1 to point E, which is the first solution result. Treating the first and fourth triangles as two similar triangles, a second similar triangle model can be obtained based on the properties of similar triangles. Substituting the total distances of the first distance d1, the third distance d3, the fifth distance L2, the sixth distance L3, and the fourth distance L1 into the second similar triangle model, the second solution result x2 can be calculated.

[0055] Furthermore, substituting the second solution result x2 into the second inverse sine function θ e2 =sin -1 (d1 / x2)=sin -1 The second attitude angle θ is calculated using ((d1+d3) / (L1+L2+L3-x2)). e2 .

[0056] S1204. Take the average of the first attitude angle and the second attitude angle as the target attitude angle.

[0057] It is understood that this embodiment uses the distances measured by three sensors to determine the attitude angle errors when the first triangle and the second triangle are similar triangles, and the attitude angle errors when the first triangle and the fourth triangle are similar triangles. The average value of the two is taken as the target attitude angle to eliminate the error caused by the non-linearity of the reference object and improve the estimation accuracy of the target attitude angle.

[0058] From the above embodiments, the target attitude angle θ can be obtained. e The calculation formula can be derived as follows:

[0059]

[0060] For example, after the sensor module measures the first distance d1, the second distance d2, and the third distance d3, substituting these distances into the above calculation formula, the target attitude angle θ can be calculated. e .

[0061] In one embodiment, Figure 5 This is a flowchart illustrating the determination of the target attitude angle provided in an embodiment of this application. For example... Figure 5 As shown, the steps for determining the target attitude angle specifically include S1205-S1207:

[0062] S1205. Based on the fifth distance, the first solution result, and the second distance, calculate the preset third similar triangle model to obtain the first lateral offset.

[0063] In this embodiment, the expression for the third similar triangle model is as follows:

[0064]

[0065] It is understandable that the second and third triangles can be considered as two similar triangles, and a third similar triangle model can be obtained based on the properties of similar triangles. Substituting the second distance d2, the fourth distance L1, and the fifth distance L2 into the third similar triangle model, the first lateral offset y can be calculated. e1 .

[0066] S1206. Based on the total distance, the second solution result, the sixth distance, and the third distance, calculate the preset fourth similar triangle model to obtain the second lateral offset.

[0067] In this embodiment, the expression for the fourth similar triangle model is as follows:

[0068]

[0069] It is understandable that the fourth and third triangles can be considered as two similar triangles, and a model of the fourth similar triangle can be obtained based on the properties of similar triangles. Substituting the third distance d3, the fourth distance L1, the fifth distance L2, and the sixth distance L3 into the model of the third similar triangle, the second lateral offset y can be calculated. e2 .

[0070] S1207. Take the average of the first lateral offset and the second lateral offset as the target lateral offset.

[0071] It is understood that this embodiment uses the distances measured by three sensors to determine the lateral error when the third triangle and the second triangle are similar triangles, and the lateral error when the third triangle and the fourth triangle are similar triangles. The average of the two is taken as the target lateral offset to eliminate the error caused by the non-linearity of the reference object and improve the estimation accuracy of the target lateral offset.

[0072] From the above embodiments, the target lateral offset y can be obtained. e The calculation formula can be derived as follows:

[0073]

[0074] For example, after the sensor module measures the first distance d1, the second distance d2, and the third distance d3, substituting these distances into the above calculation formula, the target's lateral offset y can be calculated. e .

[0075] S130. Based on the target lateral offset and target attitude angle, calculate the preset nonlinear control model to obtain the mobile robot's movement control parameters, and control the mobile robot's movement according to the movement control parameters; the nonlinear control model is constructed based on the error dynamic model and Lyapunov function.

