Mechanical arm trajectory planning method for full-automatic laser cleaning of tire mold

Abstract

The invention provides a mechanical arm trajectory planning method for full-automatic laser cleaning of a tire mold. The mechanical arm trajectory planning method comprises the steps that the tire mold is shot through a 3D structured light camera to obtain regional point cloud data; the point cloud data of the area is calibrated through camera hands and eyes, and the point cloud data of the tire mold under a mechanical arm coordinate system is obtained; a laser cleaning head is mounted at the tail end of the mechanical arm; the laser cleaning head moves around the tail end auxiliary shaft according to the motion trail of the mechanical arm; the tail end auxiliary shaft is a horizontal line located at the tail end of the laser cleaning head. Performing feature recognition on the point cloud data to obtain the size of the tire mold; the size of the tire mold at least comprises one of the characteristic size of the pattern block mold and the section size of the side plate mold; and according to the size of the tire mold, the movement track of the mechanical arm and the rotation angle of the laser cleaning head moving around the tail end auxiliary shaft are calculated, and laser cleaning track planning is completed. The invention further provides a mechanical arm track planning device for full-automatic laser cleaning of the tire mold.

Description

Technical Field

[0001] The present disclosure relates to the field of laser cleaning, and more specifically to a robot arm trajectory planning method for fully automatic laser cleaning of tire molds. Background Art

[0002] Tire molds are used in the production of tires. During the production process, rubber, sulfide and other surface residues will be produced, so they need to be cleaned regularly. Traditional tire mold cleaning methods mainly use manual handheld dry ice equipment, chemical cleaning equipment, etc., which are inefficient, ineffective, and not environmentally friendly.

[0003] Since tires have very high production cycle requirements, the efficiency of tire mold cleaning is a very important indicator, so the degree of automation of tire mold cleaning equipment is very important. Existing tire mold cleaning equipment often requires manual measurement, manual teaching, etc. to plan the movement path of the robot arm, and cannot achieve full automation. Therefore, the trajectory planning of the robot arm is a very important link in tire mold laser cleaning. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a robot arm trajectory planning method for fully automatic laser cleaning of tire molds, which combines 3D machine vision to perform trajectory planning on the robot arm.

[0005] The present disclosure provides a robot arm trajectory planning method for fully automatic laser cleaning of tire molds, comprising: using a 3D structured light camera to photograph the tire mold to obtain regional point cloud data; using the camera hand-eye to calibrate the regional point cloud data to obtain the point cloud data of the tire mold in the robot arm coordinate system; installing a laser cleaning head at the end of the robot arm; the laser cleaning head moves around the terminal auxiliary axis according to the motion trajectory of the robot arm; the terminal auxiliary axis is a horizontal line located at the end of the laser cleaning head; performing feature recognition on the point cloud data to obtain the size of the tire mold; the size of the tire mold at least includes one of the characteristic size of the pattern block mold and the cross-sectional size of the side panel mold; according to the size of the tire mold, calculating the motion trajectory of the robot arm and the rotation angle of the laser cleaning head around the terminal auxiliary axis to complete the laser cleaning trajectory planning.

[0006] According to an embodiment of the present disclosure, feature recognition is performed on point cloud data to obtain the size of the tire mold, including: extracting the edge contour of the pattern block mold through straight line and rectangular feature recognition; identifying the characteristic size of the pattern block mold based on the edge contour of the pattern block mold; the characteristic size of the pattern block mold includes at least one of the total length, total width, total height, cross-sectional width, and cross-sectional depth; and / or extracting the edge contour of the side panel mold through arc recognition; identifying the cross-sectional size of the side panel mold based on the edge contour of the side panel mold; the cross-sectional size of the side panel mold includes at least one of the radius and the height.

