Medium-thickness plate surface intelligent maintenance grinding system and method for multi-task efficient cooperative distribution

Abstract

The invention belongs to the technical field of medium-thickness plate defect grinding, and discloses a multi-task efficient cooperative distribution medium-thickness plate surface intelligent maintenance grinding system and method, and the system comprises a medium-thickness plate defect detection system, a task distribution system and a robot grinding system; the medium and thick plate defect detection system is used for acquiring position and depth information of defects; the task distribution system receives defect information in real time, and distributes tasks to the robot coping system in real time by utilizing a greedy algorithm based on consensus through steel plate working area division and constraint of robot collision prevention; and starting a steel plate positioning and grinding program by the robot grinding system according to the defect information, and rechecking a grinding area after grinding is completed. According to the intelligent maintenance grinding device for the medium-thickness plate surface, multi-task efficient cooperative distribution is achieved, online grinding of a medium-thickness plate finishing line robot is met, manual detection and grinding are replaced, and the automation level of a production line is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of defect repairing of medium and thick plates, and in particular to a system and method for intelligent repairing and grinding of medium and thick plate surfaces with efficient collaborative allocation of multiple tasks. Background Art

[0002] Traditional surface inspection and grinding of medium and thick plates rely on manual operation and lack effective collaborative division of labor. Workers need to independently complete multiple tasks, such as inspection, defect identification and grinding. Lack of coordination and clear division of labor leads to frequent task switching, increased labor intensity and fatigue, and reduced efficiency. Poor information transmission and inefficient collaboration can easily lead to missed defects or uneven grinding. Uneven task distribution affects work progress, thereby affecting overall production efficiency and surface quality stability. In addition, due to the harsh working environment, a large amount of dust, high temperature and noise will be generated during the grinding process. Workers are exposed to this environment for a long time and face greater health and safety risks.

[0003] In view of the above problems, the present invention provides a medium and thick plate surface intelligent inspection and grinding system and method with efficient collaborative allocation of multiple tasks. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a medium and thick plate surface intelligent inspection and grinding system and method with multi-task efficient collaborative allocation. Through intelligent scheduling and precise task allocation, the system optimizes the workflow, improves collaborative efficiency, and avoids uncertainty in manual operation. It reduces worker fatigue and operational errors, reduces health risks, and significantly improves production efficiency and product quality, ensuring that each steel plate meets the ideal quality standard before leaving the factory, and improving the overall efficiency of the production line.

[0005] The present invention discloses a medium and thick plate surface intelligent inspection and grinding system with multi-task efficient collaborative allocation, the system comprising: a medium and thick plate defect detection system, a task allocation system and a robot grinding system;

[0006] The medium and thick plate defect detection system is composed of multiple parallel cameras for collecting images and multiple line laser three-dimensional scanning components, which are used to scan the surface of the steel plate online, obtain defect location information and depth information, and transmit the obtained defect information to the task allocation system;

[0007] The task allocation system is used to receive the defect information of the steel plate surface in real time, divide the working area by the steel plate and prevent the robot from colliding, and use the consensus-based greedy algorithm to allocate the task to the robot grinding system in real time;

[0008] The robot grinding system is used to obtain the positioning coordinates of the steel plate through a laser displacement sensor, start the corresponding grinding process according to the defect information sent by the medium and thick plate defect detection system, and use a 3D structured light camera to re-inspect the grinding effect of the grinding area after the grinding is completed.

[0009] Preferably, the constraints to prevent robot collisions include:

[0010] Divide the steel plate into multiple grinding areas, including independent areas and consensus areas, wherein the independent area is an area that can be ground by only one robot, and the consensus area is an area that can be ground by multiple robots together;

[0011] Assign the acquired defect information to the grinding area at the location;

[0012] According to the collaborative robot's working range, set an independent task list and a consensus task list for the robot to prevent interference and collision when the robot is grinding steel plates;

[0013] Calculate the robot's motion envelope to prevent the motion envelopes of collaborative robots from overlapping.

[0014] Preferably, the process of allocating tasks to the robot grinding system in real time using a consensus-based greedy algorithm includes:

[0015] Each robot calculates its fitness to perform a certain task and determines the probability of task selection based on the fitness;

[0016] When entering a consensus area, the robot needs to ensure that only one robot is responsible for generating the task list for the current area;

[0017] Through the locking mechanism, the robot checks and updates the consensus task list to ensure that only one robot can create or update the task list of the current area;

[0018] In the consensus area, the robot selects tasks according to the greedy strategy and chooses the task closest to itself.

[0019] Preferably, obtaining the positioning coordinates of the steel plate by using a laser displacement sensor includes:

[0020] Use the laser displacement sensor to obtain the coordinates of the two ends of the long side of the steel plate by running the robot positioning command , and the coordinates of a point on the wide side ;

[0021] The relative coordinates of the steel plate are calculated using the three-point method, and the intersection coordinates are obtained Angle with horizontal axis Set the robot's steel plate workpiece coordinates.

