Unmanned vibration roller driving control method, device and system

Abstract

The invention relates to the technical field of unmanned vibration rollers, and particularly discloses an unmanned vibration roller driving control method, device and system, and the method comprises the steps: obtaining the positioning information of an unmanned vibration roller; processing the positioning information through an unscented Kalman filter to obtain a real-time state estimation value of the unmanned vibration roller; a trajectory planning result of the unmanned vibration roller is obtained, error calculation is carried out according to the trajectory planning result and the real-time state estimation value, and an error result is obtained; the error result is input into an LQR controller, and unmanned vibration roller control parameters are obtained; and according to the unmanned vibration roller control parameters, driving of the unmanned vibration roller is controlled. According to the unmanned vibration roller driving control method provided by the invention, the robustness of unmanned vibration roller control and the accuracy of trajectory tracking can be improved in a complex environment.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned vibratory rollers, and in particular to an unmanned vibratory roller driving control method, an unmanned vibratory roller driving control device, and an unmanned vibratory roller driving control system. Background Art

[0002] With the development of modern industrialization, vibratory rollers are widely used in infrastructure construction such as roads, airports, ports, and dams due to their high production efficiency and excellent performance. However, with the rapid advancement of big data and artificial intelligence technologies, unmanned vibratory roller technology faces new challenges, requiring greater efficiency, safety, and reliability.

[0003] Accurately tracking the driving trajectory is a key issue in unmanned vibratory roller control technology. Trajectory tracking involves designing the horizontal motion of the unmanned vibratory roller, including both longitudinal and lateral movement. Currently, traditional PID control (proportional-integral-differential control) is widely used due to its simplicity, ease of implementation, and wide applicability. However, due to factors such as the high-frequency vibration disturbances of the vibratory roller and the complex nature of the road surface under construction, PID control is particularly sensitive to interference.

[0004] There are some unmanned vibratory roller control methods in the existing technology, but they all have some insurmountable flaws. For example, one existing implementation method is to calculate the actual distance error of the roller's trajectory tracking based on the roller's actual position and the roller's target trajectory; secondly, the total disturbance of the distance error is obtained based on the disturbance observer, and an outer loop control is constructed to calculate the target heading angle; then, the target heading angle of the roller is calculated using the target heading angle and the total heading disturbance obtained by the instantaneous disturbance observer, as well as the heading angle disturbance obtained based on the steering system model parameter learner; finally, the steering wheel angle controller controls the steering wheel so that the actual heading angle of the roller approaches the target value. Due to the addition of the disturbance observer, the control system used in this technology requires more advanced hardware and software, resulting in increased costs. It also makes the control system more complex, requiring more frequent maintenance and more complex fault handling procedures. Another existing implementation method uses sensors such as lidar, cameras, and millimeter-wave radar to update the occupancy grid map, generate collision-free constraints with the static environment, identify the type of dynamic obstacles, determine their positions and contours, and establish corresponding collision-free constraints. Secondly, based on the kinematic model of the roller, the control quantity sequence in the predicted time domain is solved online and guided by the underlying mechanism to achieve precise control of the unmanned roller. The model-predictive contour control method used in this technology does not consider the impact of noise disturbances under complex working conditions, which will challenge the robustness of the controller in actual working conditions. In addition, this method requires the completion of model prediction and optimization calculations within each control cycle, which places high demands on the real-time performance of the calculation and system.

[0005] In addition, the working surface of the vibratory roller may be very rugged, including steep slopes, soft ground or unstable soil and rocks, which poses a challenge to the robustness of the controller.

[0006] Therefore, how to provide a method for improving the robustness of unmanned vibratory roller control and the accuracy of trajectory tracking in complex environments has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0007] The present invention provides an unmanned vibratory roller driving control method, an unmanned vibratory roller driving control device and an unmanned vibratory roller driving control system, which solve the problem in related technologies that the robustness of unmanned vibratory roller control and the accuracy of trajectory tracking cannot be improved in complex environments.

[0008] As a first aspect of the present invention, a method for controlling the operation of an unmanned vibratory roller is provided, comprising:

[0009] Obtaining positioning information of the unmanned vibratory roller, the positioning information including at least vehicle body coordinates, heading angle, and hinge point steering angle;

[0010] Processing the positioning information through an unscented Kalman filter to obtain a real-time state estimation value of the unmanned vibratory roller;

[0011] Obtaining a trajectory planning result of the unmanned vibratory roller, and performing error calculation based on the trajectory planning result and the real-time state estimation value to obtain an error result;

[0012] The error result is input into the LQR controller to obtain the control parameters of the unmanned vibratory roller;

[0013] The movement of the unmanned vibratory roller is controlled according to the unmanned vibratory roller control parameters.

[0014] Furthermore, the positioning information is processed by an unscented Kalman filter, including:

[0015] Construct an unscented Kalman filter;

[0016] Inputting the positioning information into the unscented Kalman filter for processing to obtain vehicle body coordinate estimation values;

[0017] The vehicle body coordinate estimation value is converted into coordinate points according to the vehicle body size information to obtain a real-time position estimation value of the roller.