[0007] According to an embodiment of the present disclosure, the edge contour of the pattern block mold is extracted through straight line and rectangular feature recognition, including extracting a cross-section of the point cloud data along the first direction coordinate axis and the second direction coordinate axis of the robot arm coordinate system according to the laser line width of the laser cleaning head; the first direction coordinate axis and the second direction coordinate axis are perpendicular; the horizontal plane where the first direction coordinate axis and the second direction coordinate axis are located is parallel to the ground of the tire mold; the distance between adjacent cross-sections is the laser line width.

[0008] According to an embodiment of the present disclosure, the edge contour of the side panel mold is extracted through arc recognition, including: identifying the point cloud data of the side panel mold to obtain a series of full circle curves; for every two adjacent full circle curves, averaging the two adjacent full circle curves to obtain an average full circle curve for identifying the cross-sectional size of the side panel mold; wherein, averaging two adjacent full circle curves to obtain the average full circle curve includes: calculating the average value of the radii of the two adjacent full circle curves as the average radius; calculating the midpoint of the centers of the two adjacent full circle curves as the average center; and obtaining the average full circle curve using the average radius and the average center.

[0009] According to an embodiment of the present disclosure, the motion trajectory of the robot arm and the rotation angle of the laser cleaning head around the terminal auxiliary axis are calculated according to the size of the tire mold, including: determining the rotation angle of the terminal auxiliary axis according to the normal vector based on the edge contour of the tread block mold and / or the side panel mold; determining the motion trajectory of the robot arm according to the cross-sectional size and edge contour of the tread block mold and / or the side panel mold and the rotation angle of the terminal auxiliary axis, wherein the movements of the terminal auxiliary axis and the robot arm are independent of each other.

[0010] According to an embodiment of the present disclosure, a tire mold is photographed using a 3D structured light camera to obtain regional point cloud data, including: using a plane carrying tool and a conveyor belt to move the tire mold to a cleaning area where a robot arm is located; dividing the plane carrying tool into a preset number of photographing areas, calling a 3D camera to photograph the plane carrying tool and the tire mold to obtain regional point cloud data; the union of the preset number of photographing areas includes a complete plane carrying tool and the tire mold.

[0011] According to an embodiment of the present disclosure, point cloud data of the tire mold in the robot arm coordinate system is obtained by using the camera hand-eye calibration area point cloud data, including: using the camera hand-eye calibration results to splice the point cloud data of a preset number of shooting areas to obtain overall point cloud data; obtaining a plane based on the overall point cloud data, and identifying the plane with the largest area as a plane carrying tool; filtering the point cloud data of the plane carrying tool to obtain point cloud data of all molds; and using clustering to divide the point cloud data of all molds to obtain separate point cloud data for each tire mold.

[0012] The second aspect of the present disclosure provides a robot arm trajectory planning device for fully automatic laser cleaning of tire molds, which can be used to implement the above method, including: a data acquisition module, used to use a 3D structured light camera to shoot the tire mold to obtain regional point cloud data; a data processing module, used to use the camera hand-eye to calibrate the regional point cloud data, and obtain the point cloud data of the tire mold in the robot arm coordinate system; a laser cleaning head is installed at the end of the robot arm; the laser cleaning head moves around the terminal auxiliary axis according to the motion trajectory of the robot arm; the terminal auxiliary axis is a horizontal line located at the end of the laser cleaning head; a size recognition module, used to perform feature recognition on the point cloud data to obtain the size of the tire mold; the size of the tire mold at least includes one of the characteristic size of the pattern block mold and the cross-sectional size of the side panel mold; a trajectory planning module, used to calculate the motion trajectory of the robot arm and the angle of movement of the laser cleaning head around the terminal auxiliary axis according to the size of the tire mold, and complete the laser cleaning trajectory planning.

[0013] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned robot arm trajectory planning method for fully automatic laser cleaning of tire molds.

[0014] The fourth aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned robot arm trajectory planning method for fully automatic laser cleaning of tire molds.