[0022] Preferably, after the grinding is completed, the process of re-inspecting the grinding effect of the grinding area using a 3D structured light camera includes:

[0023] Use a 3D structured light camera to re-inspect the grinding area and obtain point cloud data after grinding;

[0024] According to the point cloud data, the depth after grinding, i.e. the re-inspection depth, is calculated;

[0025] Use the RANSAC algorithm (random sampling consensus algorithm) to perform plane fitting on the point cloud data to determine the plane reference of the steel plate And the polished plane reference , and then adjust the two planes to be parallel, and calculate the distance between the two planes , to ensure the error between the pre-grinding depth and the actual grinding depth.

[0026] The present invention also provides a method for intelligent surface maintenance and grinding of medium and thick plates with efficient collaborative allocation of multiple tasks, which is implemented by using any one of the systems described above, and comprises:

[0027] S1: The medium and thick plate defect detection system collects image defects through the camera, and the line laser 3D scanning component scans the steel plate online to provide defect depth information;

[0028] S2: After the steel plate passes through the photoelectric switch, the roller stops, the steel plate positioning of the robot grinding system is started, and the position coordinates of the steel plate are obtained;

[0029] S3: The task allocation system is used to obtain the defect depth and corresponding location information, and the work area is divided by steel plates and the robot collision prevention constraints are used to allocate tasks to the robot grinding system in real time using a consensus-based greedy algorithm;

[0030] S4: The robot grinding system starts the corresponding grinding program by receiving the defect task;

[0031] S5: Grinding chips are collected by chip collection equipment to prevent them from covering the grinding marks and polluting the production environment;

[0032] S6: Based on the grinding requirements, the grinding area is inspected using a 3D structured light camera to ensure that the grinding depth meets the preset requirements;

[0033] S7: Re-inspect the grinding area using a 3D structured light camera;

[0034] S8: If there are still defects after grinding, use a 3D structured light camera to re-check the depth of the defect in the grinding area, and perform the next grinding and re-inspection again until the defect grinding is completed and meets the preset requirements;

[0035] S9: According to the needs of the production line, multiple robots are collaboratively controlled to perform grinding operations to achieve efficient grinding of medium and thick plates.

[0036] Preferably, in S2, after the steel plate passes through the photoelectric switch, the roller is stopped, the steel plate positioning is started, and the method for obtaining the steel plate position coordinates includes:

[0037] Place the photoelectric switch under the roller. After the steel plate passes through the photoelectric switch, the feedback signal stops the roller. The robot receives the corresponding steel plate positioning when the roller starts.

[0038] The laser displacement sensor is used to run the robot positioning command to obtain the coordinates of the two ends of the long side of the steel plate. , and the coordinates of a point on the wide side ;

[0039] The three-point method is used to calculate the relative coordinates of the steel plate. The formula is:

[0040] Calculate the slope of the long side and the intercept :

[0041] ;

[0042] ;

[0043] Calculate the slope of the broadside and the intercept :

[0044] ;

[0045] ;

[0046] Calculate the coordinates of the intersection of the long side and the wide side :

[0047] ;

[0048] ;

[0049] Calculate the angle with the horizontal axis :

[0050] ;

[0051] By obtaining the intersection coordinates Angle with horizontal axis Set the robot's steel plate workpiece coordinates.

[0052] Preferably, in S3, dividing the working area by steel plates comprises:

[0053] According to the depth D of the defect, the length L and width W of the defect are expanded; the expansion formula is:

[0054] ;

[0055] ;

[0056] in, is the actual grinding length, is the actual grinding width;

[0057] The lower right corner reference point of the working area is determined by aligning the grinding position of the tool with the defect area; the coordinates of the reference point are calculated as:

[0058] ;

[0059] ;

[0060] in, Is the reference point coordinate, Is the reference point coordinate, The starting point for grinding coordinate, The starting point for grinding coordinate, is the total length of the grinding equipment;

[0061] Combining the length and width of the tool with the expanded defect area, calculate the final length and width of the working area:

[0062] ;

[0063] ;

[0064] in, is the final length of the working area, C W is the final width of the working area, is the total width of the grinding equipment;

[0065] Assume the rotation angle is , use the rotation matrix to calculate the coordinates of the four vertices of the work area after rotation:

[0066] ;

[0067] ;

[0068] in, are the vertex coordinates before rotation, are the coordinates of the vertex after rotation;

[0069] After rotation, take the minimum and maximum values ​​of the X and Y coordinates of the four vertices to determine the boundaries of the working area:

[0070] ;

[0071] in, is the minimum boundary of the working area, is the maximum boundary of the working area, is the minimum boundary of the working area, is the maximum boundary of the working area.

[0072] Preferably, in S3, the method of using a consensus-based greedy algorithm to allocate tasks to the robot grinding system in real time includes:

[0073] Each robot calculates its fitness to perform a certain task, and determines the probability of task selection based on the fitness; the probability of task selection is:

[0074] ;

[0075] in, is the task selection probability, is the sum of the fitness scores of robot i for all tasks, is the fitness or score of robot i in completing the task;

[0076] When entering a consensus area, the robot needs to ensure that only one robot is responsible for generating the task list for the current area;

[0077] Through the locking mechanism, the robot checks and updates the consensus task list to ensure that only one robot can create or update the task list of the current area;

[0078] In the consensus area, the robot selects tasks according to the greedy strategy and chooses the task closest to itself.

