Wheel return control system and method of electric carrier

Abstract

The invention discloses a wheel restoration control system and method for an electric carrier. The system comprises wheels integrated on the electric carrier, a multi-line laser contourgraph, a high-precision camera, a driver and a control unit. The control unit is used for starting the multi-line laser contourgraph to emit a plurality of laser lines to the surfaces of the moving wheels when the vehicle state of the electric carrier is a parking state; receiving wheel three-dimensional point cloud data fed back by the multi-line laser contourgraph; receiving a wheel two-dimensional image about the moving wheel fed back by the high-precision camera; carrying out multi-modal data fusion analysis according to the wheel three-dimensional point cloud data and the wheel two-dimensional image to obtain a current actual angle of the moving wheel; and according to the current actual angle, a driver is controlled to rotate so as to rotate the moving wheel to a preset restoration position point. By adopting the embodiment of the invention, high-precision wheel restoration can be realized, so that the accuracy of wheel restoration is improved.

Description

Technical Field

[0001] The present application relates to the technical field of wheel control, and in particular to a wheel alignment control system and method for an electric transport vehicle. Background Art

[0002] In the fields of modern logistics warehousing, factory material handling, and port loading and unloading, electric trucks are widely used in the transportation and handling of goods. After the electric truck stops running and shuts down, if the previous user does not correct the wheels, the next user will find it difficult to accurately judge the steering direction of the wheels when starting to use it, which not only increases the difficulty of operation, but also may cause safety hazards. Therefore, how to accurately correct the wheels after parking is very important.

[0003] At present, the focus is mainly on simple automatic correction functions. For example, some electric transport trucks are equipped with an automatic wheel correction system based on an angle sensor, which can automatically adjust the wheels to the center position after parking. However, since the sensitivity of the angle sensor decreases with the increase of usage time, high-precision wheel correction cannot be achieved, thereby reducing the accuracy of wheel correction. Summary of the invention

[0004] The present application provides a wheel alignment control system for an electric transport vehicle. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0005] In a first aspect, an embodiment of the present application provides a wheel alignment control system for an electric transport vehicle, the system comprising: The wheels, multi-line laser profiler, high-precision camera, drive and control unit are integrated into the electric transport vehicle; The wheel includes a wheel disc and a mobile wheel. The top of the wheel disc is fixed on the chassis of the electric transport vehicle. A fixing rod is provided on the connecting piece between the wheel disc and the mobile wheel. A multi-line laser profiler and a high-precision camera are installed on a fixed rod at a preset distance from the wheel surface. A connecting shaft is provided inside the wheel disc, one end of which is connected to the moving wheel and the other end is connected to the driver; wherein, The control unit is connected to the driver, the multi-line laser profiler, and the high-precision camera for communication; wherein, The control unit is used to start the multi-line laser profiler when the vehicle state of the electric transport truck is the parking state, so as to emit multiple laser lines to the surface of the moving wheel; receive the three-dimensional wheel point cloud data about the moving wheel fed back by the multi-line laser profiler; receive the two-dimensional wheel image about the moving wheel fed back by the high-precision camera; perform multimodal data fusion analysis based on the three-dimensional wheel point cloud data and the two-dimensional wheel image to obtain the current actual angle of the moving wheel; and control the driver to rotate according to the current actual angle to rotate the moving wheel to a preset correction position point.

[0006] In a second aspect, a wheel alignment control method for an electric transport vehicle is applied to a control unit of the electric transport vehicle, and the method comprises: When the vehicle state of the electric transport truck is a parking state, a multi-line laser profiler is started to emit a plurality of laser lines to the surface of the moving wheel; receiving three-dimensional point cloud data of the moving wheel fed back by a multi-line laser profiler; receiving a two-dimensional wheel image of the moving wheel fed back by a high-precision camera; Perform multimodal data fusion analysis based on the three-dimensional point cloud data of the wheel and the two-dimensional image of the wheel to obtain the current actual angle of the moving wheel; According to the current actual angle, the drive is controlled to rotate so as to rotate the moving wheel to the preset return position point.

[0007] In the embodiment of the present application, the control unit starts the multi-line laser profiler when the vehicle is parked, emits laser lines to the wheel surface, receives feedback of the three-dimensional point cloud data of the wheel, and receives the two-dimensional image data fed back by the high-precision camera. The three-dimensional point cloud data can provide accurate shape and size information of the wheel. This data can capture the tiny details and complex shapes of the surface of the object, thereby achieving high-precision measurement. At the same time, the two-dimensional image can provide the overall appearance and edge features of the wheel. By fusing these two types of data for analysis, the accuracy of the wheel alignment control system can be improved, thereby achieving high-precision wheel alignment, thereby improving the accuracy of wheel alignment.

