Mobile crane control method and system based on space attitude perception

Abstract

The invention discloses a mobile crane control method and system based on space attitude perception, and relates to the technical field of cranes, and the method comprises the following steps: obtaining first crane data; according to the first crane data, calculating tower section center point coordinates and Euler angle attitudes based on a binocular vision three-dimensional reconstruction algorithm to obtain second crane data; inputting the second crane data into a self-adaptive fuzzy PID (Proportion Integration Differentiation) controller, and generating a hydraulic valve opening degree control instruction by combining pre-acquired environmental data and load weight dynamic setting control parameters; and controlling and driving a crane slewing mechanism according to the hydraulic valve opening control instruction. The method is used for solving the problems of insufficient target positioning precision of the mobile crane and poor stability of a control mechanism under complex working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of cranes, and more particularly, to a mobile crane control method and system based on spatial posture perception. Background Art

[0002] Under complex operating conditions, mobile cranes must achieve precise target positioning despite the influence of multiple dynamic environmental factors and diverse load characteristics. Existing technologies primarily rely on IMU sensors to measure angular velocity, laser rangefinders to obtain distance information, or traditional positioning algorithms based on simplified dynamic models, estimating the attitude and position of tower sections through analytical geometric relationships or first-order dynamic equations. However, these methods generally ignore the impact of nonlinear coupling effects and sensor noise on attitude solution accuracy, resulting in accumulated positioning errors and attitude estimation drift in actual operation, making it difficult to meet high-precision positioning requirements.

[0003] At the control level, existing solutions often employ fixed-parameter PID or simple fuzzy control structures, using empirical formulas for parameter tuning under linearized conditions. While algorithms such as adaptive PID and fuzzy PID improve disturbance rejection to a certain extent, they fail to fully integrate multi-source environmental data and load variation information, and lack real-time compensation for the hysteresis and nonlinear characteristics of the hydraulic system. This results in significant control response lag and overshoot, making it difficult to maintain high stability and fast tracking performance under complex dynamic conditions.

[0004] The existing technical solutions have at least the following technical problems: Current technical solutions for mobile crane control mostly rely on a single sensor or simplified geometric model, lacking accurate three-dimensional spatial posture perception; controllers mostly use fixed-parameter PID or linear compensation, and are unable to perform online adaptive adjustments to environmental disturbances and load changes; at the same time, they ignore the fact that binocular vision reconstruction accuracy is affected by camera calibration errors and feature matching noise, as well as the lag and nonlinear characteristics of the hydraulic system, resulting in large target positioning errors, slow dynamic response, and weak anti-interference ability, making it difficult to meet the high-precision and high-stability control requirements under complex working conditions.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0006] To overcome the above-mentioned shortcomings of the prior art, embodiments of the present invention provide a mobile crane control method and system based on spatial posture perception. By using spatial posture perception based on binocular vision three-dimensional reconstruction and combining adaptive fuzzy PID with environmental and load data to dynamically adjust the hydraulic valve opening, the problems of insufficient target positioning accuracy and poor control mechanism stability of mobile cranes under complex working conditions are addressed.

[0007] To achieve the above object, the present invention provides the following technical solutions: A mobile crane control method based on spatial posture perception includes the following steps: acquiring first crane data; calculating the tower section center point coordinates and Euler angle posture based on the first crane data using a binocular vision 3D reconstruction algorithm to obtain second crane data; inputting the second crane data into an adaptive fuzzy PID controller, dynamically adjusting control parameters based on pre-acquired environmental data and load weight, and generating a hydraulic valve opening control instruction; and controlling and driving the crane slewing mechanism according to the hydraulic valve opening control instruction.

[0008] In a preferred embodiment, the first crane data includes crane tower corner image, tower three-axis angular velocity and distance from the tower to the target point; the second crane data includes position deviation and attitude angle from the center point to the target point.

[0009] In a preferred embodiment, the method for acquiring crane data is specifically: capturing tower section corner point images using a binocular camera; measuring the three-axis angular velocity of the tower section using an IMU sensor; and measuring the distance from the tower section to the target point using a laser rangefinder.