[0079] Preferably, in S6, the method of re-inspecting the grinding area using a 3D structured light camera includes:

[0080] Use a 3D structured light camera to re-inspect the grinding area and obtain point cloud data after grinding;

[0081] According to the point cloud data, calculate the depth after grinding, i.e. the re-inspection depth, and detect the depth and surface smoothness of the grinding area to ensure that the depth error does not exceed 0.05mm; the specific method is:

[0082] Use the RANSAC algorithm to perform plane fitting on the point cloud data to determine the plane reference of the steel plate And the polished plane reference , the formula is as follows:

[0083] Randomly select the minimum number of points from the point cloud data to fit the model. For plane fitting, select three points. , , Determine a plane;

[0084] Calculating the normal vector :

[0085] ;

[0086] After unfolding, the normal vector of the plane is :

[0087] ;

[0088] ;

[0089] ;

[0090] A known point Substitute into the plane equation and calculate the constant term in the plane equation :

[0091] ;

[0092] Calculate the number of interior points, substitute all points into the plane equation, and calculate the distance from the interior points to the plane;

[0093] If the distance from a point to the plane is less than the preset threshold , then the corresponding points are considered to be interior points;

[0094] The distance formula is as follows:

[0095] ;

[0096] in, is the distance from the point to the plane, is a point in the point cloud. Iterate repeatedly until the model with the most inliers is found or the upper limit of the number of iterations is reached. The model with the most inliers is the best fitting plane model.

[0097] By calculating the rotation matrix, the normal vector of the polishing plane Normal vector rotated to the base plane superior;

[0098] The rotation axis is the cross product of the two normal vectors:

[0099] ;

[0100] in, is the rotation axis. If the cross product result is a zero vector, the two normal vectors are already parallel and no rotation is required.

[0101] Calculate the angle θ between the two normal vectors:

[0102] ;

[0103] The rotation matrix is ​​calculated using the Rodrigues rotation formula based on the rotation axis and rotation angle. :

[0104] ;

[0105] in, is the identity matrix, is the antisymmetric matrix of the rotation axis and is defined as follows:

[0106] ;

[0107] in, The rotation axis The amount of

[0108] After aligning the two planes to be parallel, calculate the distance between the two planes :

[0109] ;

[0110] in, is the common normal vector of the two planes, and are the constants in the equations of the two planes, ensuring that the error does not exceed 0.05.

[0111] Compared with the prior art, the present invention has the following beneficial effects:

[0112] The medium and thick plate surface intelligent inspection and grinding system with multi-task efficient collaborative allocation of the present invention realizes the full process intelligent operation from defect detection to grinding by integrating defect collection, steel plate positioning, real-time task allocation, grinding re-inspection and grinding technology.

[0113] The task allocation system assigns defects to the robotic grinding system for grinding according to the defect depth, location information, anti-interference method and consensus-based greedy algorithm. The standardization and consistency of the grinding process are achieved through the preset steel plate positioning, grinding procedure and grinding re-inspection process.

[0114] The ground rails of the robot grinding system make the robot no longer fixed in one position, but can automatically adjust its position according to the length of the steel plate and move flexibly for grinding. With this design, the robot can cover the entire surface of the steel plate, ensuring a wide range of grinding. Regardless of how the size of the steel plate changes, the robot can adjust its working position in real time to achieve efficient and continuous grinding operations, significantly improving the automation level and grinding accuracy of the production line, while optimizing the efficiency of the grinding process and reducing manual intervention.

[0115] The chip collection equipment equipped in the robot grinding system effectively collects the grinding chips generated during the grinding process, reducing the risk of workers inhaling harmful dust, thereby significantly reducing labor intensity and health risks for workers. The system can achieve continuous production without frequent shutdowns due to manual operation, significantly improving the overall efficiency of the production line.

[0116] Through integration with other automated equipment on the production line, fully automated management and intelligent monitoring are achieved, problems in the production process are discovered and resolved in a timely manner, and the stability and efficiency of the production process are ensured, thereby significantly improving the market competitiveness of the products and the overall efficiency of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0118] Figure 1 It is an overall logic flow chart of a method for intelligent surface maintenance and grinding of medium and thick plates with efficient multi-task collaborative allocation in an embodiment of the present invention;

[0119] Figure 2 It is a task allocation logic flow chart in an embodiment of the present invention;

[0120] Figure 3 It is a logical flow chart of area division in an embodiment of the present invention;

[0121] Figure 4 A schematic diagram of a polishing robot in an embodiment of the present invention;

[0122] Figure 5 It is the overall system structure diagram of the medium and thick plate surface intelligent grinding system in the embodiment of the present invention.