[0018] Furthermore, an unscented Kalman filter is constructed, including:

[0019] The motion of the unmanned vibratory roller is simplified into plane motion, and the state space equation of the vibratory roller and vehicle body is established based on the kinematic model;

[0020] Determining a state transfer function based on the vibration roller-car body state space equation;

[0021] The sigma point is predicted according to the state transfer function to obtain the Kalman gain and covariance matrix to complete the construction of the unscented Kalman filter.

[0022] Furthermore, the expression of the vibration roller-vehicle state space equation is:

[0023] ,

[0024] in, represents the vehicle body center of mass position velocity, represents the velocity component of the vehicle body in the X direction in the global coordinate system, represents the velocity component of the vehicle body in the Y direction in the global coordinate system, represents the angular velocity of the vehicle body’s center of mass in the global coordinate system, Indicates the angle difference between the body and the roller, It represents the distance from the center of mass of the roller to the hinge point, Indicates the distance from the center of mass of the vehicle body to the hinge point;

[0025] The expression of the state transfer function is:

[0026] ,

[0027] in, , state variables , Indicates the moment, represents the discrete system sampling time; Indicates the coordinate of the vehicle body's center of mass on the X-axis in the global coordinate system; Indicates the Y-axis coordinate of the vehicle body's center of mass in the global coordinate system; Indicates the heading angle of the vehicle body's center of mass.

[0028] Furthermore, the sigma point is predicted according to the state transfer function to obtain the Kalman gain and covariance matrix, including:

[0029] Initialization to generate sigma points and determine the state estimation vector and covariance matrix at the initialization moment;

[0030] Performing a nonlinear transformation on the sigma point through the state transfer function to obtain a predicted state estimate of the sigma point;

[0031] Calculate the mean and covariance of the predicted state based on the state estimate of the predicted sigma point;

[0032] Calculate the Kalman gain based on the mean and covariance of the predicted state and update the state estimate and covariance matrix.

[0033] Furthermore, the error result is input into the LQR controller to obtain the unmanned vibratory roller control parameters, including:

[0034] Construct an LQR controller to determine the control quantity;

[0035] The error result is controlled according to the control amount to obtain the control parameters of the unmanned vibrating roller.

[0036] Furthermore, an LQR controller is constructed to determine the control quantity, including:

[0037] Construct error-based state-space equations;

[0038] Determine the cost function and output weight matrix;

[0039] Designing an LQR controller based on the error-based state-space equation, the cost function, and the output weight matrix;

[0040] A control variable is determined according to the LQR controller.

[0041] Furthermore, the cost function and the output weight matrix are determined, including:

[0042] Discretizing the error-based state space equation to obtain a linear time-varying prediction model of the vibratory roller;

[0043] Determine the LQR cost calculation equation based on the linear time-varying prediction model of the vibrating roller;

[0044] An output weight matrix is determined according to the LQR cost calculation equation.

[0045] As another aspect of the present invention, there is provided an unmanned vibratory roller travel control device for implementing the unmanned vibratory roller travel control method described above, comprising:

[0046] A positioning information acquisition module is used to obtain positioning information of the unmanned vibratory roller, wherein the positioning information includes at least vehicle body coordinates, heading angle, and hinge point steering angle;

[0047] A Kalman filter module is used to process the positioning information through an unscented Kalman filter to obtain a real-time state estimation value of the unmanned vibratory roller;

[0048] an error calculation module, configured to obtain a trajectory planning result of the unmanned vibratory roller, and perform error calculation based on the trajectory planning result and the real-time state estimation value to obtain an error result;

[0049] An LQR control module is used to input the error result into an LQR controller to obtain control parameters of an unmanned vibratory roller;

[0050] The driving control module is used to control the driving of the unmanned vibratory roller according to the control parameters of the unmanned vibratory roller.

[0051] As another aspect of the present invention, there is provided an unmanned vibratory roller travel control system, comprising: a positioning device, a decision-making device, an execution device, and the unmanned vibratory roller travel control device described above, wherein the positioning device, the decision-making device, and the execution device are all communicatively connected to the unmanned vibratory roller travel control device;

[0052] The positioning device is used to collect positioning information of the unmanned vibrating roller;

[0053] The decision-making device is used to plan the trajectory of the unmanned vibratory roller and obtain the trajectory planning result;

[0054] The unmanned vibratory roller travel control device is used to process positioning information according to an unscented Kalman filter, perform error calculation based on trajectory planning results and real-time state estimation values, and input the error results into an LQR controller to obtain unmanned vibratory roller control parameters;

[0055] The execution device is used to control the travel of the unmanned vibratory roller according to the unmanned vibratory roller control parameters.