[0010] In a preferred embodiment, the second crane data is input into the adaptive fuzzy PID controller, and the control parameters are dynamically adjusted in combination with the pre-acquired environmental data and load weight to generate a hydraulic valve opening control instruction. Specifically, the second crane data, environmental data and load weight are combined to construct a four-dimensional input vector; the four-dimensional input vector is mapped to the fuzzy domain based on the Gaussian membership function, and the trigger strength of each rule is calculated by traversing the pre-acquired fuzzy rule library; the fuzzification is performed based on the center of gravity method to obtain the control parameters; the hydraulic valve opening is adjusted according to the control parameters to generate a hydraulic valve opening control instruction.

[0011] In a preferred embodiment, the binocular vision three-dimensional reconstruction algorithm is specifically as follows: calibrating the two cameras to obtain the camera parameters themselves, and constructing their respective projection matrices based on the camera parameters themselves; using the two cameras to shoot the same tower section corner, extracting the projection homogeneous coordinates of the corresponding spatial points on the two images, and based on the pinhole imaging principle, respectively obtaining the projection relationship of the two cameras on the same tower section corner; obtaining a group of projection linear equations based on the projection matrix, the projection homogeneous coordinates and the projection relationship; using the least squares method to solve the group of projection linear equations to obtain the optimal solution for the world coordinates of the spatial point; homogenizing the obtained world coordinates of the spatial point, extracting the first three dimensions, and reconstructing the three-dimensional spatial coordinates of the tower section corner.

[0012] A mobile crane control system based on spatial posture perception includes: an external data acquisition module for acquiring first crane data; a data reconstruction module for calculating the coordinates of the tower section center point and the Euler angle posture based on the first crane data using a binocular vision three-dimensional reconstruction algorithm to obtain second crane data; a control parameter setting module for inputting the second crane data into an adaptive fuzzy PID controller, dynamically setting the control parameters based on pre-acquired environmental data and load weight, and generating a hydraulic valve opening control instruction; and a control module for controlling the crane's slewing mechanism according to the hydraulic valve opening control instruction.

[0013] The technical effects and advantages of the mobile crane control method and system based on spatial posture perception of the present invention are as follows: This invention achieves high-precision intelligent control of the crane's slewing mechanism by integrating binocular vision 3D reconstruction, multi-sensor data fusion, and adaptive fuzzy PID control. Its core advantages lie in: using a binocular camera to reconstruct the 3D coordinates of tower section corners, obtaining real-time angular velocity and target distance through an IMU and laser rangefinder, and collaboratively calculating the tower section center position and Euler angle attitude, thereby improving spatial positioning accuracy; dynamic optimization control: constructing a four-dimensional input vector from position deviation, attitude deflection angle, ambient wind speed, and load weight. Through Gaussian membership function fuzzification and a rule base triggering mechanism, PID parameters are dynamically adjusted in real time to achieve dynamic optimization control of the mobile crane and significantly enhance the system's anti-interference capability; dynamically compensating for position errors through control equations, and generating hydraulic valve opening commands based on adaptive parameters to achieve precise and stable actuation of the crane's slewing mechanism. This significantly improves the positioning accuracy and stability of heavy object hoisting, reduces the intensity of manual intervention, adapts to complex working conditions, and ensures operational safety and efficiency. This method effectively addresses the problems of insufficient target positioning accuracy and poor control mechanism stability of mobile cranes under complex working conditions, achieving precise and stable control of the tower section hoisting process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic flow chart of a mobile crane control method based on spatial posture perception provided by an embodiment of the present invention.

[0015] Figure 2 A schematic structural diagram of a mobile crane control system based on spatial posture perception provided by an embodiment of the present invention.

[0016] Figure 3 Schematic diagram of the binocular vision measurement model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] Example 1, Figure 1 The present invention provides a mobile crane control method based on spatial posture perception, which includes the following steps: S1, obtain the first crane data; S2, based on the first crane data, solve the tower section center point coordinates and Euler angle posture based on the binocular vision 3D reconstruction algorithm to obtain the second crane data; S3, inputting the second crane data into the adaptive fuzzy PID controller, combining the pre-acquired environmental data and the load weight to dynamically adjust the control parameters and generate a hydraulic valve opening control instruction; S4, controls the crane slewing mechanism according to the hydraulic valve opening control instruction.

