A concrete compressive strength test method based on the full-process scheduling system

Abstract

The present invention is applicable to the technical field of sample testing, and provides a method for testing the compressive strength of concrete based on a full-process scheduling system, the method comprising the following steps: monitoring the status of all resource equipment through the full-process scheduling system; assigning priorities to the samples to be tested through the full-process scheduling system; the AGV trolley drives into a maintenance room, takes the samples to be tested according to the priorities of the samples, and transfers them to a mechanical arm; the samples on the AGV trolley are grabbed by a mechanical arm based on computer vision technology and transferred to a press for a compressive strength test; the AGV trolley is controlled to drive to a sample retention area, and the tested samples are stored. The present invention utilizes a full-process scheduling system to perform unified scheduling management on all resource equipment in the test process, thereby ensuring that each device is efficiently and reasonably utilized, and greatly improving the overall efficiency of the test.

Description

Technical Field

[0001] The invention relates to the technical field of sample testing, and in particular to a concrete compressive strength testing method based on a full-process scheduling system. Background Art

[0002] In the prior art, during the traditional compressive strength test of concrete test blocks, AGV carts are used for unmanned material delivery and robotic arms are used for loading and unloading to improve production efficiency.

[0003] For the test block delivery process, the solution provided by the existing technology is to adopt a pre-set process, that is, the AGV car has a set work process, moves to a certain point at a certain time to complete a task, and then moves to the set point in the process at the next time to continue the next task. Such a production process will result in the car not delivering materials in time after the material is consumed, or the car moves to the delivery point and leaves without delivering materials, thereby affecting production efficiency.

[0004] As for the loading process of the test, the interaction between the robot arm and the press is to move to the set point at a certain moment. Often the press is waiting for the robot arm to load the material, and there is also an efficiency problem. Therefore, a concrete compressive strength test method based on the full-process scheduling system is proposed to solve the above problems. Summary of the invention

[0005] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a concrete compressive strength test method based on a full-process scheduling system to solve the problems existing in the above-mentioned background technology.

[0006] The present invention is implemented as follows: a concrete compressive strength test method based on a full-process scheduling system, the method comprising the following steps:

[0007] The status of all resource equipment, including AGV, robotic arms and presses, is monitored through the full-process scheduling system;

[0008] When the samples in the curing room need to be tested, the whole process scheduling system will assign priorities to the samples to be tested. The priorities are assigned according to the delivery time and processing complexity of the tests.

[0009] The AGV is called up through the whole process dispatching system, so that the AGV drives to the maintenance room, takes the samples to be tested according to the priority of the samples and transfers them to the robotic arm;

[0010] The samples on the AGV are grabbed by a robot arm based on computer vision technology and transferred to the press for compressive strength testing. The test information is uploaded to the full-process scheduling system. The robot arm is also used to transfer the samples after the test to the AGV again.

[0011] The whole process dispatching system controls the AGV to drive to the sample retention location and stores the tested samples.

[0012] As a further solution of the present invention: the step of monitoring the status of all resource devices through the full-process scheduling system specifically includes:

[0013] Obtaining task queue information of resource equipment through the full-process scheduling system, wherein the task queue information is the status of tasks to be processed of each resource equipment;

[0014] When the resource device finishes processing the tasks in the task queue, it generates idle queue information and sends it to the full-process scheduling system to update the task queue information.

[0015] As a further solution of the present invention: the step of the AGV driving into the curing room and taking the samples to be tested according to the priority of the samples specifically includes:

[0016] The location information of the sample to be sampled with the highest priority is sent to the AGV car through the full-process scheduling system, and the location information of the sample is obtained by monitoring the status of the sample through the full-process scheduling system;

[0017] The AGV performs mobile sampling based on the location information and uses visual SLAM technology to capture the environment image in real time and build a three-dimensional map of the environment, allowing the AGV to update the map while moving;

[0018] When the AGV is in an unknown environment, it locates its own position in real time and plans the optimal path based on the real-time updated three-dimensional map of the environment.

[0019] As a further solution of the present invention: during the mobile sampling process of the AGV according to the position information, when the AGV is in an unknown environment, it locates its own position in real time and plans the optimal path according to the three-dimensional map of the environment updated in real time.