[0056] The unmanned vibratory roller travel control method provided by the present invention processes the acquired positioning information through an unscented Kalman filter, and performs error calculation based on the planning result and the real-time state estimation value output by the unscented Kalman filter, and inputs the error result into the LQR controller to obtain the unmanned vibratory roller control parameters, and realizes the control of the unmanned vibratory roller travel based on the unmanned vibratory roller control parameters. This unmanned vibratory roller travel control method combines the unscented Kalman filter with the LQR controller, which can enable the unmanned vibratory roller to obtain a relatively accurate control error value under the condition that the system measurement parameters contain disturbances, and realize accurate tracking of the desired trajectory according to the proposed control emphasis. Therefore, the unmanned vibratory roller travel control method of the present invention can improve the robustness of the unmanned vibratory roller control and the accuracy of trajectory tracking in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention, but do not constitute a limitation of the present invention.

[0058] Figure 1 This is a flow chart of the unmanned vibratory roller driving control method provided by the present invention.

[0059] Figure 2 This is a flow chart of the unscented Kalman filter provided by the present invention for processing positioning information.

[0060] Figure 3 The present invention provides a flowchart for constructing an unscented Kalman filter.

[0061] Figure 4 The present invention provides a flow chart for obtaining control parameters of an unmanned vibrating roller.

[0062] Figure 5 This is a data flow diagram of the unmanned vibratory roller driving control method provided by the present invention.

[0063] Figure 6 This is a structural block diagram of the unmanned vibratory roller travel control device provided by the present invention.

[0064] Figure 7 This is a structural block diagram of the unmanned vibratory roller travel control system provided by the present invention.

[0065] Figure 8 This is a structural block diagram of a specific implementation scheme of the unmanned vibratory roller travel control system provided by the present invention. DETAILED DESCRIPTION

[0066] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0067] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0068] It should be noted that the terms "first," "second," and the like in the specification and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate for the embodiments of the present invention described herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatuses.

[0069] Currently, the road surface where unmanned vibratory rollers operate may be very rugged, including steep slopes, soft ground, or unstable soil and rocks, which poses a challenge to the robustness of the controller. In view of the shortcomings and problems of traditional unmanned vibratory roller control methods in complex environments, such as poor robustness and difficulty in adjusting system parameters, which lead to poor control effects, this embodiment provides an unmanned vibratory roller driving control method. Figure 1 Flowchart of the unmanned vibrating roller driving control method provided by an embodiment of the present invention. Figure 1 Shown, including:

[0070] S100, obtaining positioning information of the unmanned vibratory roller, wherein the positioning information includes at least vehicle body coordinates, heading angle, and steering angle of the hinge point;

[0071] In an embodiment of the present invention, the positioning device is used to collect positioning information of the unmanned vibratory roller in real time, and the positioning information at least includes vehicle body coordinates, heading angle and hinge point steering angle.

[0072] Specifically, the positioning device can determine the precise position and heading of the unmanned vibratory roller, and may include a GPS global positioning system and an inertial measurement unit (IMU), wherein the GPS receiver and IMU can be installed inside the cab of the unmanned vibratory roller, and the positioning antenna is located on the top of the cab.

[0073] S200, processing the positioning information through an unscented Kalman filter to obtain a real-time state estimation value of the unmanned vibratory roller;

[0074] In an embodiment of the present invention, the positioning information is processed by an unscented Kalman filter to obtain a vehicle body coordinate estimate. Since the vehicle body size is fixed, coordinate point conversion can be performed based on the vehicle body coordinate estimate to obtain the position information of the roller at the current moment.

[0075] S300, obtaining a trajectory planning result of the unmanned vibratory roller, and performing error calculation based on the trajectory planning result and the real-time state estimation value to obtain an error result;

[0076] In an embodiment of the present invention, the decision-making device may specifically output a trajectory planning result. The decision-making device may specifically be a controller in the system that generates a trajectory planning result for the unmanned vibratory roller based on real-time perception information. The specific implementation of trajectory planning is well known to those skilled in the art and will not be described in detail here. An error calculation is performed based on the trajectory planning result and the real-time state estimate to obtain an error result.

[0077] S400, inputting the error result into the LQR controller to obtain the control parameters of the unmanned vibratory roller;

[0078] In an embodiment of the present invention, the error result is input to an LQR controller, and the LQR controller can process the error result to obtain control parameters of the unmanned vibratory roller.

[0079] S500: Control the movement of the unmanned vibrating roller according to the unmanned vibrating roller control parameters.

[0080] Specifically, the travel of the unmanned vibratory roller is controlled according to the above-mentioned unmanned vibratory roller control parameters to complete the unmanned vibratory roller travel control process.

[0081] In summary, the unmanned vibratory roller travel control method provided by the present invention processes the acquired positioning information through an unscented Kalman filter, and performs error calculation based on the planning result and the real-time state estimation value output by the unscented Kalman filter, and inputs the error result into the LQR controller to obtain the unmanned vibratory roller control parameters, and realizes the control of the unmanned vibratory roller travel based on the unmanned vibratory roller control parameters. This unmanned vibratory roller travel control method combines the unscented Kalman filter with the LQR controller, which can enable the unmanned vibratory roller to obtain a relatively accurate control error value under the condition that the system measurement parameters contain disturbances, and realize accurate tracking of the desired trajectory according to the proposed control emphasis. Therefore, the unmanned vibratory roller travel control method of the present invention can improve the robustness of the unmanned vibratory roller control and the accuracy of trajectory tracking in complex environments.