[0019] This embodiment achieves high-precision intelligent control of the crane's slewing mechanism by integrating binocular vision 3D reconstruction, multi-sensor data fusion, and adaptive fuzzy PID control. Its core advantages lie in: using a binocular camera to reconstruct the 3D coordinates of tower section corners, obtaining real-time angular velocity and target distance through an IMU and laser rangefinder, and collaboratively calculating the tower section center position and Euler angle attitude, thereby improving spatial positioning accuracy. Dynamic optimization control: Position deviation, attitude deflection angle, ambient wind speed, and load weight are constructed into a four-dimensional input vector. Through Gaussian membership function fuzzification and a rule-based triggering mechanism, PID parameters are dynamically adjusted in real time to achieve dynamic optimization control of the mobile crane, significantly enhancing the system's anti-interference capability. Position error is dynamically compensated through control equations, and hydraulic valve opening commands are generated based on adaptive parameters, achieving precise and stable actuation of the crane's slewing mechanism. This significantly improves the positioning accuracy and stability of heavy object hoisting, reduces the intensity of manual intervention, adapts to complex working conditions, and ensures operational safety and efficiency. This effectively addresses the problems of insufficient target positioning accuracy and poor control mechanism stability of mobile cranes under complex working conditions, achieving precise and stable control of the tower section hoisting process.

[0020] S1, obtaining first crane data.

[0021] In this embodiment, the first crane data includes an image of a crane tower section corner point, a three-axis angular velocity of the tower section, and a distance from the tower section to a target point.

[0022] In this embodiment, the method for obtaining the crane data is specifically as follows: Capture tower section corner images through binocular cameras; The three-axis angular velocity of the tower section is measured by the IMU sensor; The distance from the tower section to the target point is measured using a laser rangefinder.

[0023] S2, based on the first crane data, the tower section center point coordinates and Euler angle posture are calculated based on a binocular vision 3D reconstruction algorithm to obtain the second crane data.

[0024] In this embodiment, the second crane data includes a position deviation and an attitude angle from a center point to a target point.

[0025] In this embodiment, the binocular vision 3D reconstruction algorithm is specifically as follows: Calibrate the two cameras to obtain their own parameters, and construct their respective projection matrices based on the camera's own parameters; Use two cameras to shoot the same tower section corner, extract the homogeneous coordinates of the projection of the corresponding spatial point on the two images, and based on the pinhole imaging principle, obtain the projection relationship of the two cameras on the same tower section corner point; According to the projection matrix, projection homogeneous coordinates and projection relationship, the projection linear equation system is obtained; The least square method is used to solve the projected linear equations to obtain the optimal solution of the world coordinates of the spatial point; The obtained world coordinates of the spatial points are homogenized, the first three dimensions are extracted, and the three-dimensional spatial coordinates of the tower section corners are reconstructed.

[0026] Figure 3 Schematic diagram of the binocular vision measurement model provided by an embodiment of the present invention.

[0027] In the picture is the world coordinate system, is the camera I coordinate system, is the coordinate system of camera II, where the optical axis of camera I is Optical axis with Camera II Orthogonal, is the image plane I coordinate system, is the image plane II coordinate system, Image I coordinate system, is the image II coordinate system.

[0028] Rays can be obtained through camera I and camera II respectively and The spatial information of the ray and When solving the coordinates of point P, we first need to find the world coordinates of the spatial point P and the corresponding projection point. and The relationship between the image coordinates is:

[0029] Binocular vision 3D reconstruction formula, the specific calculation formula is:

[0030] Where, and The projection points and The image homogeneous coordinates of is the world homogeneous coordinate of point P; and are the projection matrices of the two cameras respectively; and are the focal lengths of camera I and camera II, respectively.

[0031] eliminate and You can get information about Binocular vision 3D reconstruction formula.

[0032] To solve the system of equations according to the least squares method, first rewrite the above equation in matrix form:

[0033] The above formula can be simplified to ,in is the world coordinate of the spatial point to be found, and is a known vector that can be obtained by camera calibration. The least squares method can be used to solve it:

[0034] According to the above formula, the spatial three-dimensional coordinates of each point in the photo can be obtained from the photos taken by the binocular vision camera and the internal and external parameters of the camera.