[0020] As a further solution of the present invention: the step of grabbing the sample on the AGV trolley by a robot arm based on computer vision technology specifically includes:

[0021] Use computer vision technology to locate samples on the AGV;

[0022] The pose of the sample is estimated based on 2D to 3D mapping, rotation estimation, and pose calculation technology to determine the direction and position of the sample to be taken in space;

[0023] Use computer vision technology to grab and identify samples based on their appearance;

[0024] The motion path is planned based on the position of the robot hand and the position of the sample, and the sample is grasped according to the motion path planning.

[0025] As a further solution of the present invention: the method further comprises:

[0026] When the sample test is completed, the test area on the press is detected and identified by the robotic arm, where the test area is the area on the press where the sample is tested;

[0027] When there is sample residue in the test area, the test area is cleaned by a robotic arm.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] ① The whole process of automated testing of concrete test blocks has been realized, reducing the waste of human resources and human errors in the testing process;

[0030] ② The full-process scheduling system can handle and manage test operations and abnormal situations in a timely manner under the global environment, improving the success rate and efficiency of the test;

[0031] ③ Use the full-process scheduling system to uniformly schedule and manage all resource equipment during the test, thereby ensuring that each device is used efficiently and reasonably, greatly improving the overall efficiency of the test. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The figure is a flow chart of a concrete compressive strength test method based on a full-process scheduling system.

[0033] Figure 2 It is a schematic diagram of the equipment corresponding to a concrete compressive strength test method based on the full-process scheduling system. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0035] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0036] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a concrete compressive strength test method based on a full-process scheduling system, and the method includes the following steps:

[0037] The status of all resource equipment, including AGV, robotic arms and presses, is monitored through the full-process scheduling system;

[0038] When the samples in the curing room need to be tested, the whole process scheduling system will assign priorities to the samples to be tested. The priorities are assigned according to the delivery time and processing complexity of the tests.

[0039] The AGV is called up through the whole process dispatching system, so that the AGV drives to the maintenance room, takes the samples to be tested according to the priority of the samples and transfers them to the robotic arm;

[0040] The samples on the AGV are grabbed by a robot arm based on computer vision technology and transferred to the press for compressive strength testing. The test information is uploaded to the full-process scheduling system. The robot arm is also used to transfer the samples after the test to the AGV again.

[0041] The full-process scheduling system controls the AGV to travel to the sample retention area and store the tested samples.

[0042] It should be noted that the scheduling strategy of the full-process scheduling system is divided into three parts: task priority allocation: each test block sample is assigned a priority according to its test urgency (such as delivery time). High-priority tasks will be scheduled first; resource allocation: the system dynamically allocates tasks to AGV carts, robotic arms and presses based on the current status of resources (idle or busy) and the priority of tasks; task queue management: each resource (AGV, robotic arm, press) has a queue of tasks to be processed. When a resource becomes idle, the system will select the highest priority task from the corresponding queue for processing.

[0043] In the embodiment of the present invention, the full-process scheduling system includes the following modules:

[0044] Task receiving and analysis module: When a new sample block needs to be tested, the system will assign it a priority based on its delivery time and processing complexity.

[0045] Resource status monitoring module: The full-process scheduling system monitors the status of all resources (AGV, robotic arms, presses) in real time and makes adjustments as needed. For example, when the robotic arm cannot accurately grasp the sample, the system can solve the problem by recalculating the path or adjusting the robotic arm's posture. After the test is completed, the robotic arm automatically identifies and handles the residue to keep the test area clean.

[0046] Dynamic scheduling decision module: When the samples in the curing room are ready to be moved, the system selects an idle AGV and dispatches it to the curing room to pick up the samples. The AGV moves the samples to the robotic arm. If the robotic arm is busy, the samples will be placed in the robotic arm's queue for processing. The robotic arm puts the samples into the press. If the press is busy, the samples will enter the press's queue for processing.

[0047] Feedback module: Whenever an AGV, robotic arm or press completes a task, it updates its status to “idle” and notifies the full-process scheduling system.

[0048] Iteration and Optimization Module: The full-process scheduling system continuously iterates scheduling decisions based on real-time feedback, prioritizes high-priority tasks, and maintains efficient utilization of all resources as much as possible.

[0049] The present invention realizes the automated test of the entire process of concrete test blocks, reduces the waste of human resources and human errors in the test process; the full-process scheduling system can timely handle and manage the test operation and abnormal situations in a global environment, thereby improving the success rate and efficiency of the test; the full-process scheduling system is used to uniformly schedule and manage all resource equipment in the test process, thereby ensuring that each device is efficiently and reasonably used, greatly improving the overall efficiency of the test.

