An automatic masonry and plastering system and method based on multi-machine cooperation

Abstract

The application relates to the technical field of building robots, in particular to an automatic masonry and plastering system and method based on multi-machine cooperation, which comprises an initial calibration module, a path planning module and the like.The initial calibration module is used for constructing a unified world coordinate system, collecting a standard image of a reference calibration board, completing space calibration to obtain initial space position data of equipment, and initializing operation control parameters; the path planning module is used for extracting a three-dimensional space coordinate range of a masonry operation area based on the initial space position data and an imported masonry scene digital twin model. The application generates a motion trajectory which avoids motion interference and is time-synchronized by uniformly planning a cooperative motion path of a masonry robot and an automatic plastering machine and combining an equipment motion speed threshold. The cooperative operation mode eliminates waiting time between processes, improves the masonry quantity within a unit time, does not require complex independent obstacle avoidance planning, avoids motion conflicts between equipment, and reduces system integration difficulty and cost.

Description

Technical Field

[0001] This invention relates to the field of construction robot technology, specifically to an automated masonry and plastering system and method based on multi-machine collaboration. Background Technology

[0002] Automated masonry technology is one of the key directions in the development of intelligent construction. Currently, various types of masonry robots have emerged on the market, capable of replacing manual labor in picking up and placing masonry blocks, thus improving the automation level of masonry operations to some extent. However, existing masonry robots typically have limited functionality, only responsible for the masonry action. Another crucial step in the masonry process—grouting—is still mostly done manually or by independent, non-cooperative automated equipment. This work mode leads to several prominent problems: First, after the masonry robot places the block, it needs to wait for the grouting work to be completed before it can lay the next block. The process is not well connected, the overall efficiency is low, and it is difficult to give full play to the collaborative efficiency of automated equipment. Second, manual grouting is difficult to ensure that the mortar thickness is uniform and the mortar is fully laid, which directly affects the overall strength of the wall and the quality of masonry. Although independent automated grouting equipment can partially replace manual labor, it is difficult to dynamically adjust the grouting parameters according to the masonry progress and the block posture due to the lack of real-time coordination with the masonry robot, and the quality control capability is limited. Furthermore, when independent plastering equipment shares the working space with masonry robots, there is a risk of motion interference, requiring complex obstacle avoidance planning and space management, which increases the difficulty and cost of system integration. Therefore, how to achieve efficient and high-quality collaboration between masonry and plastering processes without significantly increasing the complexity of individual machines has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0003] To achieve the above objectives, the present invention provides the following technical solution: An automated masonry and plastering system based on multi-machine collaboration includes: The initial calibration module is used to construct a unified world coordinate system, acquire standard images of the reference calibration board, complete spatial calibration to obtain the initial spatial position data of the equipment, and initialize the operation control parameters. The path planning module is used to extract the three-dimensional spatial coordinate range of the masonry operation area based on the initial spatial location data and the imported digital twin model of the masonry scene, and generate the cooperative motion path of the automatic plastering machine and the masonry robot. The collaborative operation module is used to control the masonry robot to grab the blocks and move along the path according to the collaborative motion path, and simultaneously control the automatic grouting machine to apply grout to the surface of the blocks to be laid, and place the grouted blocks at the target position. The quality inspection module is used to collect images of the block surface after grouting and images of the work area after placement, and to perform quality inspection based on the images of the block surface after grouting and the work area after placement to obtain inspection data; The closed-loop adjustment module is used to compare the test data with the preset construction quality standards, and use the deviation value to correct the control parameters of subsequent operations in real time until all masonry operations are completed and a complete operation report is generated.

[0004] Preferably, a standard image of the reference calibration board is acquired to complete spatial calibration and obtain the initial spatial position data of the equipment, and the operation control parameters are initialized, including: Standard images of a reference calibration board are acquired by a vision sensor. Based on the standard images, the spatial attitude calibration of the masonry robot, automatic plastering machine and vision sensor is completed, and the initial spatial position data under a unified coordinate system is obtained. The integrated grout supply system is simultaneously monitored to obtain data on the mortar storage bin's inventory and the sealing status of the grout supply pipeline. If the existing data and sealing status data do not meet the preset construction conditions, an alarm will be triggered. When the existing data and sealing status data meet the preset construction conditions, initialize the operation control parameters; The operation control parameters include the baseline value of grout thickness, the preset value of masonry pressure, the initial value of mortar flow rate, and the threshold value of equipment movement speed.