[0008] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0010] Figure 1 It is a schematic diagram of the system structure of a wheel alignment control system of an electric transport vehicle provided in an embodiment of the present application; Figure 2is a structural schematic diagram of a wheel provided in an embodiment of the present application; Figure 3 is a side schematic diagram of an electric transport vehicle provided in an embodiment of the present application; Figure 4 is a side schematic diagram of another electric transport vehicle provided in an embodiment of the present application; Figure 5 It is a schematic diagram of a model architecture of a pre-trained tilt angle quantization model provided in an embodiment of the present application; Figure 6 It is a flow chart of a model training method of a tilt angle quantization model provided in an embodiment of the present application; Figure 7 It is a flow chart of a wheel alignment control method of an electric transport vehicle provided in an embodiment of the present application; Figure 8 It is a structural schematic diagram of a control unit provided in an embodiment of the present application; Fig. 9 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The following description and the accompanying drawings sufficiently illustrate specific embodiments of the present application to enable those skilled in the art to practice them.

[0012] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0013] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of systems and methods consistent with some aspects of the present application as detailed in the attached claims.

[0014] In the description of the present application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. In addition, in the description of the present application, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0015] At present, wheel alignment mainly focuses on simple automatic alignment functions. For example, some electric transport trucks are equipped with an automatic wheel alignment system based on an angle sensor, which can automatically adjust the wheels to the center position after parking.

[0016] The inventors have realized that since the sensitivity of the angle sensor decreases with the increase of usage time, high-precision wheel alignment cannot be achieved, thereby reducing the accuracy of wheel alignment.

[0017] In the embodiment of the present application, the control unit starts the multi-line laser profiler when the vehicle is parked, emits laser lines to the wheel surface, receives feedback of the three-dimensional point cloud data of the wheel, and receives the two-dimensional image data fed back by the high-precision camera. The three-dimensional point cloud data can provide accurate shape and size information of the wheel. This data can capture the tiny details and complex shapes of the surface of the object, thereby achieving high-precision measurement. At the same time, the two-dimensional image can provide the overall appearance and edge features of the wheel. By fusing these two types of data for analysis, the accuracy of the wheel alignment control system can be improved, thereby achieving high-precision wheel alignment, thereby improving the accuracy of wheel alignment.

[0018] See also Figure 1 , Figure 1 The system structure diagram of a wheel alignment control system of an electric transport vehicle provided in an embodiment of the present application comprises: a wheel integrated in the electric transport vehicle, a multi-line laser profiler 1, a high-precision camera (2), a driver and a control unit. The driver and the control unit are inherent components of the electric transport vehicle and will not be described in detail here.

[0019] Among them, for example Figure 2 As shown, the wheel includes a wheel disc (4) and a moving wheel (5), the top of the wheel disc (4) is fixed on the chassis of the electric transport vehicle; a fixing rod (3) is provided on the connecting piece between the wheel disc (4) and the moving wheel (5); a multi-line laser profiler 1 and a high-precision camera (2) are installed on the fixing rod (3) and are at a preset distance from the wheel surface; a connecting shaft is provided inside the wheel disc (4), one end of the connecting shaft is connected to the moving wheel (5), and the other end is connected to the driver; wherein the control unit is respectively connected to the driver, the multi-line laser profiler 1, and the high-precision camera (2) for communication. wherein the connecting shaft is an inherent component of the electric transport vehicle for driving the wheel to move, and will not be described in detail here.

[0020] Among them, the electric transport truck is an electrically driven transport tool used to transport materials or goods in factories, warehouses and other environments. The moving wheels are connected to the wheel disc through connectors, and the moving wheels are directly in contact with the ground and are responsible for the movement of the vehicle. The multi-line laser profiler is a measuring device that can emit multiple laser lines and capture their reflections to obtain three-dimensional point cloud data of objects. A high-precision camera is a camera that can capture high-resolution images and is used to obtain two-dimensional image information of the wheel. The drive is a device that controls the rotation of the wheel, which can be an electric motor or other type of actuator. The control unit is the control center of the system, responsible for processing data from the sensor and controlling the action of the drive.

[0021] Among them, the side of the electric transport vehicle is Figure 3 and Figure 4 shown.

[0022] In some embodiments of the present application, a control unit is used to start a multi-line laser profiler to emit multiple laser lines to the surface of the moving wheel when the vehicle state of the electric transport truck is a parking state; receive three-dimensional wheel point cloud data about the moving wheel fed back by the multi-line laser profiler; receive a two-dimensional wheel image about the moving wheel fed back by a high-precision camera; perform multimodal data fusion analysis based on the three-dimensional wheel point cloud data and the two-dimensional wheel image to obtain the current actual angle of the moving wheel; and control the drive to rotate according to the current actual angle to rotate the moving wheel to a preset correction position point.

[0023] Among them, the vehicle state is the current working mode of the vehicle, such as driving, parking, etc. The moving wheels are the actual driving wheels of the electric transport truck, which can rotate and move. The three-dimensional point cloud data is the three-dimensional information of the wheel surface obtained by the multi-line laser profiler. The two-dimensional image is the wheel image obtained by a high-precision camera. The current actual angle is the actual spatial direction angle of the wheel, which is used to determine whether the wheel needs to be adjusted. The preset correction position is the predetermined position that the wheel needs to be automatically adjusted to, specifically the upright position of the wheel, which is parallel to the vehicle body.