[0082] In an embodiment of the present invention, the positioning information is processed by an unscented Kalman filter, such as Figure 2 Shown, including:

[0083] S210, constructing an unscented Kalman filter;

[0084] In the embodiment of the present invention, the positioning information is processed based on the unscented Kalman filter. First, it is necessary to construct the unscented Kalman filter according to the target.

[0085] Specifically, an unscented Kalman filter is constructed, such as Figure 3 Shown, including:

[0086] S211, simplifying the motion of the unmanned vibratory roller into a planar motion, and establishing a vibratory roller-vehicle state space equation based on a kinematic model;

[0087] The expression of the vibration roller-car body state space equation is:

[0088] ,

[0089] in, represents the vehicle body center of mass position velocity, represents the velocity component of the vehicle body in the X direction in the global coordinate system, represents the velocity component of the vehicle body in the Y direction in the global coordinate system, represents the angular velocity of the vehicle body’s center of mass in the global coordinate system, Indicates the angle difference between the body and the roller, It represents the distance from the center of mass of the roller to the hinge point, Indicates the distance from the center of mass of the vehicle body to the hinge point.

[0090] S212, determining a state transfer function according to the vibration roller-vehicle body state space equation;

[0091] In the embodiment of the present invention, the expression of the vibration roller-car body state space equation is discretized to obtain the state transfer function .

[0092] In this embodiment of the present invention, the expression of the state transition function is:

[0093] ,

[0094] in, , state variables , Indicates the moment, represents the discrete system sampling time; Indicates the coordinate of the vehicle body's center of mass on the X-axis in the global coordinate system; Indicates the Y-axis coordinate of the vehicle body's center of mass in the global coordinate system; Indicates the heading angle of the vehicle body's center of mass.

[0095] S213 , performing prediction processing on the sigma point according to the state transfer function to obtain the Kalman gain and covariance matrix to complete the construction of the unscented Kalman filter.

[0096] In the embodiment of the present invention, a nonlinear transformation is performed on the sigma point based on the state transfer function, and then processing is performed based on the obtained predicted sigma point to complete the construction of the unscented Kalman filter.

[0097] Specifically, the sigma point is predicted according to the state transfer function to obtain the Kalman gain and covariance matrix, including:

[0098] 1) Initialization to generate sigma points and determine the state estimation vector and covariance matrix at the initialization moment;

[0099] initialization, is the initial moment. After initialization, sigma points are generated, which are evenly distributed with the estimated state vector The left and right ends (a total of 2n+1), for the state estimation vector and the covariance matrix have:

[0100] , for ,

[0101] , for ,

[0102] in, represents the scaling parameter, , Represents the state dimension, express The estimated value of the state at the moment is the central sigma point, Indicates the sigma points.

[0103] 2) performing a nonlinear transformation on the sigma point through the state transfer function to obtain a predicted state estimate of the sigma point;

[0104] In the embodiment of the present invention, the expression of the state estimation value of the predicted sigma point is:

[0105] .

[0106] 3) Calculate the mean and covariance of the predicted state based on the state estimate of the predicted sigma point;

[0107] In the embodiment of the present invention, the specific calculation process is:

[0108] ,

[0109] ,

[0110] in, and Both represent the prediction weights of sigma points, represents the process noise covariance matrix;

[0111] ,

[0112] ,

[0113] ,

[0114] in, Used to control the distribution of sigma points, Represents a parameter related to the prior distribution information of the state, adjusting It can improve the accuracy of the covariance matrix. For Gaussian distribution, is optimal, m and c are used to distinguish weight coefficient categories.

[0115] 4) Calculate the Kalman gain based on the mean and covariance of the predicted state and update the state estimate and covariance matrix.

[0116] In the embodiment of the present invention, the predicted sigma point is taken one step forward by the following formula to obtain the k-1 moment predicted k moment measured sigma point :

[0117] ;

[0118] Combined with the k-1 moment prediction k moment measurement sigma point The expression of , and calculate the mean and covariance of the k-1 moment measurement sigma point predicted based on the predicted measurement sigma point one step ahead:

[0119] ,

[0120] ,

[0121] ,

[0122] in, and denote the measurement covariance matrix and the cross covariance matrix between state and measurement, represents the measurement noise covariance matrix.

[0123] Finally, the Kalman gain is calculated and the state estimate and covariance matrix are updated:

[0124] ,

[0125] ,

[0126] ,

[0127] in, represents the Kalman gain at time k after the update, After the update The moment covariance matrix, express The covariance matrix of the state estimation error at the moment, express The true value measured by the sensor at the moment, express The estimated value of the state at that moment.

[0128] S220, inputting the positioning information into the unscented Kalman filter for processing to obtain vehicle body coordinate estimation values;

[0129] In an embodiment of the present invention, the vehicle body coordinate estimation value can be obtained by inputting the above positioning information into an unscented Kalman filter.