[0005] Preferably, based on the initial spatial location data and the imported digital twin model of the masonry scene, the three-dimensional spatial coordinate range of the masonry operation area is extracted, and the cooperative motion path of the automatic plastering machine and the masonry robot is generated, including: The digital twin model of the imported masonry scene is analyzed and imported to obtain construction parameters by combining the initial spatial location data. The construction parameters include the starting coordinates of masonry, the type of masonry process, the coordinates of the benchmark control point, the block specification parameters, and the masonry quality standards. Based on the construction parameters, the three-dimensional spatial coordinate range of the masonry operation area is extracted, and the arrangement rules and masonry sequence of the blocks are analyzed. The path optimization algorithm is used to plan the brick picking path, transportation path and block placement path of the masonry robot. Based on the brick picking path, transport path, and block placement path, and combined with the equipment movement speed threshold in the operation control parameters, a collaborative movement path for the automatic plastering machine and the masonry robot is generated to avoid motion interference and synchronize time.

[0006] Preferably, controlling the masonry robot to grasp blocks and move along the path according to the cooperative motion path includes: Control the bricklaying robot to drive it to the preset brick-picking station along the brick-picking path in the cooperative motion path; Based on the extracted block specification parameters, the clamping claw opening and clamping force of the masonry robot's clamping mechanism are automatically calculated and adjusted. The block is gripped by adjusting the opening of the gripper jaws and the gripping force, and the current gripping force data is collected in real time by a pressure sensor. If the current clamping force data reaches the preset clamping force threshold, confirm that the clamping is stable and generate the next operation signal.

[0007] Preferably, the synchronously controlled automatic grouting machine performs grouting operations on the surfaces of the blocks to be laid, including: Upon receiving the next operation signal, the robot is controlled to carry the blocks along the transfer path, while simultaneously sending a collaborative operation instruction to the automatic plastering machine. Based on collaborative operation instructions, the position and posture of the robotic arm of the automatic plastering machine are adjusted in real time so that the plastering nozzle is aligned with the surface of the block to be laid in the transfer path. Initiate the mortar application operation, collect the actual mortar flow data in real time through the flow sensor, and dynamically adjust the output power of the mortar supply pump in combination with the initial value of mortar flow and the benchmark value of mortar thickness in the operation control parameters to complete the mortar application on the surface to be masonry.

[0008] Preferably, placing the plastered blocks at the target location includes: Control the masonry robot to carry the masonry blocks that have been coated with mortar and move them to the target masonry point along the block placement path in the cooperative motion path. The system uses a visual sensor to acquire local area images of the target masonry point in real time, and identifies and calculates the block posture fine-tuning amount based on the local area images. Adjust the spatial posture of the block according to the fine-tuning amount of the block posture to make its axis consistent with the construction baseline, place the block at the target position and apply the preset value of the masonry pressure in the operation control parameters. Maintain the pressure corresponding to the preset masonry pressure value for a preset duration, and confirm the fit status a second time through a visual sensor during the pressure maintenance period to complete a single block placement action.

[0009] Preferably, images of the block surface after grouting and images of the work area after placement are acquired. Quality inspection data is obtained based on these images, including: After the plastering operation is completed, images of the block surface after plastering are captured by a vision sensor; After the blocks are placed in the target position, images of the work area are captured by a vision sensor. Gray-scale analysis was performed on the surface image of the masonry block after mortar application to extract gray-scale feature quantities that reflect the distribution state of the mortar. The uniformity of grout thickness and mortar fullness are calculated based on grayscale feature values, defect location areas are identified, and the first detection dataset is generated. Based on the image of the work area after placement, the edge contour coordinate sequence of the placed blocks is extracted, and the axial offset, verticality deviation and gap width between adjacent blocks are calculated using the edge contour coordinate sequence. The axis offset, verticality deviation, and gap width are summarized to generate a second detection dataset. The first detection dataset and the second detection dataset are then merged and extracted to generate complete detection data.

[0010] Preferably, the test data is compared with preset construction quality standards, and the control parameters for subsequent operations are corrected in real time using the deviation value, including: The test data is compared with the preset construction quality standards, and the error values ​​that exceed the allowable deviation range are extracted. If there is an error value, the proportional-integral-derivative (PID) control algorithm is used to calculate the compensation amount of each control parameter in real time based on the error value. The compensation amount is used to correct the grouting flow rate, grouting nozzle tilt angle, masonry robot positioning compensation amount, and masonry pressure parameters in subsequent operations in real time, and to generate subsequent operation parameters; if there are no error values, the current operation control parameters are kept as subsequent operation parameters.

[0011] Preferably, a complete work report is generated until all masonry work is completed, including: The subsequent operation parameters are fed back to the control system, and various path data and action instructions are called in a loop to repeatedly execute the grabbing, grouting and placement operations until all the preset masonry operation tasks within the three-dimensional spatial coordinate range are completed. After all masonry work is completed, extract the test data and time node records accumulated throughout the entire cycle. Based on the accumulated inspection data and time node records, a complete work report is generated and stored in association with the digital twin model. The complete work report includes the number of masonry blocks, the single work station operation time, the cumulative operation time, the quality inspection pass rate, the defect location, and the defect handling measures.