[0076] For example, Figure 6 This is a schematic diagram of the mobile robot provided in the embodiments of this application in the world coordinate system. For example... Figure 6 As shown, assume the linear velocity of the steering wheel of the mobile robot is v. i The angular velocity of the steering wheel is ω i If the steering wheel heading angle is β and the rotation center point of the mobile robot is point O, then the rotation radius R and rotation angular velocity ω of the mobile robot are expressed as follows:

[0077]

[0078]

[0079] The forward velocity v of the mobile robot and its velocity components along the X and Y axes in the world coordinate system can be expressed as:

[0080] v = v i cosβ

[0081] v x =vcosθ

[0082] v y =vsinθ

[0083] Where θ is the angle between the robot's body and the X-axis. Assume that the initial coordinates of the robot's center point O4 in the world coordinate system are (x0, y0), and the angle between the robot's body and the X-axis is θ0. Integrating the expressions for the rotational angular velocity ω, the forward velocity v, and the velocity components, we obtain the kinematic model of the mobile robot as follows:

[0084]

[0085] Define the control input of the mobile robot as u = [v, ω]. T The state variables are q = [x, y, θ]. T Based on the above expression, the pose error model of the mobile robot can be derived as follows:

[0086] in,

[0087] Define the desired state variable q of the mobile robot. d =[x d ,y d ,θ d ] T Correspondingly, the state variable error q e =[x e ,y e ,θ e ] T It can be represented as q e =T(q) d -q), x e This represents the longitudinal error between the current position and the desired position of the mobile robot.

[0088] in, T is the pose transformation matrix between the world coordinate system and the vehicle coordinate system. The desired state variable q... d (x) d ,y d Let q be the desired position of the mobile robot. d θ d Let be the desired pose angle of the mobile robot.

[0089] Furthermore, the dynamic error model of the mobile robot can be derived as follows:

[0090]

[0091] Among them, v d To satisfy the desired state variable q for the mobile robot d The forward speed of the car at that time, ω d Let q be the expected state variable of the mobile robot. d The angular velocity at that time.

[0092] This embodiment can design an output feedback controller for the mobile robot based on Lyapunov functions, so that the state variable error q of the mobile robot is minimized. e It converges asymptotically. Therefore, a Lyapunov function can be designed as shown in the following expression:

[0093] V = k(x) e 2 +y e 2 ) 2 +2(sin(θ e / 2))

[0094] Wherein, the state variable error q e Let V be the independent variable of the Lyapunov function V. Taking the derivative of the Lyapunov function V, we obtain:

[0095]

[0096] when At that time, the state variable error q e It converges asymptotically. Therefore, a control model can be constructed:

[0097]

[0098] Among them, k1 and k2 are constants, whose values ​​can be determined through actual experiments.

[0099] Since the control inputs of the above control model are the vehicle's forward speed v and turning angular velocity ω, and these two values ​​are difficult to obtain directly, this embodiment defines the control input as u. i =[v i ,ω i ] T Based on the vehicle's forward speed v and the steering wheel's linear speed v i The relation v = v i cosβ, and the rotational angular velocity ω and the steering wheel angular velocity ω i Relationship ω i =ω, transforming the above control model, we can obtain a nonlinear control model:

[0100]

[0101] In this embodiment, the movement control parameters are the steering wheel linear velocity and steering wheel angular velocity. The steering wheel linear velocity and steering wheel angular velocity can be determined based on the aforementioned nonlinear control model. For example, Figure 7 This is a flowchart illustrating the determination of movement control parameters provided in an embodiment of this application. For example... Figure 7 As shown, the steps for determining the movement control parameters specifically include S1301-S1302:

[0102] S1301. Transform the preset desired position and desired velocity from the world coordinate system to the Fellerian coordinate system to obtain the desired linear velocity and desired angular velocity.

[0103] For example, the expected state variable q at the current moment d =[x d ,y d ,θ d ] T By transforming to the Frenet coordinate system, the desired linear velocity v can be obtained. d and desired angular velocity ω d .

[0104] S1302. Obtain the steering wheel heading angle of the mobile robot. Based on the desired linear velocity, desired angular velocity, target lateral offset, target attitude angle, steering wheel heading angle and preset longitudinal offset, solve the nonlinear control model to obtain the steering wheel linear velocity and steering wheel angular velocity.