[0123] In the figure: 10, medium and thick plate to be tested; 20, roller table; 30, camera; 40, line laser 3D scanning component; 50, robot control cabinet; 60, robot walking track; 70, grinding robot; 80, photoelectric switch; 90, grinding chip box; 100, chip collection port; 110, grinding wheel; 120, electric spindle; 130, laser displacement sensor; 140, force control compensator. DETAILED DESCRIPTION

[0124] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0125] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0126] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0127] Embodiment 1

[0128] like Figure 5 As shown, the embodiment of the present invention provides a medium and thick plate surface intelligent inspection and grinding system with multi-task efficient collaborative allocation, the system comprising: a medium and thick plate defect detection system, a task allocation system and a robot grinding system;

[0129] The medium and thick plate defect detection system is composed of a plurality of parallel cameras 30 for collecting images and a plurality of line laser three-dimensional scanning components 40, which are used to scan the surface of the steel plate online, obtain defect location information and depth information, and transmit the obtained defect information to the task allocation system;

[0130] The task allocation system is used to receive the defect information of the steel plate surface in real time, divide the working area by the steel plate and prevent the robot from colliding, and use the consensus-based greedy algorithm to allocate the tasks to the robot grinding system in real time;

[0131] The robot grinding system is used to obtain the positioning coordinates of the steel plate through the laser displacement sensor 130, start the corresponding grinding process according to the defect information sent by the medium and thick plate defect detection system, and use the 3D structured light camera to re-check the grinding effect of the grinding area after the grinding is completed.

[0132] In this embodiment, if Figure 2 As shown, the constraints that prevent the robot from colliding include:

[0133] Divide the steel plate into multiple grinding areas, including independent areas and consensus areas, wherein the independent area is an area that can be ground by only one robot, and the consensus area is an area that can be ground by multiple robots together;

[0134] Assign the acquired defect information to the grinding area at the location;

[0135] According to the collaborative robot's working range, set an independent task list and a consensus task list for the robot to prevent interference and collision when the robot is grinding steel plates;

[0136] Calculate the robot's motion envelope to prevent the motion envelopes of collaborative robots from overlapping.

[0137] In this embodiment, if Figure 3 As shown in Figure 1, the process of allocating tasks to the robot grinding system in real time using a consensus-based greedy algorithm includes:

[0138] Calculating the probability of task selection: In order to allocate tasks within the consensus area, each robot calculates its fitness for a task (such as distance, current workload, etc.) and determines the probability of task selection based on this;

[0139] When entering a consensus area, the robot needs to ensure that only one robot is responsible for generating the task list for the current area;

[0140] Through the locking mechanism, the robot checks and updates the consensus task list to ensure that only one robot can create or update the task list of the current area, thereby preventing multiple robots from repeatedly selecting the same task;

[0141] In the consensus area, the robot selects the task closest to itself according to the greedy strategy; the specific steps are as follows:

[0142] Locking the task selection process: Before selecting each task, the robot will lock the entire task selection and execution process to prevent other robots from selecting the same task at the same time;

[0143] Task selection: Select the nearest unassigned task. For the work area of ​​the selected task, check whether it overlaps with the current work area of ​​other robots to ensure that task allocation does not interfere;

[0144] Task execution: Execute the selected task, mark the task as assigned and unlock it after completion; after the task is completed, update the task status to the global consensus task list through the consensus mechanism;

[0145] In the independent area, all tasks are selected according to the greedy algorithm and assigned to the robot's work area;

[0146] In the consensus area, if all tasks cannot be successfully assigned (interference conflicts occur), these unassigned tasks will be added to the global conflict task list and wait for reallocation;

[0147] The robot can periodically check the conflicting task list and greedily select and assign unfinished conflicting tasks until all tasks are completed.

[0148] In this embodiment, obtaining the positioning coordinates of the steel plate by the laser displacement sensor 130 includes:

[0149] The laser displacement sensor 130 is used to obtain the coordinates of the two ends of the long side of the steel plate by running the robot positioning command. , and the coordinates of a point on the wide side ;

[0150] The relative coordinates of the steel plate are calculated using the three-point method, and the intersection coordinates are obtained Angle with horizontal axis Set the robot's steel plate workpiece coordinates.

[0151] In this embodiment, if Figure 4 As shown, the robot grinding system includes a robot, an electric spindle 120, a force control compensator 140, a 3D structured light camera, a laser displacement sensor 130, a robot walking track 60 and a chip collection device;

[0152] The robot uses a laser displacement sensor 130 to measure and calculate the coordinate position of the steel plate;

[0153] The robot is used to set the corresponding grinding program according to the received defect depth and corresponding position information;

[0154] The robot is also used to move laterally via the robot walking track 60 and adjust its position as the length of the steel plate changes, thereby achieving the extension of the grinding area and the precise grinding of the entire range of the steel plate;

[0155] Chip collection equipment is used to collect grinding chips from stainless steel surfaces;

[0156] The 3D structured light camera is used to re-inspect the grinding area to verify whether the grinding meets the requirements;

[0157] The electric spindle 120 is used to provide the rotational power of the grinding tool and adjust the grinding speed and force;

[0158] The force control compensator 140 is used to adjust the downward force of the grinding head in real time to ensure constant pressure during the grinding process;

[0159] The force control compensator 140 is also used to monitor the downforce during grinding in real time and provide feedback to the robot;

[0160] The robot is also used to dynamically adjust the speed of the electric spindle 120 and the downward force of the force control compensator 140 according to the feedback information.