[0012] An automated masonry and plastering method based on multi-machine collaboration, applicable to the aforementioned automated masonry and plastering system based on multi-machine collaboration, includes: Construct a unified world coordinate system, acquire standard images of the reference calibration board, complete spatial calibration to obtain the initial spatial position data of the equipment, and initialize the operation control parameters; Based on the initial spatial location data and the imported digital twin model of the masonry scene, the three-dimensional spatial coordinate range of the masonry operation area is extracted, and the cooperative motion path of the automatic plastering machine and the masonry robot is generated. The masonry robot is controlled by the cooperative motion path to grab the blocks and move along the path. Simultaneously, the automatic grouting machine is controlled to apply grout to the surface of the blocks to be laid and place the grouted blocks at the target position. Collect images of the block surface after grouting and images of the work area after placement, and perform quality inspection based on the images of the block surface after grouting and the work area after placement to obtain inspection data; The test data is compared with the preset construction quality standards, and the control parameters of subsequent operations are corrected in real time using the deviation value until all masonry work is completed and a complete work report is generated.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention generates a motion trajectory that avoids motion interference and is synchronized in time by uniformly planning the collaborative motion path of the bricklaying robot and the automatic plastering machine, combined with the equipment motion speed threshold. On the one hand, after the bricklaying robot grabs the block, as it moves along the transfer path, the automatic plastering machine simultaneously completes the plastering operation on the surface of the block to be laid, realizing the parallelization of the brick-plastering-laying process. This collaborative operation method eliminates the waiting time between processes, greatly increases the number of bricks laid per unit time, and significantly improves the overall work efficiency. On the other hand, during the transfer process, the position of the robotic arm of the automatic plastering machine is adjusted in real time, so that the plastering nozzle is accurately aligned with the surface of the block to be laid. The two work in coordination within a shared workspace, without the need for complex independent obstacle avoidance planning, avoiding motion conflicts between equipment, and reducing the difficulty and cost of system integration. This invention constructs a closed-loop control mechanism of detection-comparison-adjustment. It uses a visual sensor to collect images of the block surface after grouting and the work area after masonry is completed in real time. Through algorithms such as grayscale analysis and edge contour extraction, it accurately detects quality indicators such as the uniformity of grout thickness, mortar fullness, block axis offset, verticality deviation, and gap width. The detection data is compared with preset quality standards, and subsequent operation parameters are corrected in real time. This effectively solves the problems of unstable quality of manual grouting and lack of coordinated control of independent equipment, ensuring uniform mortar thickness and full laying, and improving the overall strength of the wall and the accuracy of masonry. Attached Figure Description

[0014] Figure 1 This is a schematic flowchart of the overall method in one embodiment of the present invention; Figure 2 This is a schematic diagram of the overall system architecture in one embodiment of the present invention.

[0015] In the diagram: 1. Initial calibration module; 2. Path planning module; 3. Collaborative operation module; 4. Quality inspection module; 5. Closed-loop adjustment module. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1, please refer to Figure 1 This invention provides a technical solution: an automated masonry and plastering system based on multi-machine collaboration, comprising: Initial calibration module 1 is used to construct a unified world coordinate system, acquire standard images of the reference calibration board, complete spatial calibration to obtain the initial spatial position data of the equipment, and initialize the operation control parameters; Path planning module 2 is used to extract the three-dimensional spatial coordinate range of the masonry operation area based on the initial spatial location data and the imported digital twin model of the masonry scene, and generate the cooperative motion path of the automatic plastering machine and the masonry robot. The collaborative operation module 3 is used to control the masonry robot to grab the blocks and move along the path according to the collaborative motion path, and simultaneously control the automatic grouting machine to apply grout to the surface of the blocks to be laid, and place the grouted blocks at the target position. Quality inspection module 4 is used to collect images of the block surface after grouting and images of the work area after placement, and to perform quality inspection based on the images of the block surface after grouting and the work area after placement to obtain inspection data; The closed-loop adjustment module 5 is used to compare the detection data with the preset construction quality standards, and use the deviation value to correct the control parameters of subsequent operations in real time until all masonry operations are completed and a complete operation report is generated.

[0018] It should be noted that this system achieves automated control of the masonry process through multi-module data interaction. The initial calibration module 1 establishes the correspondence between physical equipment and the virtual coordinate system, and outputs the position and pose data of the equipment; the path planning module 2 generates motion commands based on the position and pose data and the virtual model; the collaborative operation module 3 executes the motion commands and controls the action of the end effector; the quality inspection module 4 collects image data of the operation results and converts them into quantitative indicators; the closed-loop adjustment module 5 compares the quantitative indicators with the standard values, generates parameter correction commands, and feeds them back to the preceding modules; the modules are connected through wired or wireless communication protocols to form a closed loop of data transmission and command control.