[0024] For example, when the electric transport truck stops running, the control unit detects that the vehicle is parked. The control unit starts the multi-line laser profiler and emits multiple laser lines to the surface of the moving wheel. The multi-line laser profiler scans the wheel, obtains the three-dimensional point cloud data of the wheel, and feeds it back to the control unit. At the same time, the high-precision camera takes the wheel image and obtains the two-dimensional image data of the wheel. The control unit fuses and analyzes the three-dimensional point cloud data and the two-dimensional image data to calculate the current actual angle of the wheel. The analysis results show that there is a deviation between the current angle of the wheel and the preset correction position. The control unit sends a command to the driver based on the calculated deviation angle to drive the wheel to rotate. The driver executes the command and accurately adjusts the wheel angle to correct it to the preset position.

[0025] In some embodiments of the present application, a specific process of performing multimodal data fusion analysis based on the three-dimensional point cloud data of the wheel and the two-dimensional image of the wheel to obtain the current actual angle of the mobile wheel includes: using a filtering algorithm to smooth the three-dimensional point cloud data of the wheel to obtain smoothed three-dimensional point cloud data of the wheel; performing feature extraction on the smoothed three-dimensional point cloud data of the wheel to obtain the shape, size, edge position and angle information of the mobile wheel; determining the three-dimensional center position, edge points and angle change points of the mobile wheel based on the shape, size, edge position and angle information of the mobile wheel; based on the three-dimensional center position, edge points and angle change points, Generate the inclination angle of the mobile wheel; perform grayscale processing, edge detection and background separation on the two-dimensional image of the wheel to extract the wheel contour information, the wheel contour information includes the overall appearance or edge features or marking points; determine the rotation angle of the mobile wheel according to the wheel contour information; based on the inclination angle and the rotation angle, determine the comprehensive angle of the mobile wheel in the preset spatial coordinate system, the two straight lines forming the preset correction position are the horizontal axis and the vertical axis of the preset spatial coordinate system, the forward direction of the electric transport vehicle coincides with the vertical axis, and the wheel center of the mobile wheel is the origin of the preset spatial coordinate system; the comprehensive angle is used as the current actual angle of the mobile wheel.

[0026] Specifically, point cloud data contains the three-dimensional coordinate information of each point on the surface of an object, so the shape and size of the object can be measured by calculating the distance between points or fitting a geometric model. Point cloud data can be used to identify the edge points of an object through an edge detection algorithm. For example, curvature calculation or deep learning methods based on point clouds can detect the edge features of the surface of an object. By fitting a plane or calculating the geometric features in the point cloud data, the angle information of the surface of the object can be obtained. For example, after fitting a plane using point cloud data, the angle information can be obtained by calculating the angle between the planes.

[0027] Among them, the filtering algorithm is a signal processing technology used to remove noise from the data and smooth the data curve. The three-dimensional point cloud data is the data of the wheel surface shape and size obtained by three-dimensional scanning or measurement. Feature extraction is to extract useful information from the data, such as shape, size, edge position and angle. The three-dimensional center position is the position of the geometric center point of the wheel in three-dimensional space. The edge point is a significant point on the wheel profile, usually located at the edge or corner of the profile. The angle change point is a point on the wheel profile where the angle changes significantly. The tilt angle is the degree of inclination of the wheel relative to the horizontal plane. The two-dimensional image is a planar image of the wheel, such as a photo taken by a camera. Grayscale processing is to convert a color image into a grayscale image to simplify data processing. Edge detection is an algorithm for identifying the edges of objects in an image. Background separation is a technology for separating the foreground and background from an image. The contour information includes information about the overall appearance of the wheel, edge features, and markers. The rotation angle is the angle at which the wheel rotates around its central axis. The comprehensive angle is the final angle of the wheel in the spatial coordinate system after the tilt angle and the rotation angle are combined. The preset spatial coordinate system is the reference coordinate system set for the electric transporter. The current actual angle is the actual spatial direction angle of the wheel and is used for control and adjustment.

[0028] In an embodiment of the present application, by using a filtering algorithm to smooth the three-dimensional point cloud data of the wheel, the noise and interference in the data can be effectively removed, thereby obtaining more accurate and reliable three-dimensional point cloud data of the wheel. This step is crucial for subsequent feature extraction because it can ensure that the extracted features more accurately reflect the actual shape and size of the wheel. Furthermore, feature extraction based on the smoothed three-dimensional point cloud data can obtain the shape, size, edge position and angle information of the wheel, which is the basis for determining the three-dimensional center position, edge points and angle change points of the wheel. By determining these key points, the inclination angle of the wheel can be generated, providing important data for subsequent wheel posture analysis.