[0035] S3, inputting the second crane data into the adaptive fuzzy PID controller, combining the pre-acquired environmental data and the load weight to dynamically adjust the control parameters, and generating a hydraulic valve opening control instruction.

[0036] In this embodiment, the second crane data is input into the adaptive fuzzy PID controller, and the control parameters are dynamically adjusted based on the pre-acquired environmental data and load weight to generate the hydraulic valve opening control instruction, specifically: Combining the second crane data, environmental data, and load weight to construct a four-dimensional input vector; The four-dimensional input vector is mapped to the fuzzy domain based on the Gaussian membership function, and the triggering strength of each rule is calculated by traversing the pre-acquired fuzzy rule base; Defuzzification based on the center of gravity method to obtain control parameters; Adjust the hydraulic valve opening according to the control parameters and generate a hydraulic valve opening control instruction.

[0037] In this embodiment, the environmental data includes environmental wind speed.

[0038] In this embodiment, the Gaussian membership function is specifically calculated as follows:

[0039] Where, is the membership function of input vector dimension j under rule i, is a fuzzy set, is the preset cluster center, is the j-th dimension of the input vector, is the standard deviation of the Gaussian function obtained based on historical crane data.

[0040] In this embodiment, the trigger strength is calculated as follows:

[0041] Where, is the trigger strength, are the Gaussian membership functions of the four-dimensional input vectors.

[0042] In this embodiment, the specific formula for defuzzification based on the centroid method is:

[0043] Where, is the control parameter increment obtained by defuzzification, The control parameter adjustment amount defined for the i-th rule, is the total number of rules in the rule base.

[0044] In this embodiment, the control parameter is specifically formulated as follows:

[0045] Where, For the control parameters, is the control parameter gain reference value.

[0046] In this embodiment, the specific formula for adjusting the hydraulic valve opening according to the control parameters is:

[0047] Where, is the control equation, is the position deviation from the center point to the target point, is the historical accumulation of position deviation, is the position deviation change rate, is the preset differential gain coefficient.

[0048] S4, controlling and driving the crane slewing mechanism according to the hydraulic valve opening control instruction.

[0049] The binocular vision 3D reconstruction algorithm uses two calibrated cameras to simultaneously capture the same scene from different perspectives. By extracting the corresponding matching of feature points in the image, combining the camera's internal and external parameters (projection matrix) and the pinhole imaging model, it establishes a projection equation from pixel coordinates to spatial coordinates, and then uses the least squares method to solve the optimal 3D world coordinates of each feature point to achieve dense or sparse spatial reconstruction.

[0050] An inertial measurement unit (IMU) is a microelectronic device that integrates a three-axis accelerometer, a three-axis gyroscope, and sometimes a magnetometer. It can measure the linear acceleration and angular velocity of a vehicle along three orthogonal axes in real time. In attitude calculation, the IMU fuses acceleration and angular velocity data to provide the system with high-frequency attitude change information within a short timescale, compensating for delays in visual or other sensors.

[0051] Laser rangefinders measure the distance to a target by emitting pulsed or frequency-modulated continuous wave (FMCW) laser light and measuring its round-trip time or phase difference. They offer high accuracy (millimeter level), high frequency, and robustness to light interference. They are often used to measure the straight-line distance from a crane tower section to a target point in real time, providing reliable depth information for position deviation calculations.

[0052] The adaptive fuzzy PID controller combines the proportional-integral-differential architecture of traditional PID with the rule-based reasoning capabilities of fuzzy logic. It first fuzzifies input variables, such as the system error and its rate of change. Using a set of empirical or pre-set fuzzy rule bases, it calculates the trigger strength of each rule. Defuzzification then derives the dynamic adjustment of the PID gain, enabling online adaptive tuning of control parameters. This ensures excellent tracking performance and robustness in nonlinear, time-varying, and disturbed environments.