[0019] In an optional embodiment, a standard image of a reference calibration board is acquired to complete spatial calibration and obtain the initial spatial position data of the device, and the operation control parameters are initialized, including: Standard images of a reference calibration board are acquired by a vision sensor. Based on the standard images, the spatial attitude calibration of the masonry robot, automatic plastering machine and vision sensor is completed, and the initial spatial position data under a unified coordinate system is obtained. The integrated grout supply system is simultaneously monitored to obtain data on the mortar storage bin's inventory and the sealing status of the grout supply pipeline. If the existing data and sealing status data do not meet the preset construction conditions, an alarm will be triggered. When the existing data and sealing status data meet the preset construction conditions, initialize the operation control parameters; The operation control parameters include the baseline value of grout thickness, the preset value of masonry pressure, the initial value of mortar flow rate, and the threshold value of equipment movement speed.

[0020] It should be noted that spatial attitude calibration refers to using a vision sensor to capture images of a calibration board with specific characteristics, and then using algorithms to calculate the specific position (X, Y, Z coordinates) and attitude (rotation angle) of each device in a unified world coordinate system. Inventory data refers to the volume or weight percentage of remaining mortar in the mortar storage bin. Sealing status data refers to the stability of pressure in the mortar supply pipeline, used to determine if leakage exists. Operation control parameters are the baseline settings for system operation. For example, if the reference calibration board is a checkerboard pattern, the vision sensor calculates the center coordinates of the masonry robot's base as (0,0,0) and the mortar sprayer's nozzle coordinates as (2m, 0.5m, 1.2m). If the mortar inventory is detected to be below 20% or the pipeline pressure drops by more than 0.1MPa within one minute, the conditions are deemed unmet and an alarm is triggered. When the conditions are met, the initial mortar thickness baseline value is 10mm, the preset masonry pressure value is 500N, the initial mortar flow rate is 5L / min, and the equipment movement speed threshold is 0.8m / s.

[0021] In an optional embodiment, based on initial spatial location data and an imported digital twin model of the masonry scene, the three-dimensional spatial coordinate range of the masonry work area is extracted, and a cooperative motion path between the automatic plastering machine and the masonry robot is generated, including: The digital twin model of the imported masonry scene is analyzed and imported to obtain construction parameters by combining the initial spatial location data. The construction parameters include the starting coordinates of masonry, the type of masonry process, the coordinates of the benchmark control point, the block specification parameters, and the masonry quality standards. Based on the construction parameters, the three-dimensional spatial coordinate range of the masonry operation area is extracted, and the arrangement rules and masonry sequence of the blocks are analyzed. The path optimization algorithm is used to plan the brick picking path, transportation path and block placement path of the masonry robot. Based on the brick picking path, transport path, and block placement path, and combined with the equipment movement speed threshold in the operation control parameters, a collaborative movement path for the automatic plastering machine and the masonry robot is generated to avoid motion interference and synchronize time.

[0022] It should be noted that a digital twin model refers to a three-dimensional virtual model built in a computer that is consistent with the actual construction scene, including geometric and physical information such as walls, doors and windows, and the arrangement of blocks. Construction parameters are specific construction requirements extracted from the model; path optimization algorithms refer to algorithms such as A*, Dijkstra's algorithm, or fast expanding random tree algorithm, used to find the optimal path in an obstacle environment; cooperative motion path refers to the movement trajectory of two devices that do not collide in space and closely coordinate in time; for example, the digital twin model shows that a wall 5 meters long and 3 meters high needs to be built, and the coordinates of the 8 vertices of the wall are extracted as the work area; according to the block arrangement rules such as I-shaped masonry, the robot's brick picking path is planned, i.e., from the brick stacking area to the grouting area, and the transfer path is planned, i.e., from the grouting area to the masonry point and the placement path; combined with the speed threshold of 0.8 m / s, a cooperative path is generated: the robot picks up a brick at T=0s, arrives at the grouting area at T=3s, at which time the grouting machine needs to arrive on time at T=3s and start working, and the work ends at T=6s. The robot carries the block to the masonry point, arrives and places it at T=9s, the grouting machine withdraws at T=9s, and both return to their original positions at T=12s, with no collision and seamless action throughout the process.

[0023] In an optional embodiment, controlling the bricklaying robot to grasp blocks and move along the path according to a cooperative motion path includes: Control the bricklaying robot to drive it to the preset brick-picking station along the brick-picking path in the cooperative motion path; Based on the extracted block specification parameters, the clamping claw opening and clamping force of the masonry robot's clamping mechanism are automatically calculated and adjusted. The block is grasped by adjusting the opening of the gripper and the gripping force, and the current gripping force data is collected in real time by the pressure sensor. If the current clamping force data reaches the preset clamping force threshold, confirm that the clamping is stable and generate the next operation signal.