[0029] In an embodiment of the present application, the two-dimensional image of the wheel is grayed, edge detected, and background separated, and the contour information of the wheel, including the overall appearance, edge features, and marking points, can be extracted. This information can help determine the rotation angle of the wheel, further enriching the description of the wheel posture. Finally, based on the tilt angle and the rotation angle, the comprehensive angle of the wheel in the preset spatial coordinate system can be determined. The determination of this comprehensive angle enables the current position and posture of the wheel to be accurately described and controlled. Specifically, the two straight lines of the preset return position are used as the horizontal axis and the vertical axis of the coordinate system respectively, the forward direction of the electric transport vehicle coincides with the vertical axis, and the center of the wheel is the origin of the coordinate system. Such a setting makes the control and positioning of the wheel more intuitive and accurate. Using the comprehensive angle as the current actual angle of the wheel can provide an accurate reference for the control and adjustment of the wheel, ensuring that the wheel can be accurately rotated to the preset return position, thereby improving the control accuracy and reliability of the entire system.

[0030] In some embodiments of the present application, the specific process of determining the three-dimensional center position, edge point and angle change point of the mobile wheel based on the shape, size, edge position and angle information of the mobile wheel includes: analyzing the shape and size information of the mobile wheel to determine the wheel profile of the mobile wheel; calculating the center of mass of the wheel profile of the mobile wheel, and taking the center of mass as the three-dimensional center position of the mobile wheel in the three-dimensional space; calculating the curvature of each contour point on the contour line of the wheel contour through the edge position; taking the contour point with the largest curvature change as the edge point; calculating the angle change of each contour point on the contour line of the wheel contour through the angle information; and taking the contour point with the largest angle change as the angle change point.

[0031] The formula for calculating the center of mass of the wheel profile is:

[0032] The contour of the moving wheel is given by Points( ) and the center of mass is ( ),( ) is the contour of the moving wheel The coordinates of the points, is the total number of contour points of the moving wheel; where, Edge point( ) is calculated as:

[0033] in, The contour of the moving wheel provided for the edge position The coordinates of the contour points, and For the The first-order derivative of the contour point, that is, the contour at and The slope in the direction, and For the The second-order derivative of the contour point, that is, the contour at and The rate of change of the slope in the direction is used to reflect the profile of the moving wheel in and The curvature changes in the direction; among them, Angle change point The calculation formula is:

[0034] in, The angle information provided by The angle change of each contour point.

[0035] In some embodiments of the present application, the specific process of generating the inclination angle of the mobile wheel based on the three-dimensional center position, edge points and angle change points includes: standardizing the three-dimensional center position, edge points and angle change points to eliminate the dimensional differences between different features, and obtain the target three-dimensional center position, target edge points and target angle change points; extracting the three-dimensional center position features from the target three-dimensional center position; extracting edge features from the target edge points; extracting angle change features from the target angle change points; performing feature splicing processing on the three-dimensional center position features, edge features and angle change features to obtain a comprehensive feature vector that can be understood by a pre-trained inclination angle quantization model; inputting the comprehensive feature vector into the pre-trained inclination angle quantization model to output the inclination angle of the mobile wheel.

[0036] Among them, the three-dimensional center position is the center point of the wheel in three-dimensional space, which is used to determine the position and posture of the wheel. The edge point is the key point on the wheel contour, usually located at the edge or corner of the wheel, and is used to determine the shape and position of the wheel. The angle change point is the point on the wheel contour where the angle changes significantly, which is used to determine the inclination and rotation state of the wheel. Grayscale processing is the process of converting a color image into a grayscale image, which simplifies the image data and facilitates subsequent processing. Edge detection is an algorithm for identifying the edges of objects in an image, which is used to extract the contour information of the wheel. Background separation is a technology that separates the foreground and background from an image, which is used to extract the contour information of the wheel. The contour information includes the overall appearance of the wheel, edge features, and information of marker points, which is used to determine the rotation angle of the wheel.

[0037] Among them, for example Figure 5 As shown, the pre-trained tilt angle quantization model includes a feature extraction layer, a feature fusion layer, an angle calculation layer, and an angle output layer.

[0038] In some embodiments of the present application, the comprehensive feature vector is input into a pre-trained tilt angle quantization model, and the specific process of outputting the tilt angle of the mobile wheel includes: the feature extraction layer extracts the feature representations of the three-dimensional center position feature, the edge feature and the angle change feature from the comprehensive feature vector respectively; the feature fusion layer fuses the feature representation with the preset enhanced feature to form a fused feature vector, and the preset enhanced feature includes the radius feature, the width feature and the thickness feature of the mobile wheel; the angle calculation layer calculates based on the fused feature to obtain the angle parameter; the angle parameter is decoded and processed to convert it into an actual physical angle value to obtain the tilt angle of the mobile wheel; wherein the angle parameter The calculation formula is as follows:

[0039] in, is the angle parameter, are the model weights of the pre-trained tilt angle quantization model, is the bias vector of the model, is the fusion feature vector;

[0040] in, is the inclination angle of the moving wheel, is the mean value of the tilt angle, is the standard deviation of the tilt angle.