[0105] In this embodiment, the longitudinal offset is a constant measured based on actual experiments. The current steering wheel heading angle of the mobile robot is obtained, and after calculating the target attitude angle and target lateral offset based on the distance measured by the sensor module, the desired linear velocity, desired angular velocity, target lateral offset, target attitude angle, steering wheel heading angle, and preset longitudinal offset are substituted into the aforementioned nonlinear control model to calculate the steering wheel linear velocity and steering wheel angular velocity. The steering wheel movement is controlled based on the steering wheel linear velocity and steering wheel angular velocity to drive the mobile robot to move as close to the reference object as possible. As can be seen from the expression of the nonlinear control model, when there are no parameter uncertainties or uncertain external disturbances in the mobile robot and all parameters of the mobile robot are known, the movement control error of the mobile robot is bounded. This ensures that the movement control of the mobile robot will not have large errors, greatly improving the control accuracy and stability of the mobile robot.

[0106] In another embodiment, the control input to the mobile robot is the linear velocity of the steering wheel. To ensure the stability of the vehicle's forward speed during operation, the steering wheel linear velocity needs to be frequently adjusted, making the control method relatively complex. Therefore, this embodiment proposes a simple and feasible linear control model based on the nonlinear control model. It is understood that the vehicle's forward speed mainly affects the magnitude of the longitudinal error, but in magnetic strip navigation, the longitudinal error is a fictitious and meaningless parameter. Therefore, when designing the control model, the steering wheel linear velocity can be set to a constant value, which simplifies the control method and does not affect the control performance.

[0107] From the nonlinear control model and the expression for the turning angular velocity, it can be seen that the steering wheel heading angle β and θ e y e The nonlinear relationship can be expressed as: β = f(y) e ,θ e ).

[0108] The expression of the nonlinear relationship in y e0 =0, θ e0 Performing a Taylor expansion at the point = 0 yields:

[0109]

[0110] Based on the above formula, the linear control model can be designed as follows:

[0111] β=-k3y e -k4θ e

[0112] Among them, k3 and k4 are proportional systems and their values ​​can be determined according to actual experiments.

[0113] In this embodiment, Figure 8 This is a flowchart illustrating the control of a mobile robot based on a linear control model, as provided in an embodiment of this application. Figure 8 As shown, the steps for controlling the movement of the mobile robot based on the linear control model specifically include S1303-S1304:

[0114] S1303. Based on the target lateral offset and target attitude angle, calculate the preset linear control model to obtain the steering wheel heading angle; wherein, the linear control model is obtained by Taylor expansion of the nonlinear relationship between the steering wheel heading angle and the target lateral offset and target attitude angle.

[0115] For example, after calculating the target attitude angle and the target lateral offset based on the distance measured by the sensor module, the target attitude angle and the target lateral offset are substituted into the linear control model to calculate the steering wheel heading angle.

[0116] S1304. Based on the steering wheel heading angle and the preset steering wheel linear velocity, control the steering wheel to drive the mobile robot to move.

[0117] In this embodiment, the linear velocity of the steering wheel is preset according to actual needs, so that the steering wheel always maintains the same linear velocity. Then, the turning effect of the mobile robot is achieved by adjusting the heading angle of the steering wheel. The control method is relatively simple and easier for the mobile robot to execute.

[0118] In summary, the robot motion control method provided in this application determines the distance between the sensor module and a reference object based on the electrical signals generated by the sensor module; determines the target posture angle and target lateral offset of the mobile robot relative to the reference object based on the distance; calculates a preset nonlinear control model based on the target lateral offset and target posture angle to obtain the motion control parameters of the mobile robot; and controls the movement of the mobile robot based on the motion control parameters. The nonlinear control model is constructed based on an error dynamic model and a Lyapunov function. Through the above technical means, the target posture angle and target lateral offset of the mobile robot relative to the reference object are estimated based on the distance measured by the sensor module. The target posture angle and target lateral offset can be regarded as the error between the current pose and the desired pose of the mobile robot. When designing the nonlinear control model using the error dynamic model and the Lyapunov function, the pose error is used as an unknown, and the error dynamic model is obtained by differentiating the pose error model. The pose deviation and error dynamic equations are then substituted into the derivative of the Lyapunov function to construct the nonlinear control model. The nonlinear control model designed based on Lyapunov functions can control the pose deviation of the mobile robot to converge asymptotically, with high control accuracy and stability. It can control the mobile robot to move accurately along the reference object, thereby improving the navigation accuracy of the mobile robot and thus improving the operation accuracy of the mobile robot.