[0015] According to the robot arm trajectory planning method for fully automatic laser cleaning of tire molds provided by the present disclosure, the tire mold on the tray is photographed by the robot arm carrying a 3D camera to obtain 3D point cloud data, and the motion trajectory of the robot arm and the angle of the terminal auxiliary axis are obtained through the point cloud processing algorithm. Since the motion trajectory planning and angle calculation are fully automatic, and for the terminal auxiliary axis, the optimal angle of the terminal auxiliary axis is determined according to the normal of the surface, and laser cleaning is performed in the optimal posture, therefore, the technical problems of low automation and poor effect of traditional laser cleaning are at least partially solved, and the technical effect of fully automatic and high-precision laser cleaning of tire molds is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a robot arm trajectory planning method for fully automatic laser cleaning of tire molds according to an embodiment of the present disclosure is schematically shown;

[0017] Figure 2 Schematically shows a schematic diagram of a laser cleaning head and a terminal auxiliary shaft according to an embodiment of the present disclosure;

[0018] Figure 3 Schematically shows a schematic diagram of area division of a conveyor belt according to an embodiment of the present disclosure;

[0019] Figure 4 A schematic diagram of dividing a 3D camera photographing area according to an embodiment of the present disclosure is shown schematically;

[0020] Figure 5 A schematic diagram of a local coordinate system of a pattern block mold according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 6 A schematic diagram of first cross-section extraction of a pattern block mold according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 7 A schematic diagram of the second cross-section extraction of a pattern block mold according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 8 A schematic diagram of a local coordinate system of a side panel mold according to an embodiment of the present disclosure is schematically shown;

[0024] Fig. 9 A schematic diagram of calculating the full circle trajectory of a side panel mold according to an embodiment of the present disclosure is schematically shown;

[0025] Fig.10 A schematic diagram of a device for a robot arm trajectory planning device for fully automatic laser cleaning of tire molds according to an embodiment of the present disclosure is schematically shown;

[0026] Fig.11 A block diagram of an electronic device suitable for implementing a robot arm trajectory planning method for fully automatic laser cleaning of tire molds according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0027] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0028] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0029] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0030] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0031] First, the technical terms involved in this disclosure are explained as follows:

[0032] Camera hand-eye calibration: One of the core technologies of machine vision in robot guidance, it is mainly used to determine the transformation relationship between the robot arm and the camera, that is, to unify the relative posture relationship between the robot's end effector (hand) and the camera, complete the coordinate transformation from camera to robot, and enable the robot to accurately locate the target in the camera's field of view.

[0033] Point cloud data: Point cloud data is a collection of a large number of points on the surface of an object obtained through scanning technology (such as laser scanning, camera photography, etc.). Each point contains three-dimensional coordinates (X, Y, Z) and possible other attribute information (such as color, reflectivity, intensity, etc.).

[0034] 3D structured light camera: The working principle of 3D structured light camera is mainly based on structured light projection and binocular vision technology. First, a structured light projector is used to illuminate the surface of the object to be measured (usually a red or green laser light source), forming a series of light spots or light strips. These light spots or light strips will change according to the shape of the object surface. Then, the camera captures these changing images and combines them with computer algorithms to calculate the 3D point cloud data of the object surface.

[0035] Figure 1 The flowchart of the method according to the embodiment of the present disclosure is schematically shown as follows: Figure 1As shown, an embodiment of the present disclosure provides a robot arm trajectory planning method for fully automatic laser cleaning of tire molds, including: using a 3D structured light camera to photograph the tire mold to obtain regional point cloud data; using the camera hand-eye to calibrate the regional point cloud data to obtain the point cloud data of the tire mold in the robot arm coordinate system; installing a laser cleaning head at the end of the robot arm; the laser cleaning head moves around the terminal auxiliary axis according to the motion trajectory of the robot arm; the terminal auxiliary axis is a horizontal line located at the end of the laser cleaning head; performing feature recognition on the point cloud data to obtain the size of the tire mold; the size of the tire mold includes at least one of the characteristic size of the pattern block mold and the cross-sectional size of the side panel mold; according to the size of the tire mold, calculating the motion trajectory of the robot arm and the angle of movement of the laser cleaning head around the terminal auxiliary axis to complete the laser cleaning trajectory planning.