[0041] Specifically, the specific process of generating a pre-trained tilt angle quantization model includes: collecting historical three-dimensional point cloud data of different moving wheels in a parking state, and based on the historical three-dimensional point cloud data, determining the historical wheel shape, historical size, historical edge position and historical angle information of the moving wheel; obtaining the historical tilt angles of different moving wheels in a parking state as tilt angle labels of different moving wheels; associating the tilt angle labels of different moving wheels with the historical three-dimensional point cloud data of different moving wheels in a parking state to obtain model training samples; using a neural network to create a tilt angle quantization model; inputting the model training samples into the pre-trained tilt angle quantization model, and outputting the loss value of the model; when the loss value of the model reaches the minimum, generating a pre-trained tilt angle quantization model.

[0042] Specifically, when the loss value of the model has not reached the minimum, continue to execute the step of inputting the model training samples into the pre-trained tilt angle quantization model until the loss value of the model reaches the minimum.

[0043] In some embodiments of the present application, the specific process of determining the rotation angle of the mobile wheel according to the wheel profile information includes: when there are marking points in the wheel profile information, extracting the position coordinates of the marking points in the two-dimensional image of the wheel; analyzing the position changes of the marking points at different time points through the position coordinates; or, when there are edge features in the wheel profile information, determining the edge position points on the mobile wheel based on the edge features; analyzing the position changes of the edge position points at different time points; or, when there is an overall appearance in the wheel profile information, extracting the outline of the mobile wheel from the overall appearance; analyzing the position changes of the outline at different time points; and calculating the rotation angle of the mobile wheel according to the position changes; wherein the calculation formula for the rotation angle is:

[0044] in,( )and( ) are the position coordinates of the marking point, edge position point, and contour at different time points.

[0045] The tilt angle is obtained by rotating around the Z axis of the preset spatial coordinate system, and the rotation angle is obtained by rotating around the Y axis of the preset spatial coordinate system.

[0046] In some embodiments of the present application, the specific process of determining the comprehensive angle of the mobile wheel in the preset spatial coordinate system based on the tilt angle and the rotation angle includes: calculating the rotation matrix around the Z axis and the Y axis of the preset spatial coordinate system according to the tilt angle and the rotation angle; wherein,

[0047]

[0048] According to the rotation matrix around the Z axis and Y axis of the preset spatial coordinate system, a comprehensive rotation matrix is ​​calculated; wherein the comprehensive rotation matrix The calculation formula is:

[0049] in, is the tilt angle around the Z axis, is the rotation angle around the Y axis, and They are the rotation matrices around the Z axis and the Y axis respectively; Based on the comprehensive rotation matrix, calculate the comprehensive angle of the mobile wheel in the preset spatial coordinate system ;in,

[0050] in, is the trace of the rotation matrix.

[0051] In some embodiments of the present application, according to the current actual angle, the specific process of controlling the driver to rotate the mobile wheel to the preset return position point includes: calculating the deviation between the current actual angle and the angle of the preset return position, and the deviation angle will be used to guide the adjustment action of the driver to ensure that the wheel can be accurately rotated to the preset position. According to the calculated angle deviation, the control unit sends a corresponding control signal to the driver. After receiving the signal, the driver adjusts its output to achieve accurate rotation of the wheel. Specifically, it involves the generation of PWM (pulse width modulation) signals, which are used to control the speed and direction of the motor. The driver rotates the wheel according to the instructions of the control unit so that it rotates from the current position to the preset return position. During this process, the control unit may also monitor the position and angle of the wheel in real time to ensure the accuracy of the rotation. Once the wheel rotates to the preset position, the control unit will stop the output of the driver and complete the entire return process.

[0052] In some embodiments, the connection piece between the wheel disc and the mobile wheel can be fixed on the frame of the mobile transport vehicle.

[0053] In the embodiment of the present application, the control unit starts the multi-line laser profiler when the vehicle is parked, emits laser lines to the wheel surface, receives feedback of the three-dimensional point cloud data of the wheel, and receives the two-dimensional image data fed back by the high-precision camera. The three-dimensional point cloud data can provide accurate shape and size information of the wheel. This data can capture the tiny details and complex shapes of the surface of the object, thereby achieving high-precision measurement. At the same time, the two-dimensional image can provide the overall appearance and edge features of the wheel. By fusing these two types of data for analysis, the accuracy of the wheel alignment control system can be improved, thereby achieving high-precision wheel alignment, thereby improving the accuracy of wheel alignment.

[0054] See also Figure 6 , which is a flow chart of a model training method for a tilt angle quantization model provided in an embodiment of the present application. Figure 6 As shown, the detection method of the embodiment of the present application may include the following steps: S101, collecting historical three-dimensional point cloud data of different mobile wheels in a parking state, and determining historical wheel shape, historical size, historical edge position and historical angle information of the mobile wheel based on the historical three-dimensional point cloud data; S102, obtaining historical tilt angles of different moving wheels in a parking state as tilt angle labels of different moving wheels; S103, associating the inclination angle labels of different moving wheels with the historical three-dimensional point cloud data of different moving wheels in a parking state to obtain a model training sample; S104, using a neural network to create a tilt angle quantization model; S105, inputting the model training sample into the pre-trained tilt angle quantization model, and outputting the loss value of the model; S106, when the loss value of the model reaches the minimum, generate a pre-trained tilt angle quantization model.