[0161] In this embodiment, after the grinding is completed, the process of re-inspecting the grinding effect of the grinding area using a 3D structured light camera includes:

[0162] Use a 3D structured light camera to re-inspect the grinding area and obtain point cloud data after grinding;

[0163] According to the point cloud data, the depth after grinding, i.e. the re-inspection depth, is calculated;

[0164] Use the RANSAC algorithm to perform plane fitting on the point cloud data to determine the plane reference of the steel plate And the polished plane reference , and then adjust the two planes to be parallel, and calculate the distance between the two planes , to ensure the error between the pre-grinding depth and the actual grinding depth.

[0165] The system of this embodiment is used to implement a corresponding method for intelligent maintenance and grinding of medium and thick plate surfaces with efficient collaborative allocation of multiple tasks in any of the embodiments described later, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0166] It should be noted that a multi-task efficient collaborative distribution medium and thick plate surface intelligent grinding system is embodied in the form of functional units. The term "module" here can be implemented in the form of software and / or hardware, and is not specifically limited to this.

[0167] For example, a "module" may be a software program, a hardware circuit, or a combination of the two that implements the above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor, or a group processor, etc.) and a memory for executing one or more software or firmware programs, a combined logic circuit, and / or other suitable components that support the described functions.

[0168] Embodiment 2

[0169] Based on the same inventive concept, corresponding to any of the above embodiment systems, Figure 1 As shown, the present invention also provides a method for intelligent surface maintenance and grinding of medium and thick plates with efficient collaborative allocation of multiple tasks, which is implemented by using any one of the systems described above, and includes:

[0170] S1: The medium and thick plate defect detection system collects image defects through a camera, and the line laser three-dimensional scanning component 40 scans the steel plate online to provide defect depth information;

[0171] S2: After the steel plate passes through the photoelectric switch 80, the roller table 20 is stopped, the steel plate positioning of the robot grinding system is started, and the position coordinates of the steel plate are obtained;

[0172] S3: The task allocation system is used to obtain the defect depth and corresponding location information, and the work area is divided by steel plates and the robot collision prevention constraints are used to allocate tasks to the robot grinding system in real time using a consensus-based greedy algorithm;

[0173] S4: The robot grinding system starts the corresponding grinding program by receiving the defect task;

[0174] S5: Grinding chips are collected by chip collection equipment to prevent them from covering the grinding marks and polluting the production environment;

[0175] S6: Based on the grinding requirements, the grinding area is inspected using a 3D structured light camera to ensure that the grinding depth meets the preset requirements;

[0176] S7: Re-inspect the grinding area using a 3D structured light camera;

[0177] S8: If there are still defects after grinding, use a 3D structured light camera to re-check the depth of the defect in the grinding area, and perform the next grinding and re-inspection again until the defect grinding is completed and meets the preset requirements;

[0178] S9: According to the needs of the production line, multiple robots are collaboratively controlled to perform grinding operations to achieve efficient grinding of medium and thick plates.

[0179] In this embodiment, in S2, after the steel plate passes through the photoelectric switch 80, the roller conveyor 20 is stopped, the steel plate positioning is started, and the method for obtaining the steel plate position coordinates includes:

[0180] The photoelectric switch 80 is placed under the roller 20. After the steel plate passes through the photoelectric switch 80, the feedback signal stops the roller 20. The robot receives the signal from the roller 20 and starts the corresponding steel plate positioning.

[0181] The laser displacement sensor 130 is used to run the robot positioning command to obtain the coordinates of the two ends of the long side of the steel plate. , and the coordinates of a point on the wide side ;

[0182] The three-point method is used to calculate the relative coordinates of the steel plate. The formula is:

[0183] Calculate the slope of the long side and the intercept :

[0184] ;

[0185] ;

[0186] Calculate the slope of the broadside and the intercept :

[0187] ;

[0188] ;

[0189] Calculate the coordinates of the intersection of the long side and the wide side :

[0190] ;

[0191] ;

[0192] Calculate the angle with the horizontal axis :

[0193] ;

[0194] By obtaining the intersection coordinates Angle with horizontal axis Set the robot's steel plate workpiece coordinates.

[0195] In this embodiment, in S3, dividing the working area by steel plates includes:

[0196] Divide into multiple grinding areas according to the steel plate, including independent areas and consensus areas;

[0197] An independent area is an area that only one robot can polish by itself;

[0198] The consensus area is the range where multiple robots can work together.