[0024] It should be noted that the block specifications refer to the length, width, height, and weight of the block; the gripper opening refers to the distance between the two grippers of the gripping mechanism; the gripping force refers to the force with which the grippers clamp the block; the pressure sensor is installed inside the gripping mechanism to measure the actual clamping force; and the preset gripping force threshold is the force standard for determining whether the block is securely gripped. For example, if the block specifications are 240mm × 115mm × 53mm, the system automatically calculates the gripper opening to be 250mm. When the bricklaying robot moves to the brick-picking station, the grippers close and clamp the block. The pressure sensor provides real-time feedback on the gripping force. If the preset gripping force threshold is 100N, when the sensor detects that the gripping force reaches 100N, the system confirms that the block has been securely gripped, generates a "successful gripping" signal, and notifies subsequent steps to begin. If the gripping force is only 50N, the system will instruct the grippers to tighten further.

[0025] In an optional embodiment, synchronously controlling an automatic grouting machine to apply grout to the mating surfaces of the blocks includes: Upon receiving the next operation signal, the robot is controlled to carry the blocks along the transfer path, while simultaneously sending a collaborative operation instruction to the automatic plastering machine. The position and posture of the robotic arm of the automatic plastering machine are adjusted in real time based on the collaborative operation instructions, so that the plastering nozzle is aligned with the surface of the block to be laid in the transfer path. Initiate the mortar application operation, collect the actual mortar flow data in real time through the flow sensor, and dynamically adjust the output power of the mortar supply pump in combination with the initial value of mortar flow and the benchmark value of mortar thickness in the operation control parameters to complete the mortar application on the surface to be masonry.

[0026] It should be noted that the robotic arm posture refers to the angles of each joint of the plastering machine's robotic arm and the spatial position of the end-cap nozzle; the flow sensor is installed on the mortar supply pipeline to measure the volume of mortar passing through per unit time; the output power of the mortar supply pump determines the mortar delivery speed and pressure; for example, after receiving a signal, the masonry robot begins to move along the transport path, while the plastering machine receives instructions and adjusts the robotic arm angle through visual tracking or path planning data to ensure that the nozzle is always perpendicularly aligned with the block surface. The initial mortar flow rate is set to 5L / min, corresponding to a plastering thickness of 10mm; during operation, the flow sensor detects an actual flow rate of 4.5L / min. Since the actual flow rate may change due to variations in mortar viscosity, the system automatically increases the output power of the mortar supply pump, such as from 50% to 60%, to restore the flow rate to 5L / min, ensuring that the plastering thickness is uniform and meets the baseline value.

[0027] In an optional embodiment, placing the plastered blocks at the target location includes: Control the masonry robot to carry the masonry blocks that have been coated with mortar and move them to the target masonry point along the block placement path in the cooperative motion path. The system uses a visual sensor to acquire local area images of the target masonry point in real time, and identifies and calculates the block posture fine-tuning amount based on the local area images. Adjust the spatial posture of the block according to the fine-tuning amount of the block posture to make its axis consistent with the construction baseline, place the block at the target position and apply the preset value of the masonry pressure in the operation control parameters. Maintain the pressure corresponding to the preset masonry pressure value for a preset duration, and confirm the fit status a second time through a visual sensor during the pressure maintenance period to complete a single block placement action.

[0028] It should be noted that the attitude fine-tuning amount refers to the deviation of the current attitude of the block from the ideal attitude in terms of angle and position, including the horizontal rotation angle and the vertical tilt angle; the construction baseline refers to the horizontal and vertical reference lines for masonry. The preset masonry pressure value refers to the vertical pressure applied during placement; for example, the masonry robot carries the block to above the target point, the vision sensor takes pictures of the already laid bottom block and mortar joints, and calculates through image recognition that the block to be placed needs to be rotated 1.5 degrees clockwise and shifted 3mm to the left to align, which is the attitude fine-tuning amount; the masonry robot adjusts the block's attitude accordingly, places it in place and applies 500N of pressure, maintains this pressure for 3 seconds, which is the preset duration, during which the vision sensor takes pictures again to confirm that the block has not shifted or tilted, after 3 seconds the robot withdraws, completing the placement.

[0029] In an optional embodiment, images of the block surface after grouting and images of the work area after placement are acquired. Quality inspection is performed based on these images to obtain inspection data, including: After the plastering operation is completed, images of the block surface after plastering are captured by a vision sensor; After the blocks are placed in the target position, images of the work area are captured by a vision sensor. Gray-scale analysis was performed on the surface image of the masonry block after mortar application to extract gray-scale feature quantities that reflect the distribution state of the mortar. The uniformity of grout thickness and mortar fullness are calculated based on grayscale feature values, defect location areas are identified, and the first detection dataset is generated. Based on the image of the work area after placement, the edge contour coordinate sequence of the placed blocks is extracted, and the axial offset, verticality deviation and gap width between adjacent blocks are calculated using the edge contour coordinate sequence. The axis offset, verticality deviation, and gap width are summarized to generate a second detection dataset. The first detection dataset and the second detection dataset are then merged and extracted to generate complete detection data.