[0055] Wherein, when the loss value of the model has not reached the minimum, the step of inputting the model training samples into the pre-trained tilt angle quantization model is continued until the loss value of the model reaches the minimum.

[0056] It should be noted that the specific control logic of steps S101 to S106 can be found in the specific implementation logic of the system embodiment, which will not be repeated here.

[0057] In the embodiment of the present application, the control unit starts the multi-line laser profiler when the vehicle is parked, emits laser lines to the wheel surface, receives feedback of the three-dimensional point cloud data of the wheel, and receives the two-dimensional image data fed back by the high-precision camera. The three-dimensional point cloud data can provide accurate shape and size information of the wheel. This data can capture the tiny details and complex shapes of the surface of the object, thereby achieving high-precision measurement. At the same time, the two-dimensional image can provide the overall appearance and edge features of the wheel. By fusing these two types of data for analysis, the accuracy of the wheel alignment control system can be improved, thereby achieving high-precision wheel alignment, thereby improving the accuracy of wheel alignment.

[0058] See also Figure 7 , is a flow chart of a wheel alignment control method for an electric transport vehicle according to an embodiment of the present application. Figure 7 As shown, the detection method of the embodiment of the present application may include the following steps: S201, when the vehicle state of the electric transport truck is a parking state, starting a multi-line laser profiler to emit a plurality of laser lines to the surface of the moving wheel; S202, receiving wheel three-dimensional point cloud data about the moving wheel fed back by a multi-line laser profiler; S203, receiving a two-dimensional wheel image of the moving wheel fed back by a high-precision camera; S204, performing multimodal data fusion analysis based on the three-dimensional point cloud data of the wheel and the two-dimensional image of the wheel to obtain the current actual angle of the moving wheel; S205, controlling the driver to rotate according to the current actual angle to rotate the moving wheel to a preset return position point.

[0059] In the embodiment of the present application, the control unit starts the multi-line laser profiler when the vehicle is parked, emits laser lines to the wheel surface, receives feedback of the three-dimensional point cloud data of the wheel, and receives the two-dimensional image data fed back by the high-precision camera. The three-dimensional point cloud data can provide accurate shape and size information of the wheel. This data can capture the tiny details and complex shapes of the surface of the object, thereby achieving high-precision measurement. At the same time, the two-dimensional image can provide the overall appearance and edge features of the wheel. By fusing these two types of data for analysis, the accuracy of the wheel alignment control system can be improved, thereby achieving high-precision wheel alignment, thereby improving the accuracy of wheel alignment.

[0060] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0061] See also Figure 8 , which shows a schematic diagram of the structure of a control unit provided by an exemplary embodiment of the present application. The control unit can be implemented as all or part of the electronic device through software, hardware or a combination of both. The control unit 1 includes a starting module 10, a first receiving module 20, a second receiving module 30, a fusion analysis module 40, and a control module 50.

[0062] The starting module 10 is used to start the multi-line laser profiler when the vehicle state of the electric transport vehicle is a parking state, so as to emit a plurality of laser lines to the surface of the moving wheel; A first receiving module 20 is used to receive wheel three-dimensional point cloud data about the moving wheel fed back by a multi-line laser profiler; The second receiving module 30 is used to receive a two-dimensional wheel image of the moving wheel fed back by a high-precision camera; A fusion analysis module 40 is used to perform multimodal data fusion analysis based on the three-dimensional point cloud data of the wheel and the two-dimensional image of the wheel to obtain the current actual angle of the moving wheel; The control module 50 is used to control the driver to rotate according to the current actual angle, so as to rotate the moving wheel to a preset return position point.

[0063] It should be noted that the wheel alignment control device for an electric transport vehicle provided in the above embodiment only uses the division of the above functional modules as an example when executing the wheel alignment control method for an electric transport vehicle. In actual applications, the above functional distribution can be completed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the wheel alignment control device for an electric transport vehicle provided in the above embodiment and the wheel alignment control method embodiment for an electric transport vehicle belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0064] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0065] In the embodiment of the present application, the control unit starts the multi-line laser profiler when the vehicle is parked, emits laser lines to the wheel surface, receives feedback of the three-dimensional point cloud data of the wheel, and receives the two-dimensional image data fed back by the high-precision camera. The three-dimensional point cloud data can provide accurate shape and size information of the wheel. This data can capture the tiny details and complex shapes of the surface of the object, thereby achieving high-precision measurement. At the same time, the two-dimensional image can provide the overall appearance and edge features of the wheel. By fusing these two types of data for analysis, the accuracy of the wheel alignment control system can be improved, thereby achieving high-precision wheel alignment, thereby improving the accuracy of wheel alignment.

[0066] The present application also provides a computer-readable medium having program instructions stored thereon, and when the program instructions are executed by a processor, the wheel alignment control method of the electric transport vehicle provided by the above-mentioned various method embodiments is implemented.

[0067] The present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the wheel alignment control method for an electric transport vehicle of each of the above method embodiments.