[0130] S230 , performing coordinate point conversion on the vehicle body coordinate estimation value according to the vehicle body size information to obtain a real-time position estimation value of the roller.

[0131] Since the vehicle body size information is certain, the vehicle body coordinate estimation value is converted into coordinate points based on the vehicle body size information to obtain the real-time position estimation value of the roller.

[0132] In the embodiment of the present invention, the error result is input into the LQR controller to obtain the control parameters of the unmanned vibrating roller, such as Figure 4 Shown, including:

[0133] S410, constructing an LQR controller to determine a control variable;

[0134] In the embodiment of the present invention, an error-based state space equation can be constructed based on the structure of the unmanned vibratory roller to further determine the control quantity of the LQR controller.

[0135] Specifically, an LQR controller is constructed to determine the control quantity, including:

[0136] (1) Constructing error-based state-space equations;

[0137] Considering that the unmanned vibrating roller is longitudinally symmetrical in structure, and that the tire and steel wheel are in point contact with the ground, and simplifying the motion of the unmanned vibrating roller into plane motion, the velocity of the center of mass of the roller is Breaking it down on the X and Y axes:

[0138] ,

[0139] in, and They represent the velocity of the center of mass in the drum coordinate and the angle between it and the global coordinate X.

[0140] Considering that there is no lateral and longitudinal slip at the hinge point of the vibrating roller, the vector sum of the vehicle body's velocity at the hinge point should be equal to the vector sum of the roller at the hinge point:

[0141] ,

[0142] in, and Respectively represent the distances from the center of mass of the roller and the center of mass of the vehicle body to the hinge point, and represent the angular velocities of the drum and the vehicle body, respectively.

[0143] The velocity vector and Convert to the coordinate axis where the roller is located and obtain:

[0144] ,

[0145] ,

[0146] in, Indicates the angle difference between the body and the roller.

[0147] according to and Combining the two expressions, we can obtain the differential equation of angular velocity in the drum coordinate system:

[0148] ,

[0149] The The expression of the velocity of the center of mass of the drum The expressions of the X-axis and Y-axis are combined to establish the vibration roller-drum state space equation:

[0150] ,

[0151] Since the above-mentioned vibration roller state space equation is a nonlinear equation group, in order to facilitate the solution of the LQR controller, it is linearized by Taylor expansion to obtain the error-based state control equation:

[0152] ,

[0153] in, , , , , ,

[0154] in, Represents the state matrix, which is used to describe the relationship between the system state variables; Represents state variables; Represents the input matrix, which is used to describe the impact of input on states; Represents system input; Indicates the drum speed; represents the hinge angle; Indicates the desired speed of the drum; Indicates the distance from the center of mass of the roller to the hinge point; Indicates the distance from the center of mass of the vehicle body to the hinge point; 、 and Indicates the desired point coordinates and desired heading angle; Indicates the expected curvature of the track point;

[0155] Discretize the error-based state control equation to obtain the linear time-varying prediction model of the vibrating roller:

[0156] ,

[0157] in, , , , ,

[0158] Where T represents the offline system sampling time.

[0159] (2) Determine the cost function and output weight matrix;

[0160] In an embodiment of the present invention, the cost function and the output weight matrix are determined according to the above-mentioned error-based state-space equation.

[0161] Specifically, determine the cost function and output weight matrix, including:

[0162] (21) Discretizing the error-based state space equation to obtain a linear time-varying prediction model of the vibrating roller;

[0163] (22) Determining an LQR cost calculation equation based on the linear time-varying prediction model of the vibrating roller;

[0164] In the embodiment of the present invention, the LQR cost calculation equation is expressed as:

[0165] ,

[0166] Where n=2, , , Represents the control effect matrix, which is used to indicate the importance the controller places on the state variables. Represents the output weight matrix, which is used to measure the impact of different outputs on system performance. The weight coefficient 、 and They are used to adjust the sensitivity of the controller to longitudinal error, lateral error and heading error respectively. and Adjust the speed control amount and the angle control amount respectively. If the lateral deviation change rate or the curvature of the reference path When it is large, a smaller weight can be selected to improve the following performance and reduce the lateral deviation between the actual driving path and the reference path. 、 and corner weights , making the controller more sensitive to the change of the steering angle, reducing the restriction on the front wheel steering angle, and ensuring the tracking accuracy of the vibratory roller; if the lateral deviation change rate or the curvature of the reference path Smaller, choose a larger weight 、 and corner weights , suppressing the steering angle swing. The weight of the longitudinal control , Same thing.

[0167] By designing the output weight matrix and measuring the impact of different outputs on system performance, the performance of the entire system can be further improved and the robustness of the system can be enhanced.

[0168] (23) Determine the output weight matrix according to the LQR cost calculation equation.

[0169] Based on the cost calculation equation designed in the above steps and the output weight matrix, the LQR controller is designed and the lateral and longitudinal control feedback control quantities are calculated to achieve dynamic control of the system:

[0170] Substitute into the discrete time Riccati equation, and when The matrix can be obtained when ,in is the state feedback gain matrix.