[0030] It should be noted that grayscale analysis refers to using the brightness values ​​of image pixels to determine the mortar coverage. Mortar is usually darker or lighter than the base color of the block. Grayscale feature quantities refer to statistical indicators reflecting the distribution of mortar, such as average grayscale and grayscale variance. Mortar fullness refers to the degree of mortar filling in the mortar joints. Edge contour coordinate sequence refers to the pixel coordinates of the block corners extracted through image processing. For example, after mortar application, the camera takes a picture of the block surface. Grayscale analysis reveals that the grayscale value of a certain area is too high, indicating that the mortar is too thin. It is calculated that the uniformity of mortar thickness in this area is poor, and the mortar fullness is 80%. This area is identified as a defect area, generating the first detection dataset. After the block is placed, the camera takes a picture of the work area, extracts the edge contour coordinates of the block, calculates that the block axis is offset from the baseline by 5mm, the verticality deviation is 2mm, and the gap width with adjacent blocks is 12mm, generating the second detection dataset. The system merges the two sets of data to obtain the complete quality inspection data of the block.

[0031] In an optional embodiment, the detection data is compared with a preset construction quality standard, and the control parameters for subsequent operations are corrected in real time using the deviation value, including: The test data is compared with the preset construction quality standards, and the error values ​​that exceed the allowable deviation range are extracted. If there is an error value, the proportional-integral-derivative (PID) control algorithm is used to calculate the compensation amount of each control parameter in real time based on the error value. The compensation amount is used to correct the grouting flow rate, grouting nozzle tilt angle, masonry robot positioning compensation amount, and masonry pressure parameters in subsequent operations in real time, and to generate subsequent operation parameters; if there are no error values, the current operation control parameters are kept as subsequent operation parameters.

[0032] It should be noted that the construction quality standard refers to the masonry acceptance standard stipulated by the state or project, such as the allowable value of axial offset being ±3mm. The proportional-integral-derivative (PID) control algorithm is a control algorithm that calculates the control quantity based on the proportional, integral, and derivative terms of the error. The compensation quantity refers to the adjustment of the control parameters to eliminate the error. For example, if the system detects an axial offset of 5mm, exceeding the ±3mm standard, the error value is +2mm. The PID algorithm calculates a positioning compensation of -2.5mm based on this error, i.e., a leftward correction, and simultaneously calculates that the grouting nozzle tilt angle needs to be adjusted by 0.5 degrees based on the verticality deviation. The system uses these compensation quantities to correct subsequent operation parameters: setting the robot positioning compensation to -2.5mm and adjusting the nozzle tilt angle by 0.5 degrees. If the detection data are all within the allowable range, the system maintains the current parameters and continues to execute the operation.

[0033] In an optional embodiment, generating a complete work report until all masonry work is completed includes: The subsequent operation parameters are fed back to the control system, and various path data and action instructions are called in a loop to repeatedly execute the grabbing, grouting and placement operations until all the preset masonry operation tasks within the three-dimensional spatial coordinate range are completed. After all masonry work is completed, extract the test data and time node records accumulated throughout the entire cycle. Based on the accumulated inspection data and time node records, a complete work report is generated and stored in association with the digital twin model. The complete work report includes the number of masonry blocks, the single work station operation time, the cumulative operation time, the quality inspection pass rate, the defect location, and the defect handling measures.

[0034] It should be noted that time node records refer to the timestamps of the start and end of each work action; associated storage refers to mapping the work report to specific components in the digital twin model, allowing users to view the construction record of each brick on the model. For example, the system feeds back the corrected parameters to the control system, which then controls the robot and plastering machine to continue working on the next brick, repeating this cycle until the preset task of laying 1000 bricks is completed. After the task is completed, the system extracts all the detection data from these 1000 cycles, such as the offset, fullness, and time records for each brick, with the time record showing that each brick took 30 seconds to lay; it generates a work report showing that a total of 1000 bricks were laid, with a pass rate of 99%, a total time of 500 minutes, and lists the specific defect locations, such as the verticality exceeding the standard of brick number 58 and the corresponding remedial measures; this report is bound to the wall component ID in the digital twin model, and users can view the report by clicking on the wall in the model.