[0036] In this embodiment, if Figure 2 As shown, the laser cleaning head is installed at the end of the robot arm and includes a horizontal end auxiliary axis. The cleaning head can rotate around the axis driven by the motor, so that the working space of the robot arm is larger. Therefore, the laser cleaning motion trajectory not only includes the 6-axis motion trajectory of the robot arm, but also includes the motion trajectory of the end auxiliary axis of the laser cleaning head, with a total of 7 degrees of freedom. For the end auxiliary axis, before the 6-axis trajectory planning of the robot arm, the optimal rotation angle of the end auxiliary axis will be determined according to the normal of the surface, and then the trajectory planning of the robot arm will be carried out to perform laser cleaning in the optimal posture. The movement of the end auxiliary axis is independent of the robot arm. During the movement of the robot arm, the end auxiliary axis does not participate in the linkage.

[0037] Through the embodiments of the present disclosure, through the automated robotic arm and laser cleaning head trajectory planning, multiple tire molds can be cleaned efficiently and accurately at the same time, thereby improving production efficiency; the motion trajectory of the robotic arm and the planning of the rotation angle of the laser cleaning head around the terminal auxiliary axis are utilized to ensure the high quality of the cleaning process.

[0038] On the basis of the above-mentioned embodiment, feature recognition is performed on the point cloud data to obtain the size of the tire mold, including: extracting the edge contour of the pattern block mold through straight line and rectangular feature recognition; identifying the characteristic size of the pattern block mold according to the edge contour of the pattern block mold; the characteristic size of the pattern block mold includes at least one of the total length, total width, total height, cross-sectional width, and cross-sectional depth; and / or extracting the edge contour of the side panel mold through arc recognition; identifying the cross-sectional size of the side panel mold according to the edge contour of the side panel mold; the cross-sectional size of the side panel mold includes at least one of the radius and the height.

[0039] Through the embodiments of the present disclosure, the key features of the tire mold, such as the size and contour of the tread block and the side panel, are accurately extracted through the straight line, rectangle and arc recognition technology. Accurate size data is provided to provide a reliable basis for subsequent trajectory planning and laser cleaning. Image processing and machine learning technology are used to realize automatic recognition of mold size and improve the intelligence level of the system.

[0040] On the basis of the above embodiments, the edge contour of the pattern block mold is extracted by recognizing straight line and rectangular features, including extracting a cross-section of the point cloud data along the first direction coordinate axis and the second direction coordinate axis of the robot arm coordinate system according to the laser line width of the laser cleaning head; the first direction coordinate axis and the second direction coordinate axis are perpendicular; the horizontal plane where the first direction coordinate axis and the second direction coordinate axis are located is parallel to the ground of the tire mold; the distance between adjacent cross sections is the laser line width; and the edge contour of the pattern block mold is extracted using the cross section. The edge contour of the pattern block mold is extracted using the cross section.

[0041] In this embodiment, if Figure 5 As shown in the figure, for the pattern block mold, the edge contour is extracted by recognizing the straight line and rectangle features, thereby establishing a local coordinate system, and then calculating the transformation relationship between the local coordinate system and the robot arm coordinate system to obtain the three-axis coordinates and three-axis Euler angles of the pattern block mold in the robot arm coordinate system. Then, two cross-section point set extractions are performed. The first extraction is as follows Figure 6 As shown, according to the laser line width of the cleaning head, the point cloud data of the pattern block is segmented at intervals of a laser line width along the x-axis direction of the local coordinate system to obtain several groups of yz cross-section point sets; the second extraction is as follows Figure 7 As shown, according to the laser line width of the cleaning head, the point cloud data of the pattern block is segmented at intervals of a laser line width along the y-axis direction of the local coordinate system to obtain several groups of xz cross-section point sets.