[0199] Assign the acquired defect information to the grinding area at the location;

[0200] For the collaborative robot work area, set up independent task lists and consensus task lists for the robots;

[0201] Prevent interference and collision between robots when grinding steel plates, calculate the motion envelope of the robots, and prevent the motion envelope of collaborative robots from overlapping; the specific methods are as follows:

[0202] According to the depth D of the defect, the length L and width W of the defect are expanded; the expansion formula is:

[0203] ;

[0204] ;

[0205] in, is the actual grinding length, is the actual grinding width;

[0206] The lower right corner reference point of the working area is determined by aligning the grinding position of the tool with the defect area; the coordinates of the reference point are calculated as:

[0207] ;

[0208] ;

[0209] in, Is the reference point coordinate, Is the reference point coordinate, The starting point for grinding coordinate, The starting point for grinding coordinate, is the total length of the grinding equipment;

[0210] Combining the length and width of the tool with the expanded defect area, calculate the final length and width of the working area:

[0211] ;

[0212] ;

[0213] in, is the final length of the working area, CW is the final width of the working area, is the total width of the grinding equipment;

[0214] Assume the rotation angle is , use the rotation matrix to calculate the coordinates of the four vertices of the work area after rotation:

[0215] ;

[0216] ;

[0217] in, are the vertex coordinates before rotation, are the coordinates of the vertex after rotation;

[0218] After rotation, take the minimum and maximum values ​​of the X and Y coordinates of the four vertices to determine the boundaries of the working area:

[0219] ;

[0220] in, is the minimum boundary of the working area, is the maximum boundary of the working area, is the minimum boundary of the working area, is the maximum boundary of the working area.

[0221] In this embodiment, in S3, the method of allocating tasks to the robot grinding system in real time using a consensus-based greedy algorithm includes:

[0222] Each robot calculates its fitness to perform a certain task, and determines the probability of task selection based on the fitness; the probability of task selection is:

[0223] ;

[0224] in, is the task selection probability, is the sum of the fitness scores of robot i for all tasks, is the fitness or score of robot i in completing the task;

[0225] When entering a consensus area, the robot needs to ensure that only one robot is responsible for generating the task list for the current area;

[0226] Through the locking mechanism, the robot checks and updates the consensus task list to ensure that only one robot can create or update the task list of the current area;

[0227] In the consensus area, the robot selects tasks according to the greedy strategy and chooses the task closest to itself.

[0228] In the consensus area, the robot selects the task closest to itself according to the greedy strategy; the specific steps are as follows:

[0229] Locking the task selection process: Before selecting each task, the robot will lock the entire task selection and execution process to prevent other robots from selecting the same task at the same time;

[0230] Task selection: Select the nearest unassigned task. For the work area of ​​the selected task, check whether it overlaps with the current work area of ​​other robots to ensure that task allocation does not interfere;

[0231] Task execution: Execute the selected task, mark the task as assigned and unlock it after completion; after the task is completed, update the task status to the global consensus task list through the consensus mechanism;

[0232] In the independent area, all tasks are selected according to the greedy algorithm and assigned to the robot's work area;

[0233] In the consensus area, if all tasks cannot be successfully assigned (interference conflicts occur), these unassigned tasks will be added to the global conflict task list and wait for reallocation;

[0234] The robot can periodically check the conflicting task list and greedily select and assign unfinished conflicting tasks until all tasks are completed.

[0235] In this embodiment, in S6, the method of re-inspecting the grinding area using a 3D structured light camera includes:

[0236] Use a 3D structured light camera to re-inspect the grinding area and obtain point cloud data after grinding;

[0237] According to the point cloud data, the depth after grinding, i.e. the re-inspection depth, is calculated, and the depth and surface smoothness of the grinding area are tested to ensure that the depth error does not exceed 0.05mm; the specific method is:

[0238] Use the RANSAC algorithm to perform plane fitting on the point cloud data to determine the plane reference of the steel plate And the polished plane reference , the formula is as follows:

[0239] Randomly select the minimum number of points from the point cloud data to fit the model. For plane fitting, select three points. , , Determine a plane;

[0240] Calculating the normal vector :

[0241] ;

[0242] After unfolding, the normal vector of the plane is :

[0243] ;

[0244] ;

[0245] ;

[0246] A known point Substitute into the plane equation and calculate the constant term in the plane equation :

[0247] ;

[0248] Calculate the number of interior points, substitute all points into the plane equation, and calculate the distance from the interior points to the plane;

[0249] If the distance from a point to the plane is less than the preset threshold , then the corresponding points are considered to be interior points;

[0250] The distance formula is as follows:

[0251] ;

[0252] in, is the distance from the point to the plane, is a point in the point cloud. Iterate repeatedly until the model with the most inliers is found or the upper limit of the number of iterations is reached. The model with the most inliers is the best fitting plane model.

[0253] The normal vector of the grinding plane is inconsistent with the normal vector of the reference plane, and the grinding plane needs to be adjusted by rotation. The following steps can be used to calculate the rotation matrix and convert the normal vector of the grinding plane Normal vector rotated to the base plane superior;

[0254] The rotation axis is the cross product of the two normal vectors:

[0255] ;

[0256] in, is the rotation axis. If the cross product result is a zero vector, the two normal vectors are already parallel and no rotation is required.