[0050] In a preferred embodiment of the invention, the process of transferring samples by the AGV is described as follows:

[0051] Visual SLAM technology: Through one or more cameras (usually depth cameras or stereo cameras) installed on the AGV, the AGV can capture environmental images in real time during movement. Using these images, the visual SLAM system can not only build a three-dimensional map of the environment in real time, but also accurately locate the AGV itself on this basis.

[0052] Autonomous positioning and navigation: Visual SLAM enables the AGV to collect visual information of the surrounding environment (usually using a camera) in an unknown environment, locate itself in real time, and dynamically plan the optimal path to achieve accurate navigation to the target point.

[0053] Environmental mapping: By continuously capturing feature points in the environment (such as corners, ground markings, surrounding samples, etc.), the AGV can build a three-dimensional map of the environment. In this process, the car can update the map while moving, thereby adapting to changes in the environment.

[0054] Dynamic obstacle avoidance: During the movement process, visual SLAM technology can help AGV cars identify and avoid dynamic obstacles, such as walking people and moving vehicles, to ensure driving safety.

[0055] The visual SLAM AGV car greatly broadens the application scenarios of automatic guided vehicles by providing highly autonomous positioning and navigation capabilities, which not only improves work efficiency but also reduces dependence on environmental presets.

[0056] In the embodiment of the present invention, the visual SLAM technology is a technical solution based on the combination of a binocular camera and an IMU (inertial measurement unit), and its specific implementation method is as follows:

[0057] Get the depth information of the binocular camera

[0058] Binocular stereo vision is a technology that simulates the human eye to obtain depth information. By comparing two images of the same scene taken from different perspectives, the depth information of the samples in the scene can be calculated. The following is the process and related formulas for obtaining depth information with a binocular camera:

[0059] 1. Image acquisition

[0060] Two cameras (left camera and right camera) are used to capture the same scene at the same time, resulting in two images: left image (I_L) and right image (I_R).

[0061] 2. Camera Calibration

[0062] Before calculating the disparity, the camera needs to be calibrated to obtain the camera's intrinsic parameters (focal length, principal point coordinates, etc.) and extrinsic parameters (rotation matrix and translation vector). The calibration process is usually performed using a calibration board such as a chessboard.

[0063] The camera intrinsic parameter matrix P (3x4) and extrinsic parameter matrix R and translation vector t can be expressed as:

[0064] M=K*[R|t]

[0065] Among them, M is the homography matrix (HomographyMatrix), K is the intrinsic parameter matrix (3x3), R is the rotation matrix (3x3), and t is the translation vector (3x1).

[0066] 3. Parallax calculation:

[0067] For a pixel (x_L, y_L) in the left image, the corresponding point in the right image is (x_R, y_R). The horizontal distance difference between these two points is called disparity (d):

[0068] d = x R -x L

[0069] The larger the parallax, the closer the sample is; the smaller the parallax, the farther the sample is.

[0070] 4. Depth calculation:

[0071] Depth (Z) can be calculated by parallax and the camera's baseline (i.e. the distance between the two cameras) and the camera's focal length (f). The formula for depth calculation is as follows:

[0072] Z=(f*B) / d

[0073] Where B is the baseline length of the camera and f is the focal length of the camera. This formula is based on the principle of geometric triangulation.

[0074] 5. Depth map generation:

[0075] The above disparity and depth calculations are performed on each pixel in the left image, and finally a depth map is generated. Each pixel value in the map represents the depth information of the corresponding point in the scene.

[0076] IMU attitude information

[0077] An inertial measurement unit (IMU) is a sensor that can measure and report specific physical quantities of a sample, such as acceleration, angular velocity, and sometimes the orientation of a magnetic field around it. An IMU is typically used to determine the attitude of a sample, which is the orientation of the sample relative to a reference coordinate system. The following is the process and related formulas for obtaining attitude information using an IMU:

[0078] 1. Acceleration and angular velocity measurement:

[0079] The IMU contains an accelerometer and a gyroscope. The accelerometer measures the acceleration of the sample on three spatial axes (usually X, Y, and Z), while the gyroscope measures the angular velocity of the sample around these axes. The acceleration vector (a) can be expressed as [a = [a x ,a y ,a z ] T ], the angular velocity vector (ω) can be expressed as [ω=[ω x ,ω y ,ω z ] T .