[0053] Example 2, Figure 2 The present invention provides an energy and power hierarchical probability balance adjustment system based on a distribution grid, comprising: An external data acquisition module, configured to acquire first crane data; A data reconstruction module is used to calculate the coordinates of the tower section center point and the Euler angle posture based on the first crane data using a binocular vision 3D reconstruction algorithm to obtain the second crane data; A control parameter setting module is used to input the second crane data into the adaptive fuzzy PID controller, dynamically adjust the control parameters based on the pre-acquired environmental data and load weight, and generate a hydraulic valve opening control instruction; The control module is used to control the crane slewing mechanism according to the hydraulic valve opening control instruction.

[0054] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0055] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0056] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0057] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0058] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0059] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A mobile crane control method based on spatial posture perception, characterized in that: The following steps are involved: Get the first crane data; According to the first crane data, the tower section center point coordinates and Euler angle posture are calculated based on the binocular vision 3D reconstruction algorithm to obtain the second crane data; The second crane data is input into the adaptive fuzzy PID controller, which dynamically adjusts the control parameters based on the pre-acquired environmental data and load weight to generate the hydraulic valve opening control command; The crane slewing mechanism is controlled and driven according to the hydraulic valve opening control instruction.

2. The mobile crane control method based on spatial posture perception according to claim 1 is characterized in that: The first crane data includes the crane tower section corner point image, the tower section three-axis angular velocity and the distance from the tower section to the target point; the second crane data includes the position deviation and attitude deviation angle from the center point to the target point.

3. The mobile crane control method based on spatial posture perception according to claim 2 is characterized in that: The method for obtaining the crane data is specifically as follows: Capture tower section corner images through binocular cameras; The three-axis angular velocity of the tower section is measured by the IMU sensor; The distance from the tower section to the target point is measured using a laser rangefinder.

4. The mobile crane control method based on spatial posture perception according to claim 3 is characterized in that: The second crane data is input into the adaptive fuzzy PID controller, and the control parameters are dynamically adjusted based on the pre-acquired environmental data and load weight to generate the hydraulic valve opening control instruction, specifically: Combining the second crane data, environmental data, and load weight to construct a four-dimensional input vector; The four-dimensional input vector is mapped to the fuzzy domain based on the Gaussian membership function, and the triggering strength of each rule is calculated by traversing the pre-acquired fuzzy rule base; Defuzzification based on the center of gravity method to obtain control parameters; Adjust the hydraulic valve opening according to the control parameters and generate a hydraulic valve opening control instruction.

5. The mobile crane control method based on spatial posture perception according to claim 4 is characterized in that: The binocular vision 3D reconstruction algorithm is specifically as follows: Calibrate the two cameras to obtain their own parameters, and construct their respective projection matrices based on the camera's own parameters; Use two cameras to shoot the same tower section corner, extract the homogeneous coordinates of the projection of the corresponding spatial point on the two images, and based on the pinhole imaging principle, obtain the projection relationship of the two cameras on the same tower section corner point; According to the projection matrix, projection homogeneous coordinates and projection relationship, the projection linear equation system is obtained; The least square method is used to solve the projected linear equations to obtain the optimal solution of the world coordinates of the spatial point; The obtained world coordinates of the spatial points are homogenized, the first three dimensions are extracted, and the three-dimensional spatial coordinates of the tower section corners are reconstructed.

6. The mobile crane control method based on spatial posture perception according to claim 5, characterized in that: The specific calculation formula of the trigger strength is: Where, is the trigger strength, are the Gaussian membership functions of the four-dimensional input vectors.

7. The mobile crane control method based on spatial posture perception according to claim 6, characterized in that: The specific formula for defuzzification based on the centroid method is: Where, is the control parameter increment obtained by defuzzification, The control parameter adjustment amount defined for the i-th rule, is the total number of rules in the rule base.

8. A system using the mobile crane control method based on spatial posture perception according to any one of claims 1 to 7, comprising: An external data acquisition module, configured to acquire first crane data; A data reconstruction module is used to calculate the coordinates of the tower section center point and the Euler angle posture based on the first crane data using a binocular vision 3D reconstruction algorithm to obtain the second crane data; A control parameter setting module is used to input the second crane data into the adaptive fuzzy PID controller, dynamically adjust the control parameters based on the pre-acquired environmental data and load weight, and generate a hydraulic valve opening control instruction; The control module is used to control the crane slewing mechanism according to the hydraulic valve opening control instruction.

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