[0171] (3) designing an LQR controller based on the error-based state-space equation, the cost function, and the output weight matrix;

[0172] (4) Determine the control variable according to the LQR controller.

[0173] According to the state feedback gain matrix obtained above, the controller gain matrix coefficients are calculated ;Finally, according to the controller gain matrix and current state error information , calculate the horizontal and vertical control quantities , plus the expected value, we can get the actual control quantity :

[0174] ,

[0175] The calculated results Applied to the system, dynamic control of the system can be achieved.

[0176] S420: Control the error result according to the control amount to obtain control parameters of the unmanned vibratory roller.

[0177] like Figure 5 The figure shows the data flow diagram of the unmanned vibratory roller driving control method according to an embodiment of the present invention. Specifically, the positioning device provides the measured values of the vehicle body coordinates, heading angle, and hinge steering angle. First, the unscented Kalman filter (UKF) processes the vehicle body position information and converts the more accurate vehicle body coordinate estimates into coordinate points according to the vehicle body size to calculate the roller position information at the current moment. Second, the desired coordinates obtained after trajectory planning are converted into the desired coordinates. 、 , expected point curvature and expected speed The error is calculated with the estimated value; finally, the above data is transmitted to the LQR controller, and the control quantity transmitted to the controlled object.

[0178] In summary, the unmanned vibratory roller driving control method provided by the present invention has the following advantages:

[0179] 1) Combining an unscented Kalman filter with an LQR controller, the unscented Kalman filter updates the vehicle state estimate in real time, compensating for the impact of vibration roller state measurement noise and improving the accuracy of the LQR controller state error input, thereby improving the accuracy and robustness of trajectory tracking.

[0180] 2) The unscented Kalman filter used in the present invention does not linearize the system state equation when processing the state estimation of the unmanned vibratory roller. Instead, it uses an unscented transformation to infer the posterior probability distribution of the nonlinear process, thus avoiding linearization errors and providing more accurate and reliable state estimation.

[0181] 3) The LQR controller adjusts the system dynamic characteristics through the weight matrices Q and R, and can make trade-offs between multiple performance indicators. In actual engineering applications, it can be adjusted according to different requirements of energy consumption and tracking accuracy, and has strong flexibility and adaptability.

[0182] As another embodiment of the present invention, an unmanned vibrating roller travel control device 100 is provided for implementing the unmanned vibrating roller travel control method described above, wherein: Figure 6 Shown, including:

[0183] A positioning information acquisition module 110 is used to obtain positioning information of the unmanned vibratory roller, wherein the positioning information includes at least vehicle body coordinates, heading angle, and hinge point steering angle;

[0184] A Kalman filter module 120 is configured to process the positioning information using an unscented Kalman filter to obtain a real-time state estimate of the unmanned vibratory roller;

[0185] an error calculation module 130 for obtaining a trajectory planning result of the unmanned vibratory roller and performing error calculation based on the trajectory planning result and the real-time state estimation value to obtain an error result;

[0186] An LQR control module 140 is configured to input the error result into an LQR controller to obtain control parameters of an unmanned vibratory roller;

[0187] The driving control module 150 is used to control the driving of the unmanned vibratory roller according to the control parameters of the unmanned vibratory roller.

[0188] The unmanned vibratory roller travel control device provided by the present invention processes the acquired positioning information through an unscented Kalman filter, and performs error calculation based on the planning result and the real-time state estimation value output by the unscented Kalman filter, and inputs the error result into the LQR controller to obtain the unmanned vibratory roller control parameters, and realizes the control of the unmanned vibratory roller travel based on the unmanned vibratory roller control parameters. This unmanned vibratory roller travel control method combines the unscented Kalman filter with the LQR controller, which enables the unmanned vibratory roller to obtain a relatively accurate control error value under the condition that the system measurement parameters contain disturbances, and realizes the accurate tracking of the desired trajectory according to the proposed control emphasis. Therefore, the unmanned vibratory roller travel control device of the present invention can improve the robustness of the unmanned vibratory roller control and the accuracy of trajectory tracking in complex environments.

[0189] The specific working principle of the unmanned vibratory roller travel control device according to the embodiment of the present invention can be referred to the description of the unmanned vibratory roller travel control method above, which will not be repeated here.

[0190] As another embodiment of the present invention, there is provided an unmanned vibrating roller travel control system 10, wherein, as Figure 7 As shown, it includes: a positioning device 200, a decision device 300, an execution device 400 and the unmanned vibrating roller travel control device 100 mentioned above, and the positioning device 200, the decision device 300 and the execution device 400 are all connected to the unmanned vibrating roller travel control device 100 in communication;

[0191] The positioning device 200 is used to collect positioning information of the unmanned vibrating roller;

[0192] The decision-making device 300 is used to plan the trajectory of the unmanned vibratory roller and obtain the trajectory planning result;

[0193] The unmanned vibratory roller driving control device 100 is used to process the positioning information according to the unscented Kalman filter, perform error calculation based on the trajectory planning result and the real-time state estimation value, and input the error result into the LQR controller to obtain the unmanned vibratory roller control parameters;

[0194] The execution device 400 is used to control the movement of the unmanned vibratory roller according to the unmanned vibratory roller control parameters.