[0042] Through the embodiments of the present disclosure, the edge contour of the pattern block mold is extracted to accurately identify its size and shape, providing accurate data for cleaning trajectory planning; the interval of cross-section extraction is determined according to the laser line width to ensure the integrity and accuracy of the data.

[0043] On the basis of the above-mentioned embodiment, the edge contour of the side panel mold is extracted through arc recognition, including: identifying the point cloud data of the side panel mold to obtain a series of full circle curves; arranging the series of full circle curves according to the radius size; for every two adjacent full circle curves, averaging the two adjacent full circle curves to obtain an average full circle curve for identifying the cross-sectional size of the side panel mold; wherein, averaging two adjacent full circle curves to obtain the average full circle curve includes: calculating the average value of the radii of the two adjacent full circle curves as the average radius; calculating the midpoint of the centers of the two adjacent full circle curves as the average center; and obtaining the average full circle curve using the average radius and the average center.

[0044] In this embodiment, if Figure 8 As shown, for the side panel mold, the position of the center of the circle can be identified through arc feature recognition, so as to establish a local coordinate system, and then the transformation relationship between the local coordinate system and the robot arm coordinate system is calculated to obtain the three-axis coordinates and three-axis Euler angles of the side panel mold in the robot arm coordinate system.

[0045] In this embodiment, if Fig. 9 As shown in the figure, multiple full circle curves can be extracted from the point cloud of the side panel. These full circles are arranged according to the radius to obtain a series of full circle curves. Then, for every two adjacent full circles, the average is taken (the specific averaging method is: the sum of the radius is calculated and divided by 2, and the center of the circle is also calculated according to the average value of the centers of the two arcs) to obtain a new arc trajectory, which is the motion trajectory of the robot arm.

[0046] Through the embodiments of the present disclosure, arc recognition technology is used to accurately extract the cross-sectional profile of the side panel mold, providing a basis for subsequent dimension measurement and trajectory planning; by averaging adjacent full circle curves, data noise is reduced and cleaning accuracy and efficiency are improved.

[0047] On the basis of the above embodiments, the motion trajectory of the robot arm and the rotation angle of the laser cleaning head around the terminal auxiliary axis are calculated according to the size of the tire mold, including: according to the size of the tire mold, the motion trajectory of the robot arm and the rotation angle of the laser cleaning head around the terminal auxiliary axis are calculated, including: according to the edge contour of the tread block mold and / or the side panel mold, the rotation angle of the terminal auxiliary axis movement is determined according to the normal vector; the motion trajectory of the robot arm is determined according to the cross-sectional size and edge contour of the tread block mold and / or the side panel mold and the rotation angle of the terminal auxiliary axis movement, wherein the movements of the terminal auxiliary axis and the robot arm are independent of each other.

[0048] In this embodiment, for the pattern block mold, the motion trajectory of the robot arm is the trajectory fitted by the interface points extracted twice from the pattern block mold, which are arranged in the order of shortest time and combined into a complete robot arm motion trajectory, so as to complete the trajectory planning of a single pattern block mold. Furthermore, it is necessary to first calculate the plane normal vector near the pattern block trajectory point to obtain the optimal angle of the terminal auxiliary axis, and then establish the tool coordinate system (TCP) of the robot arm with the terminal auxiliary axis angle, and then calculate the posture of the robot arm at the trajectory point, and finally obtain the 6-axis coordinate data of the trajectory point.