[0257] Calculate the angle θ between the two normal vectors:

[0258] ;

[0259] Use the Rodrigues rotation formula to calculate the rotation matrix based on the rotation axis and rotation angle :

[0260] ;

[0261] in, is the identity matrix, is the antisymmetric matrix of the rotation axis and is defined as follows:

[0262] ;

[0263] in, The rotation axis The weight of

[0264] Calculate the distance between the two planes after aligning them to be parallel :

[0265] ;

[0266] in, is the common normal vector of the two planes, and are the constants in the equations of the two planes, ensuring that the error does not exceed 0.05.

[0267] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0268] It should be noted that some embodiments of the present disclosure are described above. Other embodiments are within the scope of the attached content of the invention. In some cases, it should be understood that the size of the sequence number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The actions or steps recorded in the content of the invention can be performed in an order different from that in the above embodiment and still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0269] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the attached inventive content. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A multi-task efficient collaborative distribution medium and thick plate surface intelligent grinding system, characterized by: The system includes: a medium and thick plate defect detection system, a task allocation system and a robot grinding system; The medium and thick plate defect detection system is composed of multiple parallel cameras for collecting images and multiple line laser three-dimensional scanning components, which are used to scan the surface of the steel plate online, obtain defect location information and depth information, and transmit the obtained defect information to the task allocation system; The task allocation system is used to receive the defect information of the steel plate surface in real time, divide the working area by the steel plate and prevent the robot from colliding, and use the consensus-based greedy algorithm to allocate the task to the robot grinding system in real time; The robot grinding system is used to obtain the positioning coordinates of the steel plate through a laser displacement sensor, start the corresponding grinding process according to the defect information sent by the medium and thick plate defect detection system, and use a 3D structured light camera to re-inspect the grinding effect of the grinding area after the grinding is completed.

2. The system according to claim 1, characterized in that The constraints that prevent the robot from colliding are: Divide the steel plate into multiple grinding areas, including independent areas and consensus areas, wherein the independent area is an area that can be ground by only one robot, and the consensus area is an area that can be ground by multiple robots together; Assign the acquired defect information to the grinding area at the location; According to the collaborative robot's working range, set an independent task list and a consensus task list for the robot to prevent interference and collision when the robot is grinding steel plates; Calculate the robot's motion envelope to prevent the motion envelopes of collaborative robots from overlapping.

3. The system according to claim 1, characterized in that The process of allocating tasks to the robotic grinding system in real time using a consensus-based greedy algorithm includes: Each robot calculates its fitness to perform a certain task and determines the probability of task selection based on the fitness; When entering a consensus area, the robot needs to ensure that only one robot is responsible for generating the task list for the current area; Through the locking mechanism, the robot checks and updates the consensus task list to ensure that only one robot can create or update the task list of the current area; In the consensus area, the robot selects tasks according to the greedy strategy and chooses the task closest to itself.

4. The system according to claim 1, characterized in that The positioning coordinates of the steel plate obtained by the laser displacement sensor include: Use the laser displacement sensor to obtain the coordinates of the two ends of the long side of the steel plate by running the robot positioning command , and the coordinates of a point on the wide side ; The relative coordinates of the steel plate are calculated using the three-point method, and the intersection coordinates are obtained Angle with horizontal axis Set the robot's steel plate workpiece coordinates.

5. The system according to claim 1, characterized in that After grinding is completed, the process of using a 3D structured light camera to recheck the grinding effect of the grinding area includes: Use a 3D structured light camera to re-inspect the grinding area and obtain point cloud data after grinding; According to the point cloud data, the depth after grinding, i.e. the re-inspection depth, is calculated; Use the RANSAC algorithm to perform plane fitting on the point cloud data to determine the plane reference of the steel plate And the polished plane reference , and then adjust the two planes to be parallel, and calculate the distance between the two planes , to ensure the error between the pre-grinding depth and the actual grinding depth.

6. A method for intelligent surface maintenance and grinding of medium and thick plates with efficient collaborative allocation of multiple tasks, the method is implemented by the system according to any one of claims 1 to 5, characterized in that: The method comprises: S1: The medium and thick plate defect detection system collects image defects through the camera, and the line laser 3D scanning component scans the steel plate online to provide defect depth information; S2: After the steel plate passes through the photoelectric switch, the roller stops, the steel plate positioning of the robot grinding system is started, and the position coordinates of the steel plate are obtained; S3: The task allocation system is used to obtain the defect depth and corresponding location information, and the work area is divided by steel plates and the robot collision prevention constraints are used to allocate tasks to the robot grinding system in real time using a consensus-based greedy algorithm; S4: The robot grinding system starts the corresponding grinding program by receiving the defect task; S5: Grinding chips are collected by chip collection equipment to prevent them from covering the grinding marks and polluting the production environment; S6: Based on the grinding requirements, the grinding area is inspected using a 3D structured light camera to ensure that the grinding depth meets the preset requirements; S7: Re-inspect the grinding area using a 3D structured light camera; S8: If there are still defects after grinding, use a 3D structured light camera to re-check the depth of the defect in the grinding area, and perform the next grinding and re-inspection again until the defect grinding is completed and meets the preset requirements; S9: According to the needs of the production line, multiple robots are collaboratively controlled to perform grinding operations to achieve efficient grinding of medium and thick plates.