[0035] Example 2, please refer to Figure 2 This invention provides a technical solution: an automated masonry and plastering method based on multi-machine collaboration, applicable to the aforementioned automated masonry and plastering system based on multi-machine collaboration, comprising: S1. Construct a unified world coordinate system, acquire standard images of the reference calibration board, complete spatial calibration to obtain the initial spatial position data of the equipment, and initialize the operation control parameters; S2. Based on the initial spatial location data and the imported digital twin model of the masonry scene, extract the three-dimensional spatial coordinate range of the masonry operation area and generate the cooperative motion path of the automatic plastering machine and the masonry robot. S3. Control the masonry robot to grab the block and move along the path according to the cooperative motion path, and simultaneously control the automatic grouting machine to apply grout to the surface of the block to be laid, and place the grouted block at the target position. S4. Collect images of the block surface after grouting and images of the work area after placement, and perform quality inspection based on the images of the block surface after grouting and the work area after placement to obtain inspection data; S5. Compare the test data with the preset construction quality standards, and use the deviation value to correct the control parameters of subsequent operations in real time until all masonry operations are completed and a complete operation report is generated.

[0036] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. An automated masonry and plastering system based on multi-machine collaboration, characterized in that, include: The initial calibration module is used to construct a unified world coordinate system, acquire standard images of the reference calibration board, complete spatial calibration to obtain the initial spatial position data of the equipment, and initialize the operation control parameters. The path planning module is used to extract the three-dimensional spatial coordinate range of the masonry operation area based on the initial spatial location data and the imported digital twin model of the masonry scene, and generate the cooperative motion path of the automatic plastering machine and the masonry robot. The collaborative operation module is used to control the masonry robot to grab the blocks and move along the path according to the collaborative motion path, and simultaneously control the automatic grouting machine to apply grout to the surface of the blocks to be laid, and place the grouted blocks at the target position. The quality inspection module is used to collect images of the block surface after grouting and images of the work area after placement, and to perform quality inspection based on the images of the block surface after grouting and the work area after placement to obtain inspection data; The closed-loop adjustment module is used to compare the test data with the preset construction quality standards, and use the deviation value to correct the control parameters of subsequent operations in real time until all masonry operations are completed and a complete operation report is generated.

2. The automated masonry and plastering system based on multi-machine collaboration according to claim 1, characterized in that, Acquire standard images of the reference calibration board, complete spatial calibration to obtain the initial spatial position data of the equipment, and initialize the operation control parameters, including: Standard images of a reference calibration board are acquired by a vision sensor. Based on the standard images, the spatial attitude calibration of the masonry robot, automatic plastering machine and vision sensor is completed, and the initial spatial position data under a unified coordinate system is obtained. The integrated grout supply system is simultaneously monitored to obtain data on the mortar storage bin's inventory and the sealing status of the grout supply pipeline. If the existing data and sealing status data do not meet the preset construction conditions, an alarm will be triggered. When the existing data and sealing status data meet the preset construction conditions, initialize the operation control parameters; The operation control parameters include the baseline value of grout thickness, the preset value of masonry pressure, the initial value of mortar flow rate, and the threshold value of equipment movement speed.

3. The automated masonry and plastering system based on multi-machine collaboration according to claim 2, characterized in that, Based on the initial spatial location data and the imported digital twin model of the masonry scene, the three-dimensional spatial coordinate range of the masonry operation area is extracted, and the cooperative motion path of the automatic plastering machine and the masonry robot is generated, including: The digital twin model of the imported masonry scene is analyzed and imported to obtain construction parameters by combining the initial spatial location data. The construction parameters include the starting coordinates of masonry, the type of masonry process, the coordinates of the benchmark control point, the block specification parameters, and the masonry quality standards. Based on the construction parameters, the three-dimensional spatial coordinate range of the masonry operation area is extracted, and the arrangement rules and masonry sequence of the blocks are analyzed. The path optimization algorithm is used to plan the brick picking path, transportation path and block placement path of the masonry robot. Based on the brick picking path, transport path, and block placement path, and combined with the equipment movement speed threshold in the operation control parameters, a collaborative movement path for the automatic plastering machine and the masonry robot is generated to avoid motion interference and synchronize time.

4. The automated masonry and plastering system based on multi-machine collaboration according to claim 3, characterized in that, The bricklaying robot grasps blocks and moves along the path based on the cooperative motion path control, including: Control the bricklaying robot to drive it to the preset brick-picking station along the brick-picking path in the cooperative motion path; Based on the extracted block specification parameters, the clamping claw opening and clamping force of the masonry robot's clamping mechanism are automatically calculated and adjusted. The block is grasped by adjusting the opening of the gripper and the gripping force, and the current gripping force data is collected in real time by the pressure sensor. If the current clamping force data reaches the preset clamping force threshold, confirm that the clamping is stable and generate the next operation signal.