[0119] Based on the above embodiments, Figure 9 This is a schematic diagram of a robot movement control device provided in an embodiment of this application. (Reference) Figure 9 The robot movement control device provided in this embodiment specifically includes: a distance determination module 21, a target parameter determination module 22, and a movement control module 23.

[0120] The distance determination module is configured to determine the distance between the sensor module and the reference object based on the electrical signal generated by the sensor module.

[0121] The target parameter determination module is configured to determine the target attitude angle and target lateral offset of the mobile robot relative to the reference object based on the distance;

[0122] The motion control module is configured to calculate a preset nonlinear control model based on the target's lateral offset and target's attitude angle to obtain the motion control parameters of the mobile robot, and control the mobile robot to move according to the motion control parameters; the nonlinear control model is constructed based on the error dynamic model and Lyapunov function.

[0123] Based on the above embodiments, the sensor module includes a first sensor, a second sensor, and a third sensor; correspondingly, the distance determination module is configured to determine the first distance, the second distance, and the third distance of the first sensor, the second sensor, and the third sensor relative to the reference object, respectively, based on the electrical signals generated by the first sensor, the second sensor, and the third sensor.

[0124] Based on the above embodiments, the mobile robot includes a steering wheel and a driven wheel. The steering wheel is located at the front of the mobile robot, and the driven wheel is located at the rear of the mobile robot. A first sensor is located in front of the steering wheel, a third sensor is located behind the driven wheel, and a second sensor is located between the steering wheel and the driven wheel. The center points of the first sensor, the second sensor, the third sensor, the steering wheel, and the driven wheel are located on the same straight line.

[0125] Based on the above embodiments, the target parameter determination module includes: a first attitude angle determination unit, configured to acquire a fourth distance between a first sensor and a second sensor, calculate a preset first similar triangle model based on the first distance, the second distance, and the fourth distance, and determine a first attitude angle based on a first calculation result of the first similar triangle model; a total distance determination unit, configured to acquire a fifth distance between the second sensor and the driven wheel, acquire a sixth distance between the driven wheel and the third sensor, and determine the total distance of the fifth distance, the sixth distance, and the fourth distance; a second attitude angle determination unit, configured to calculate a preset second similar triangle model based on the first distance, the third distance, and the total distance, and determine a second attitude angle based on a second calculation result of the second similar triangle model; and a target attitude angle determination unit, configured to use the average of the first attitude angle and the second attitude angle as the target attitude angle.

[0126] Based on the above embodiments, the target parameter determination module includes: a first offset determination unit configured to calculate a preset third similar triangle model based on a fifth distance, a first solution result, and a second distance to obtain a first lateral offset; a second offset determination unit configured to calculate a preset fourth similar triangle model based on a total distance, a second solution result, a sixth distance, and a third distance to obtain a second lateral offset; and a target offset determination unit configured to use the average of the first lateral offset and the second lateral offset as the target lateral offset.

[0127] Based on the above embodiments, the mobile control module includes: a desired velocity determination unit, configured to transform the preset desired position and desired velocity from the world coordinate system to the Fellerian coordinate system to obtain the desired linear velocity and desired angular velocity; and a steering wheel speed control unit, configured to acquire the steering wheel heading angle of the mobile robot, and solve the nonlinear control model based on the desired linear velocity, desired angular velocity, target lateral offset, target attitude angle, steering wheel heading angle and preset longitudinal offset to obtain the steering wheel linear velocity and steering wheel angular velocity.

[0128] Based on the above embodiments, the robot movement control device includes: a turning angle determination module, configured to calculate a preset linear control model based on the target lateral offset and the target attitude angle to obtain the steering wheel heading angle; wherein, the linear control model is obtained by Taylor expansion of the nonlinear relationship between the steering wheel heading angle and the target lateral offset and the target attitude angle; and a steering wheel turning angle control unit, configured to control the steering wheel to drive the mobile robot to move based on the steering wheel heading angle and a preset steering wheel linear velocity.