[0068] See also Fig. 9 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Fig. 9 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0069] The communication bus 1002 is used to realize the connection and communication between these components.

[0070] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0071] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0072] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts of the entire electronic device 1000, and executes various functions and processes data of the electronic device 1000 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0073] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may also be optionally at least one storage system located away from the aforementioned processor 1001. As Fig. 9 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a wheel alignment control application program for the electric transport vehicle.

[0074] exist Fig. 9In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 1001 can be used to call the wheel alignment control application of the electric transport vehicle stored in the memory 1005, and specifically perform the following operations: When the vehicle state of the electric transport truck is a parking state, a multi-line laser profiler is started to emit a plurality of laser lines to the surface of the moving wheel; receiving three-dimensional point cloud data of the moving wheel fed back by a multi-line laser profiler; receiving a two-dimensional wheel image of the moving wheel fed back by a high-precision camera; Perform multimodal data fusion analysis based on the three-dimensional point cloud data of the wheel and the two-dimensional image of the wheel to obtain the current actual angle of the moving wheel; According to the current actual angle, the drive is controlled to rotate so as to rotate the moving wheel to the preset return position point.

[0075] In the embodiment of the present application, the control unit starts the multi-line laser profiler when the vehicle is parked, emits laser lines to the wheel surface, receives feedback of the three-dimensional point cloud data of the wheel, and receives the two-dimensional image data fed back by the high-precision camera. The three-dimensional point cloud data can provide accurate shape and size information of the wheel. This data can capture the tiny details and complex shapes of the surface of the object, thereby achieving high-precision measurement. At the same time, the two-dimensional image can provide the overall appearance and edge features of the wheel. By fusing these two types of data for analysis, the accuracy of the wheel alignment control system can be improved, thereby achieving high-precision wheel alignment, thereby improving the accuracy of wheel alignment.

[0076] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program for the wheel alignment control of the electric transport vehicle can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium of the program for the wheel alignment control of the electric transport vehicle can be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0077] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A wheel alignment control system for an electric transport vehicle, characterized in that: The system comprises: The wheels, multi-line laser profiler, high-precision camera, driver and control unit are integrated into the electric transport vehicle; wherein, The wheel comprises a wheel disc and a moving wheel, the top of the wheel disc is fixed on the chassis of the electric transport vehicle; a fixing rod is provided on the connecting piece between the wheel disc and the moving wheel; The multi-line laser profiler and the high-precision camera are installed on the fixed rod and at a preset distance from the wheel surface. A connecting shaft is provided inside the wheel disc, one end of which is connected to the moving wheel and the other end is connected to the driver; wherein, The control unit is respectively connected to the driver, the multi-line laser profiler, and the high-precision camera for communication; wherein, The control unit is used to start the multi-line laser profiler when the vehicle state of the electric transport truck is a parking state, so as to emit multiple laser lines to the surface of the mobile wheel; receive the three-dimensional wheel point cloud data about the mobile wheel fed back by the multi-line laser profiler; receive the two-dimensional wheel image about the mobile wheel fed back by the high-precision camera; perform multimodal data fusion analysis based on the three-dimensional wheel point cloud data and the two-dimensional wheel image to obtain the current actual angle of the mobile wheel; and control the driver to rotate according to the current actual angle to rotate the mobile wheel to a preset correction position point.

2. The system according to claim 1, characterized in that The performing multimodal data fusion analysis according to the three-dimensional point cloud data of the wheel and the two-dimensional image of the wheel to obtain the current actual angle of the moving wheel includes: Using a filtering algorithm to smooth the wheel three-dimensional point cloud data to obtain smoothed wheel three-dimensional point cloud data; Performing feature extraction on the smoothed three-dimensional point cloud data of the wheel to obtain the shape, size, edge position and angle information of the mobile wheel; Determine the three-dimensional center position, edge point and angle change point of the moving wheel according to the shape, size, edge position and angle information of the moving wheel; generating a tilt angle of the moving wheel based on the three-dimensional center position, edge points and angle change points; graying, edge detection and background separation are performed on the two-dimensional wheel image to extract wheel profile information, wherein the wheel profile information includes overall appearance or edge features or marking points; determining a rotation angle of the mobile wheel according to the wheel profile information; Based on the tilt angle and the rotation angle, the comprehensive angle of the mobile wheel in the preset spatial coordinate system is determined, the two straight lines forming the preset corrected position are the horizontal axis and the vertical axis of the preset spatial coordinate system, the forward direction of the electric transport vehicle coincides with the vertical axis, and the wheel center of the mobile wheel is the origin of the preset spatial coordinate system; The integrated angle is used as the current actual angle of the moving wheel.

3. The system according to claim 2, characterized in that The step of determining the three-dimensional center position, edge point, and angle change point of the moving wheel according to the shape, size, edge position, and angle information of the moving wheel comprises: analyzing the shape and size information of the mobile wheel to determine the wheel profile of the mobile wheel; Calculating the center of mass of the wheel profile of the mobile wheel, and using the center of mass as the three-dimensional center position of the mobile wheel in the three-dimensional space; Calculating the curvature of each contour point on the contour line of the wheel contour through the edge position; The contour point with the largest curvature change is regarded as the edge point; Calculating the angle change of each contour point on the contour line of the wheel contour by using the angle information; The contour point with the largest angle change is taken as the angle change point.