5. The automated masonry and plastering system based on multi-machine collaboration according to claim 4, characterized in that, The automatic grouting machine is synchronously controlled to perform grouting operations on the surfaces of the blocks to be laid, including: Upon receiving the next operation signal, the robot is controlled to carry the blocks along the transfer path, while simultaneously sending a collaborative operation instruction to the automatic plastering machine. The position and posture of the robotic arm of the automatic plastering machine are adjusted in real time based on the collaborative operation instructions, so that the plastering nozzle is aligned with the surface of the block to be laid in the transfer path. Initiate the mortar application operation, collect the actual mortar flow data in real time through the flow sensor, and dynamically adjust the output power of the mortar supply pump in combination with the initial value of mortar flow and the benchmark value of mortar thickness in the operation control parameters to complete the mortar application on the surface to be masonry.

6. The automated masonry and plastering system based on multi-machine collaboration according to claim 5, characterized in that, Place the plastered blocks in the target location, including: Control the masonry robot to carry the masonry blocks that have been coated with mortar and move them to the target masonry point along the block placement path in the cooperative motion path. The system uses a visual sensor to acquire local area images of the target masonry point in real time, and identifies and calculates the block posture fine-tuning amount based on the local area images. Adjust the spatial posture of the block according to the fine-tuning amount of the block posture to make its axis consistent with the construction baseline, place the block at the target position and apply the preset value of the masonry pressure in the operation control parameters. Maintain the pressure corresponding to the preset masonry pressure value for a preset duration, and confirm the fit status a second time through a visual sensor during the pressure maintenance period to complete a single block placement action.

7. The automated masonry and plastering system based on multi-machine collaboration according to claim 6, characterized in that, Images of the block surface after grouting and images of the work area after placement are acquired. Quality inspection data is obtained based on these images, including: After the plastering operation is completed, images of the block surface after plastering are captured by a vision sensor; After the blocks are placed in the target position, images of the work area are captured by a vision sensor. Gray-scale analysis was performed on the surface image of the masonry block after mortar application to extract gray-scale feature quantities that reflect the distribution state of the mortar. The uniformity of grout thickness and mortar fullness are calculated based on grayscale feature values, defect location areas are identified, and the first detection dataset is generated. Based on the image of the work area after placement, the edge contour coordinate sequence of the placed blocks is extracted, and the axial offset, verticality deviation and gap width between adjacent blocks are calculated using the edge contour coordinate sequence. The axis offset, verticality deviation, and gap width are summarized to generate a second detection dataset. The first detection dataset and the second detection dataset are then merged and extracted to generate complete detection data.

8. The automated masonry and plastering system based on multi-machine collaboration according to claim 7, characterized in that, The test data is compared with the preset construction quality standards, and the deviation value is used to correct the control parameters of subsequent operations in real time, including: The test data is compared with the preset construction quality standards, and the error values ​​that exceed the allowable deviation range are extracted. If there is an error value, the proportional-integral-derivative (PID) control algorithm is used to calculate the compensation amount of each control parameter in real time based on the error value. The compensation amount is used to correct the grouting flow rate, grouting nozzle tilt angle, masonry robot positioning compensation amount, and masonry pressure parameters in subsequent operations in real time, and to generate subsequent operation parameters; if there are no error values, the current operation control parameters are kept as subsequent operation parameters.

9. The automated masonry and plastering system based on multi-machine collaboration according to claim 8, characterized in that, Until all masonry work is completed and a full work report is generated, including: The subsequent operation parameters are fed back to the control system, and various path data and action instructions are called in a loop to repeatedly execute the grabbing, grouting and placement operations until all the preset masonry operation tasks within the three-dimensional spatial coordinate range are completed. After all masonry work is completed, extract the test data and time node records accumulated throughout the entire cycle. Based on the accumulated inspection data and time node records, a complete work report is generated and stored in association with the digital twin model. The complete work report includes the number of masonry blocks, the single work station operation time, the cumulative operation time, the quality inspection pass rate, the defect location, and the defect handling measures.

10. An automated masonry and plastering method based on multi-machine collaboration, applicable to the automated masonry and plastering system based on multi-machine collaboration as described in any one of claims 1-9, characterized in that, include: Construct a unified world coordinate system, acquire standard images of the reference calibration board, complete spatial calibration to obtain the initial spatial position data of the equipment, and initialize the operation control parameters; Based on the initial spatial location data and the imported digital twin model of the masonry scene, the three-dimensional spatial coordinate range of the masonry operation area is extracted, and the cooperative motion path of the automatic plastering machine and the masonry robot is generated. The masonry robot is controlled by the cooperative motion path to grab the blocks and move along the path. Simultaneously, the automatic grouting machine is controlled to apply grout to the surface of the blocks to be laid and place the grouted blocks at the target position. Collect images of the block surface after grouting and images of the work area after placement, and perform quality inspection based on the images of the block surface after grouting and the work area after placement to obtain inspection data; The test data is compared with the preset construction quality standards, and the control parameters of subsequent operations are corrected in real time using the deviation value until all masonry work is completed and a complete work report is generated.

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