[0195] In an embodiment of the present invention, the positioning device 200 is responsible for determining the precise position and heading of the vibratory roller, and includes a GPS global positioning system and an inertial measurement unit (IMU); wherein the GPS receiver and IMU are located inside the cab, and the positioning antenna is located on top of the cab.

[0196] The execution device 400 is responsible for executing the instructions of the operation calculation unit and controlling the movement and steering of the vibratory roller, including the drive and steering systems. Specifically, the execution device mainly includes a hydraulic steering system and a drive system at the hinge of the rigid body of the vibratory roller. The hydraulic solenoid valve in the steering system receives the steering control signal sent by the operation calculation unit, controls the extension and contraction of the left and right hydraulic cylinders, and combines the feedback information of the angle sensor to achieve closed-loop control of the extension and contraction of the hydraulic cylinders to accurately control the steering angle of the vibratory roller; the drive system includes an engine control module and a brake control module. After receiving the operation instructions from the operation calculation unit, the engine control module realizes automatic ignition and automatic shutdown through the matching electrical device, and continuously adjusts the engine throttle size according to the CAN bus information; the brake control module also receives the instructions of the operation calculation unit via the CAN bus, controls the parking solenoid valve through the electrical device, and sends an idle or shutdown instruction to the engine control module to realize the parking, parking and emergency stop control of the vibratory roller.

[0197] In an embodiment of the present invention, the decision-making device 300 is used to implement unmanned vibration roller trajectory planning, which may further include an information sensing unit and perform trajectory planning based on the sensing information of the information sensing unit. Figure 8 As shown, the information perception unit primarily collects environmental and self-information through sensors, including lidar, cameras, and millimeter-wave radar, which are used to identify and track static and dynamic obstacles and assess road conditions. Angle sensors are used to sense the torsion angle at the joint between the vehicle body and the roller's rigid body. The camera and lidar in the information perception unit are located atop the cab, while the millimeter-wave radars are located at the left and right front ends of the roller's steel frame. The camera provides real-time images for backstage monitoring. The lidar, located atop the vehicle body, offers a longer detection range and a wider field of view. The millimeter-wave radar, located at the front of the steel frame, detects vehicles or obstacles ahead, supporting forward collision warning and providing close-range protection. This layout creates a multi-layered perception system, ensuring the vibratory roller accurately perceives its surroundings in various driving scenarios.

[0198] In this embodiment of the present invention, the unmanned vibratory roller control device 100 obtains the angle information of the hinge point collected by the angle sensor via the CAN bus; obtains the coordinate position of the unmanned vibratory roller body measured by GPS via a serial interface; and obtains the body acceleration and angular velocity information measured by the inertial measurement unit (IMU) via the CAN bus. The specific operating process is as follows: upon receiving an operation command, the vibratory roller enters standby mode, the drive system remains in the parked state, and the steering system is controlled to perform a zeroing process to ensure that the angle sensor value is zero, that is, the front and rear of the vibratory roller hinge point remain in a straight line. Subsequently, during the operation of the unmanned vibratory roller, the received sensor information is used to adjust the drive system and steering system of the actuator in real time to ensure the trajectory tracking accuracy of the unmanned vibratory roller and the speed stability of the body.

[0199] In an embodiment of the present invention, the unmanned vibratory roller control system further includes a power supply device for providing power to various devices within the system. The power supply device is installed inside the cab and is equipped with a power monitoring device. While controlling and protecting power to all electrical devices, it also transmits power usage data and battery status to the unmanned vibratory roller control device 100 via the CAN bus.

[0200] In summary, the unmanned vibratory roller control system provided by the present invention integrates a decision-making device, a positioning device, an execution device, an unmanned vibratory roller control device, and a power supply device, providing a comprehensive supporting platform for unmanned vibratory roller control and possessing significant practicality. Through the control method and system of the present invention, the unmanned vibratory roller can achieve precise path tracking, obstacle avoidance, speed control, and compaction quality monitoring, significantly improving construction efficiency and quality while reducing labor costs and safety risks.

[0201] The specific working principle of the unmanned vibratory roller travel control system provided by the present invention can be referred to the description of the unmanned vibratory roller travel control device above, and will not be repeated here.

[0202] It will be understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present invention, and the present invention is not limited thereto. Those skilled in the art will appreciate that various modifications and improvements can be made without departing from the spirit and substance of the present invention, and such modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for controlling the operation of an unmanned vibratory roller, characterized in that: include: Obtaining positioning information of the unmanned vibratory roller, the positioning information including at least vehicle body coordinates, heading angle, and hinge point steering angle; Processing the positioning information through an unscented Kalman filter to obtain a real-time state estimation value of the unmanned vibratory roller; Obtaining a trajectory planning result of the unmanned vibratory roller, and performing error calculation based on the trajectory planning result and the real-time state estimation value to obtain an error result; Inputting the error result into the LQR controller to obtain the control parameters of the unmanned vibrating roller; The movement of the unmanned vibratory roller is controlled according to the unmanned vibratory roller control parameters.