[0080] 2. Posture points

[0081] The attitude information is usually obtained by integrating acceleration and angular velocity. First, we need to calculate the change in attitude (angular displacement) from the angular velocity. For the integration of the angular velocity, we can get the increment of the Euler angle Δθ = ∫ω, dt, where (dt) is a small time interval.

[0082] 3. Posture update:

[0083] With the increment of angular displacement, we can update the attitude of the sample. The attitude is usually represented by quaternion because it can avoid the gimbal lock problem. Assuming the initial quaternion is (q0), the updated quaternion (q1) can be calculated by the following formula: Where (×) represents quaternion multiplication.

[0084] 4. Quaternion integration:

[0085] In order to get a complete attitude update, we need to normalize the quaternion (q1): where (||·||) represents the norm (or modulus) of the quaternion.

[0086] 5. Position and velocity integration:

[0087] In addition to attitude, we can also estimate the position and velocity of the sample by integrating acceleration. The position (p) can be updated by the formula Δp = ∫(ag), dt, where (g) is the gravitational acceleration vector, and the velocity (v) can be updated by integrating the acceleration to obtain Δv = ∫adt.

[0088] 6. Noise and drift processing:

[0089] Since IMU measurements usually contain noise and long-term integration can cause drift, in practical applications, IMU data usually needs to be fused with data from other sensors through a filtering algorithm (such as a Kalman filter) to improve the accuracy of the estimate.

[0090] Fusion Algorithm

[0091] The data fusion of binocular camera and IMU is usually used to improve the accuracy and robustness of attitude estimation. The fusion process can be achieved by nonlinear optimization method (optimization in figure).

[0092] Nonlinear optimization methods and graphical optimization are used to deal with nonlinear factors in the system and estimate the state of the system by minimizing an objective function.

[0093] 1. Constructing the error function

[0094] Define an error function (E), which is typically the sum of the squares of the differences between all measured and predicted values.

[0095]

[0096] Among them (z i ) is the measured value, (f i(x)) is the predicted value calculated based on the state vector (x), and (ρ) is a penalty function, such as the Huber function.

[0097] 2. Minimize the error function

[0098] Minimize the error function by using an optimization algorithm (such as gradient descent, Levenberg-Marquardt algorithm, etc.)

[0099] 3. Consider constraints

[0100] During the optimization process, some prior knowledge or physical constraints may need to be considered, such as kinematic constraints, physical models, etc.

[0101] 4. Iterative solution

[0102] Through iterative solution, the state vector (x) is continuously updated until the error function converges to the minimum value.

[0103] Real-time and robustness

[0104] The binocular camera provides depth information of the scene, and the IMU provides posture information of the device. The combination of the two can achieve real-time positioning and map construction in a dynamic environment with a certain degree of robustness.

[0105] In a preferred embodiment of the present invention, the process of grabbing samples by a robot arm using computer vision technology is described as follows:

[0106] Robotic grasping is a complex process involving multiple key tasks, each of which is to ensure that the robot can grasp the target sample effectively and accurately. Specifically, there are four key tasks: target positioning, posture estimation, grasp detection and motion planning.

[0107] 1. Object Localization

[0108] Target localization is the initial stage of robotic grasping, which aims to identify and locate the target sample in the input visual data. This task usually includes two subtasks:

[0109] Object Detection: In this step, the algorithm identifies the samples of interest, i.e. the samples, in the image and generates a bounding box for each detected sample. This bounding box roughly indicates the location and size of the sample.

[0110] Object Segmentation: Based on object detection, object segmentation further refines the boundaries of the sample. It not only identifies the existence of the sample, but also accurately segments the outline of the sample. For the test block residue generated after the test, it is necessary to capture it with high precision. The present invention realizes the target recognition and segmentation of the sample based on the YOLO series model.

[0111] 2. Pose Estimation

[0112] The task of pose estimation is to determine the orientation and position of the sample in space. This is crucial for robotic grasping, as the robotic arm needs to know the exact orientation of the sample to grasp it correctly. Pose estimation usually involves 2D to 3D mapping, rotation estimation, and pose calculation. The present invention is based on a generative residual convolutional neural network (GR-ConvNet) that generates an anti-pattern grasp for each pixel in an n-channel input image. The specific operation is to obtain RGB and aligned depth images of the scene from an RGB-D camera. The image is preprocessed to match the input format of GR-ConvNet. The network generates image position, angle, and range images, which are then used to infer the anti-grasp pose.