[0129] The robot motion control device provided in this application, as described above, determines the distance between the sensor module and a reference object based on the electrical signals generated by the sensor module; determines the target posture angle and target lateral offset of the mobile robot relative to the reference object based on the distance; calculates a preset nonlinear control model based on the target lateral offset and target posture angle to obtain the motion control parameters of the mobile robot; and controls the movement of the mobile robot based on the motion control parameters. The nonlinear control model is constructed based on an error dynamic model and a Lyapunov function. Through the above technical means, the target posture angle and target lateral offset of the mobile robot relative to the reference object are estimated based on the distance measured by the sensor module. The target posture angle and target lateral offset can be regarded as the error between the current pose and the desired pose of the mobile robot. When designing a nonlinear control model using an error dynamic model and a Lyapunov function, the pose error is used as an unknown, and the pose error model is differentiated to obtain the error dynamic model. The pose deviation and error dynamic equations are substituted into the derivative of the Lyapunov function to construct the nonlinear control model. The nonlinear control model designed based on Lyapunov functions can control the pose deviation of the mobile robot to converge asymptotically, with high control accuracy and stability. It can control the mobile robot to move accurately along the reference object, thereby improving the navigation accuracy of the mobile robot and thus improving the operation accuracy of the mobile robot.

[0130] The robot motion control device provided in this application embodiment can be used to execute the robot motion control method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0131] Figure 10 This is a schematic diagram of the structure of a robot mobile control device provided in an embodiment of this application, with reference to... Figure 10 The robot mobility control device includes a processor 31, a memory 32, a communication device 33, an input device 34, and an output device 35. The number of processors 31 and the number of memories 32 in the robot mobility control device can be one or more. The processor 31, memory 32, communication device 33, input device 34, and output device 35 of the robot mobility control device can be connected via a bus or other means.

[0132] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to the robot mobility control method in any embodiment of this application (e.g., distance determination module 21, target parameter determination module 22, and mobility control module 23 in a robot mobility control device). The memory 32 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the device, etc. Furthermore, the memory 32 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0133] The communication device 33 is used for data transmission.

[0134] The processor 31 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 32, thereby realizing the robot movement control method described above.

[0135] Input device 34 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the device. Output device 35 may include display devices such as a display screen.

[0136] The robot mobility control device provided above can be used to execute the robot mobility control method provided in the above embodiments, and has corresponding functions and beneficial effects.

[0137] This application embodiment also provides a storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to execute a robot movement control method. The robot movement control method includes: determining the distance between the sensor module and a reference object based on the electrical signals generated by the sensor module; determining the target attitude angle and target lateral offset of the mobile robot relative to the reference object based on the distance; calculating a preset nonlinear control model based on the target lateral offset and target attitude angle to obtain the movement control parameters of the mobile robot; and controlling the movement of the mobile robot based on the movement control parameters. The nonlinear control model is constructed based on an error dynamic model and a Lyapunov function.

[0138] Storage medium – any type of memory device or storage device. The term “storage medium” is intended to include: mounting media, such as CD-ROM, floppy disk, or magnetic tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. Storage medium may also include other types of memory or combinations thereof. Furthermore, storage medium may reside in a first computer system in which the program is executed, or it may reside in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term “storage medium” can include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). Storage medium may store program instructions (e.g., specifically implemented as a computer program) executable by one or more processors.

[0139] Of course, the computer-executable instructions provided in the embodiments of this application are not limited to the robot movement control method described above, but can also execute related operations in the robot movement control method provided in any embodiment of this application.

[0140] The robot motion control device, storage medium, and robot motion control equipment provided in the above embodiments can execute the robot motion control method provided in any embodiment of this application. For technical details not described in detail in the above embodiments, please refer to the robot motion control method provided in any embodiment of this application.

[0141] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application. The scope of this application is determined by the scope of the claims.

Claims

1. A robot movement control method, characterized in that, include: The distance between the sensor module and the reference object is determined based on the electrical signal generated by the sensor module. The target attitude angle and target lateral offset of the mobile robot relative to the reference object are determined based on the distance. The mobile robot's movement control parameters are obtained by solving a preset nonlinear control model based on the target's lateral offset and the target's attitude angle. The nonlinear control model is constructed based on an error dynamic model and a Lyapunov function. The movement control parameters include the steering wheel linear velocity and the steering wheel angular velocity. This includes: transforming the preset desired position and desired velocity from the world coordinate system to the Fellerian coordinate system to obtain the desired linear velocity and desired angular velocity; obtaining the steering wheel heading angle of the mobile robot; and solving the nonlinear control model based on the desired linear velocity, the desired angular velocity, the target lateral offset, the target attitude angle, the steering wheel heading angle, and the preset longitudinal offset to obtain the steering wheel linear velocity and steering wheel angular velocity. The mobile robot is controlled to move according to the stated movement control parameters.