2. The unmanned vibratory roller driving control method according to claim 1, characterized in that: Processing the positioning information through an unscented Kalman filter includes: Construct an unscented Kalman filter; Inputting the positioning information into the unscented Kalman filter for processing to obtain vehicle body coordinate estimation values; The vehicle body coordinate estimation value is converted into coordinate points according to the vehicle body size information to obtain a real-time position estimation value of the roller.

3. The unmanned vibratory roller driving control method according to claim 2, characterized in that: Construct an unscented Kalman filter, including: The motion of the unmanned vibratory roller is simplified into plane motion, and the state space equation of the vibratory roller and vehicle body is established based on the kinematic model; Determining a state transfer function based on the vibration roller-car body state space equation; The sigma point is predicted according to the state transfer function to obtain the Kalman gain and covariance matrix to complete the construction of the unscented Kalman filter.

4. The unmanned vibratory roller driving control method according to claim 3, characterized in that: The expression of the vibration roller-car body state space equation is: ， in, represents the vehicle body center of mass position velocity, represents the velocity component of the vehicle body in the X direction in the global coordinate system, represents the velocity component of the vehicle body in the Y direction in the global coordinate system, represents the angular velocity of the vehicle body’s center of mass in the global coordinate system, Indicates the angle difference between the body and the roller, It represents the distance from the center of mass of the roller to the hinge point, Indicates the distance from the center of mass of the vehicle body to the hinge point; The expression of the state transfer function is: ， in, , state variables , Indicates the moment, represents the discrete system sampling time; Indicates the coordinate of the vehicle body's center of mass on the X-axis in the global coordinate system; Indicates the Y-axis coordinate of the vehicle body's center of mass in the global coordinate system; Indicates the heading angle of the vehicle body's center of mass.

5. The unmanned vibratory roller driving control method according to claim 3, characterized in that: The sigma point is predicted according to the state transfer function to obtain the Kalman gain and covariance matrix, including: Initialization to generate sigma points and determine the state estimation vector and covariance matrix at the initialization moment; Performing a nonlinear transformation on the sigma point through the state transfer function to obtain a predicted state estimate of the sigma point; Calculate the mean and covariance of the predicted state based on the state estimate of the predicted sigma point; The Kalman gain is calculated based on the mean and covariance of the predicted state and the state estimate and covariance matrix are updated.

6. The unmanned vibratory roller driving control method according to any one of claims 1 to 5, characterized in that: The error result is input into the LQR controller to obtain the unmanned vibratory roller control parameters, including: Construct an LQR controller to determine the control quantity; The error result is controlled according to the control amount to obtain the control parameters of the unmanned vibrating roller.

7. The unmanned vibratory roller driving control method according to claim 6, characterized in that: Construct an LQR controller to determine the control quantity, including: Construct error-based state-space equations; Determine the cost function and output weight matrix; Designing an LQR controller based on the error-based state-space equation, the cost function, and the output weight matrix; A control variable is determined according to the LQR controller.

8. The unmanned vibratory roller driving control method according to claim 7, characterized in that: Determine the cost function and output weight matrix, including: Discretizing the error-based state space equation to obtain a linear time-varying prediction model of the vibratory roller; Determine the LQR cost calculation equation based on the linear time-varying prediction model of the vibrating roller; An output weight matrix is determined according to the LQR cost calculation equation.

9. An unmanned vibratory roller travel control device, used to implement the unmanned vibratory roller travel control method according to any one of claims 1 to 8, characterized in that: include: A positioning information acquisition module is used to obtain positioning information of the unmanned vibratory roller, wherein the positioning information includes at least vehicle body coordinates, heading angle, and hinge point steering angle; A Kalman filter module is used to process the positioning information through an unscented Kalman filter to obtain a real-time state estimation value of the unmanned vibratory roller; an error calculation module, configured to obtain a trajectory planning result of the unmanned vibratory roller, and perform error calculation based on the trajectory planning result and the real-time state estimation value to obtain an error result; An LQR control module is used to input the error result into an LQR controller to obtain control parameters of an unmanned vibratory roller; The driving control module is used to control the driving of the unmanned vibratory roller according to the control parameters of the unmanned vibratory roller.

10. An unmanned vibrating roller driving control system, characterized in that: include: A positioning device, a decision-making device, an execution device and the unmanned vibrating roller travel control device according to claim 9, wherein the positioning device, the decision-making device and the execution device are all communicatively connected to the unmanned vibrating roller travel control device; The positioning device is used to collect positioning information of the unmanned vibrating roller; The decision-making device is used to plan the trajectory of the unmanned vibratory roller and obtain the trajectory planning result; The unmanned vibratory roller travel control device is used to process positioning information according to an unscented Kalman filter, perform error calculation based on trajectory planning results and real-time state estimation values, and input the error results into an LQR controller to obtain unmanned vibratory roller control parameters; The execution device is used to control the travel of the unmanned vibratory roller according to the unmanned vibratory roller control parameters.

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