4. The system according to claim 2, characterized in that The step of generating the tilt angle of the moving wheel based on the three-dimensional center position, the edge point and the angle change point comprises: The three-dimensional center position, edge point and angle change point are standardized to eliminate the dimensional difference between different features, and the target three-dimensional center position, target edge point and target angle change point are obtained; Extracting a three-dimensional center position feature from the three-dimensional center position of the target; Extracting edge features from the target edge points; Extracting angle change features from the target angle change points; Performing feature splicing processing on the three-dimensional center position feature, the edge feature, and the angle change feature to obtain a comprehensive feature vector that can be understood by a pre-trained tilt angle quantization model; The comprehensive feature vector is input into a pre-trained tilt angle quantization model, and the tilt angle of the moving wheel is output.

5. The system according to claim 4, characterized in that The pre-trained tilt angle quantization model includes a feature extraction layer, a feature fusion layer, an angle calculation layer and an angle output layer; The step of inputting the comprehensive feature vector into a pre-trained tilt angle quantization model and outputting the tilt angle of the moving wheel comprises: The feature extraction layer extracts feature representations of three-dimensional center position features, edge features, and angle change features from the comprehensive feature vector respectively; The feature fusion layer fuses the feature representation with preset enhanced features to form a fused feature vector, wherein the preset enhanced features include a radius feature, a width feature, and a thickness feature of the moving wheel; The angle calculation layer performs calculation based on the fusion features to obtain angle parameters; The angle parameter is decoded and converted into an actual physical angle value to obtain the inclination angle of the moving wheel.

6. The system according to claim 4, characterized in that Follow these steps to generate a pre-trained tilt angle quantization model, including: Collecting historical three-dimensional point cloud data of different mobile wheels in a parking state, and determining historical wheel shape, historical size, historical edge position and historical angle information of the mobile wheel based on the historical three-dimensional point cloud data; Obtain historical tilt angles of different moving wheels in a parking state as tilt angle labels of different moving wheels; The tilt angle labels of different moving wheels are associated with the historical three-dimensional point cloud data of different moving wheels in the parking state to obtain model training samples; A neural network is used to create a tilt angle quantification model; Inputting the model training sample into the pre-trained tilt angle quantization model, and outputting the loss value of the model; When the loss value of the model reaches the minimum, a pre-trained tilt angle quantization model is generated.

7. The system according to claim 6, characterized in that The system further comprises: In the case where the loss value of the model has not reached the minimum, the step of inputting the model training samples into the pre-trained tilt angle quantization model is continued until the loss value of the model reaches the minimum.

8. The system according to claim 2, characterized in that Determining the rotation angle of the moving wheel according to the wheel profile information includes: In the case where the marking point exists in the wheel profile information, extracting the position coordinates of the marking point in the two-dimensional image of the wheel; Analyzing the position changes of the marking points at different time points through the position coordinates; or, In the case where the wheel profile information has the edge feature, determining an edge position point on the moving wheel based on the edge feature; Analyze the position changes of the edge position points at different time points; or, In the case where the wheel profile information has the overall appearance, extracting the profile of the moving wheel from the overall appearance; analyzing the position changes of the contour at different time points; According to the position change, the rotation angle of the moving wheel is calculated.

9. The system according to claim 2, characterized in that The tilt angle is obtained by rotating around the Z axis of the preset spatial coordinate system, and the rotation angle is obtained by rotating around the Y axis of the preset spatial coordinate system; The step of determining the comprehensive angle of the moving wheel in a preset spatial coordinate system based on the tilt angle and the rotation angle includes: Calculate the rotation matrix around the Z axis and Y axis of the preset spatial coordinate system according to the tilt angle and the rotation angle; Calculating a comprehensive rotation matrix according to the rotation matrices around the Z axis and the Y axis of the preset spatial coordinate system; Based on the comprehensive rotation matrix, the comprehensive angle of the moving wheel in the preset space coordinate system is calculated.

10. A wheel alignment control method for an electric transport vehicle implemented using the system according to any one of claims 1 to 9, characterized in that: Applied to the control unit of the electric transport vehicle, the method comprises: When the vehicle state of the electric transport vehicle is a parking state, starting the multi-line laser profiler to emit a plurality of laser lines to the surface of the moving wheel; receiving wheel three-dimensional point cloud data about the mobile wheel fed back by the multi-line laser profiler; receiving a two-dimensional wheel image of the mobile wheel fed back by the high-precision camera; Performing multimodal data fusion analysis based on the three-dimensional point cloud data of the wheel and the two-dimensional image of the wheel to obtain the current actual angle of the moving wheel; According to the current actual angle, the driver is controlled to rotate so as to rotate the moving wheel to a preset home position point.

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