[0113] 3. Grasping Detection

[0114] Grasp detection refers to the process of identifying the best grasping point or grasping posture of a sample. It is divided into grasping point identification, which determines which points on the sample are suitable for grasping. These points should be able to provide stable grasping and not easy to slip. Grasp posture generation, based on the shape and characteristics of the sample, generates possible grasping postures, including the grasping direction and finger configuration. It also includes grasping quality assessment, evaluates the stability and success rate of each grasping posture, and selects the optimal grasping solution. You can refer to the control module in the (GR-ConvNet) network. The task controller uses the grasping posture generated by the inference module to prepare and execute the planning of grasping and placing tasks. It uses a trajectory planner and a controller to communicate the required actions to the robot arm through an interface.

[0115] 4. Motion Planning

[0116] Motion planning is the process of designing a path for the robot to get from its current position to the target grasping point. This task is specifically path planning, which generates a path from the starting point to the grasping point while avoiding collisions and considering the joint limitations of the robot. Motion planning also includes two aspects: trajectory generation and execution strategy. Trajectory generation is to generate a smooth and continuous trajectory for the movement of the robot hand. The execution strategy is to determine the specific strategy for the robot to perform the grasping action, including the control of speed, acceleration and force. The motion planning of the robot can be implemented according to the RRT algorithm.

[0117] In summary, the present invention utilizes visual SLAM technology and computer vision technology to improve the positioning accuracy and grasping accuracy of the AGV and the robotic arm, and enhances the stability and reliability of the system.

[0118] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0119] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0120] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0121] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A concrete compressive strength test method based on a full-process scheduling system, characterized in that: The method comprises the following steps: The status of all resource equipment, including AGV, robotic arms and presses, is monitored through the full-process scheduling system; When samples in the curing room need to be tested, the whole process scheduling system assigns priority to the samples to be tested, and the priority is assigned according to the delivery time and processing complexity of the test; The AGV is called up through the whole process dispatching system, so that the AGV drives to the maintenance room, takes the samples to be tested according to the priority of the samples and transfers them to the robotic arm; The samples on the AGV are grabbed by a robot arm based on computer vision technology and transferred to the press for compressive strength testing. The test information is uploaded to the full-process scheduling system. The robot arm is also used to transfer the samples after the test to the AGV again. The whole process dispatching system controls the AGV to drive to the sample storage area and store the tested samples; The step of monitoring the status of all resource devices through the full-process scheduling system specifically includes: Obtaining task queue information of resource equipment through the full-process scheduling system, wherein the task queue information is the status of tasks to be processed of each resource equipment; When the resource device finishes processing the tasks in the task queue, it generates idle queue information and sends it to the full-process scheduling system to update the task queue information; The step of the AGV driving to the curing room and taking samples to be tested according to the priority of the samples specifically includes: The location information of the sample to be sampled with the highest priority is sent to the AGV car through the full-process scheduling system, and the location information of the sample is obtained by monitoring the status of the sample through the full-process scheduling system; The AGV performs mobile sampling based on the location information and uses visual SLAM technology to capture the environment image in real time and build a three-dimensional map of the environment, allowing the AGV to update the map while moving; When the AGV is in an unknown environment, it integrates binocular camera and IMU information to locate its own position in real time and plans the optimal path based on the real-time updated 3D map of the environment; The step of grabbing the sample on the AGV trolley by a robot arm based on computer vision technology specifically includes: Use computer vision technology to locate samples on the AGV; The pose of the sample is estimated based on the generated residual convolutional neural network to determine the direction and position of the sample to be taken in space; Use computer vision technology to grab and identify samples based on their appearance; The motion path is planned based on the position of the robot hand and the position of the sample, and the sample is grasped according to the motion path planning.

2. The concrete compressive strength test method based on the full-process scheduling system according to claim 1 is characterized in that: The method further comprises: When the sample test is completed, the test area on the press is detected and identified by the robotic arm, where the test area is the area on the press where the sample is tested; When there is sample residue in the test area, the test area is cleaned by a robotic arm.

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