2. The robot movement control method according to claim 1, characterized in that, The sensor module includes a first sensor, a second sensor, and a third sensor; correspondingly, determining the distance of the sensor module relative to the reference object based on the electrical signal generated by the sensor module includes: Based on the electrical signals generated by the first sensor, the second sensor, and the third sensor, the first distance, the second distance, and the third distance of the first sensor, the second sensor, and the third sensor relative to the reference object are determined respectively.

3. The robot movement control method according to claim 2, characterized in that, The mobile robot includes a steering wheel and a driven wheel. The steering wheel is located at the front of the mobile robot, and the driven wheel is located at the rear of the mobile robot. The first sensor is located in front of the steering wheel, the third sensor is located behind the driven wheel, and the second sensor is located between the steering wheel and the driven wheel. The center points of the first sensor, the second sensor, the third sensor, the steering wheel, and the driven wheel are located on the same straight line.

4. The robot movement control method according to claim 3, characterized in that, Determining the target attitude angle of the mobile robot relative to the reference object based on the distance includes: Obtain the fourth distance between the first sensor and the second sensor, calculate a preset first similar triangle model based on the first distance, the second distance and the fourth distance, and determine the first attitude angle based on the first calculation result of the first similar triangle model; Obtain the fifth distance between the second sensor and the driven wheel, obtain the sixth distance between the driven wheel and the third sensor, and determine the total distance of the fifth distance, the sixth distance and the fourth distance; A preset second similar triangle model is calculated based on the first distance, the third distance, and the total distance; and a second attitude angle is determined based on the second calculation result of the second similar triangle model. The average of the first attitude angle and the second attitude angle is taken as the target attitude angle.

5. The robot movement control method according to claim 4, characterized in that, Determining the target attitude angle and target lateral offset of the mobile robot relative to the reference object based on the distance includes: Based on the fifth distance, the first solution result, and the second distance, a preset third similar triangle model is calculated to obtain the first lateral offset. Based on the total distance, the second solution result, the sixth distance, and the third distance, a preset fourth similar triangle model is calculated to obtain the second lateral offset. The average of the first lateral offset and the second lateral offset is taken as the target lateral offset.

6. The robot movement control method according to claim 1, characterized in that, The method further includes: The steering wheel heading angle is obtained by solving a preset linear control model based on the target lateral offset and the target attitude angle; wherein, the linear control model is obtained by Taylor expansion of the nonlinear relationship between the steering wheel heading angle and the target lateral offset and the target attitude angle. Based on the steering wheel's heading angle and the preset steering wheel linear velocity, the steering wheel is controlled to drive the mobile robot to move.

7. A robot movement control device, characterized in that, include: The distance determination module is configured to determine the distance of the sensor module relative to a reference object based on the electrical signal generated by the sensor module. The target parameter determination module is configured to determine the target attitude angle and target lateral offset of the mobile robot relative to the reference object based on the distance. A motion control module is configured to calculate a preset nonlinear control model based on the target lateral offset and the target attitude angle to obtain the motion control parameters of the mobile robot, and control the movement of the mobile robot according to the motion control parameters. The nonlinear control model is constructed based on an error dynamic model and a Lyapunov function, and the motion control parameters include the steering wheel linear velocity and steering wheel angular velocity. The motion control module includes: a desired velocity determination unit, configured to transform a preset desired position and desired velocity from the world coordinate system to the Fellerian coordinate system to obtain the desired linear velocity and desired angular velocity; and a steering wheel speed control unit, configured to acquire the steering wheel heading angle of the mobile robot, and calculate the nonlinear control model based on the desired linear velocity, the desired angular velocity, the target lateral offset, the target attitude angle, the steering wheel heading angle, and a preset longitudinal offset to obtain the steering wheel linear velocity and steering wheel angular velocity.

8. A robot movement control device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the robot motion control method as described in any one of claims 1-6.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the robot motion control method as described in any one of claims 1-6.

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