7. The method according to claim 6, characterized in that In S2, after the steel plate passes through the photoelectric switch, the roller is stopped, the steel plate positioning is started, and the method for obtaining the steel plate position coordinates includes: Place the photoelectric switch under the roller. After the steel plate passes through the photoelectric switch, the feedback signal stops the roller. The robot receives the corresponding steel plate positioning when the roller starts. The laser displacement sensor is used to run the robot positioning command to obtain the coordinates of the two ends of the long side of the steel plate. , and the coordinates of a point on the wide side ; The three-point method is used to calculate the relative coordinates of the steel plate. The formula is: Calculate the slope of the long side and the intercept : ； ； Calculate the slope of the broadside and the intercept : ； ； Calculate the coordinates of the intersection of the long side and the wide side : ； ； Calculate the angle with the horizontal axis : ； By obtaining the intersection coordinates Angle with horizontal axis Set the robot's steel plate workpiece coordinates.

8. The method according to claim 6, characterized in that In S3, dividing the working area by steel plates includes: According to the depth D of the defect, the length L and width W of the defect are expanded; the expansion formula is: ； ； in, is the actual grinding length, is the actual grinding width; The lower right corner reference point of the working area is determined by aligning the grinding position of the tool with the defect area; the coordinates of the reference point are calculated as: ； ； in, Is the reference point coordinate, Is the reference point coordinate, The starting point for grinding coordinate, The starting point for grinding coordinate, is the total length of the grinding equipment; Combining the length and width of the tool with the expanded defect area, calculate the final length and width of the working area: ； ； in, is the final length of the working area, C W is the final width of the working area, is the total width of the grinding equipment; Assume the rotation angle is , use the rotation matrix to calculate the coordinates of the four vertices of the work area after rotation: ； ； in, are the vertex coordinates before rotation, are the coordinates of the vertex after rotation; After rotation, take the minimum and maximum values ​​of the X and Y coordinates of the four vertices to determine the boundaries of the working area: ； in, is the minimum boundary of the working area, is the maximum boundary of the working area, is the minimum boundary of the working area, is the maximum boundary of the working area.

9. The method according to claim 6, characterized in that In S3, the method of allocating tasks to the robot grinding system in real time using a consensus-based greedy algorithm includes: Each robot calculates its fitness to perform a certain task, and determines the probability of task selection based on the fitness; the probability of task selection is: ； in, is the task selection probability, is the sum of the fitness scores of robot i for all tasks, is the fitness or score of robot i in completing the task; When entering a consensus area, the robot needs to ensure that only one robot is responsible for generating the task list for the current area; Through the locking mechanism, the robot checks and updates the consensus task list to ensure that only one robot can create or update the task list of the current area; In the consensus area, the robot selects tasks according to the greedy strategy and chooses the task closest to itself.

10. The method according to claim 6, characterized in that In S6, the method of re-inspecting the grinding area using a 3D structured light camera includes: Use a 3D structured light camera to re-inspect the grinding area and obtain point cloud data after grinding; According to the point cloud data, calculate the depth after grinding, i.e. the re-inspection depth, and detect the depth and surface smoothness of the grinding area to ensure that the depth error does not exceed 0.05mm; the specific method is: Use the RANSAC algorithm to perform plane fitting on the point cloud data to determine the plane reference of the steel plate And the polished plane reference , the formula is as follows: Randomly select the minimum number of points from the point cloud data to fit the model. For plane fitting, select three points. , , Determine a plane; Calculating the normal vector : ； After unfolding, the normal vector of the plane is obtained : ； ； ； A known point Substitute into the plane equation and calculate the constant term in the plane equation : ； Calculate the number of interior points, substitute all points into the plane equation, and calculate the distance from the interior points to the plane; If the distance from a point to the plane is less than the preset threshold , then the corresponding points are considered to be interior points; The distance formula is as follows: ； in, is the distance from the point to the plane, is a point in the point cloud. Iterate repeatedly until the model with the most inliers is found or the upper limit of the number of iterations is reached. The model with the most inliers is the best fitting plane model. By calculating the rotation matrix, the normal vector of the polishing plane Normal vector rotated to the base plane superior; The rotation axis is the cross product of the two normal vectors: ； in, is the rotation axis. If the cross product result is a zero vector, the two normal vectors are already parallel and no rotation is required. Calculate the angle θ between the two normal vectors: ； Use the Rodrigues rotation formula to calculate the rotation matrix based on the rotation axis and rotation angle : ； in, is the identity matrix, is the antisymmetric matrix of the rotation axis and is defined as follows: ； in, The rotation axis The amount of Calculate the distance between the two planes after aligning them to be parallel : ； in, is the common normal vector of the two planes, and are the constants in the equations of the two planes, ensuring that the error does not exceed 0.05.

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