[0049] In this embodiment, for the side panel mold, for this new arc trajectory, the closest end auxiliary axis angle can be obtained based on the calculated point cloud surface normal vector, and then the tool coordinate system (TCP) of the robot arm is established with the end auxiliary axis angle, and then the posture of the robot arm at the trajectory point is calculated, and finally the 6-axis coordinate data of the trajectory is obtained. The multiple trajectories calculated from adjacent full circles can be arranged in order from large to small radius, and can be combined into a complete robot arm motion trajectory, so as to complete the trajectory planning of a single side panel mold.

[0050] Through the embodiments of the present disclosure, the optimal robot arm motion trajectory is planned according to the mold size and contour to ensure that the laser cleaning head can cover the entire surface of the mold; the rotation angle of the laser cleaning head around the terminal auxiliary axis is determined by the normal vector to achieve precise cleaning angle control and improve the cleaning effect.

[0051] On the basis of the above embodiment, a 3D structured light camera is used to photograph the tire mold to obtain regional point cloud data, including: using a plane carrying tool and a conveyor belt to move the tire mold to a cleaning area where a robot arm is located; dividing the plane carrying tool into a preset number of photographing areas, calling a 3D camera to photograph the plane carrying tool and the tire mold to obtain regional point cloud data; the union of the preset number of photographing areas includes a complete plane carrying tool and a tire mold.

[0052] In this embodiment, if Figure 3 As shown, a conveyor belt composed of a sprocket chain is installed on the bracket in front of the robotic arm, and a loading area, a cleaning area and an unloading area are provided on the conveyor belt. The tire mold is placed on a pallet, and the pallet is not fixedly connected to the conveyor belt. It can be conveniently placed on the conveyor belt by a crane, and can also be conveniently removed to achieve loading and unloading. The conveyor belt can move horizontally to transport the pallet from the loading area to the cleaning area, and after the cleaning is completed, it is transported to the unloading area, and at the same time the next pallet is transported from the loading area to the cleaning area. An infrared positioning device is installed in the cleaning area on the conveyor belt to ensure that the pallet moves to the appropriate cleaning area. When the tire mold is in the cleaning area, after the cleaning is completed using the robotic arm according to the trajectory planning method described in the present invention, the conveyor belt will transport the pallet to the unloading area.

[0053] In this embodiment, if Figure 4 As shown, in the 3D vision system disclosed in the present invention, the photographing area of ​​the 3D structured light camera is a rectangle. According to the size of the rectangle, the pallet is divided into multiple photographing areas (in this example, 6 photographing areas are divided), there is an overlapping part between each photographing area, and the edge of the photographing area is outside the edge of the pallet to ensure that the 3D point cloud information of all tire molds can be fully captured.

[0054] Through the embodiments of the present disclosure, a flat carrying tool and a conveyor belt are used to move the tire mold to a clean area, thereby improving the degree of automation; and by overlapping the photographing areas, it is ensured that the complete tire mold and its surrounding environment are photographed, thereby providing complete data for subsequent point cloud data processing.

[0055] On the basis of the above embodiment, the point cloud data of the camera hand-eye calibration area is used to obtain the point cloud data of the tire mold in the robot arm coordinate system, including: using the camera hand-eye calibration results to splice the point cloud data of a preset number of shooting areas to obtain overall point cloud data; obtaining a plane based on the overall point cloud data, and identifying the plane with the largest area as the plane carrying tool; filtering the point cloud data of the plane carrying tool to obtain the point cloud data of all molds; and using clustering to divide the point cloud data of all molds to obtain separate point cloud data for each tire mold.

[0056] In this embodiment, the point cloud data obtained from each photographing area is subjected to coordinate transformation, point cloud splicing and downsampling according to the camera hand-eye calibration result, so that point cloud data containing all tire molds on the pallet can be obtained. The bottom surface of the pallet is extracted according to the principle of maximum area, the point cloud of the bottom surface is filtered out, and then the point cloud is clustered to achieve point cloud segmentation and extract the individual point cloud of each mold.

[0057] Through the embodiments of the present disclosure, through camera hand-eye calibration, the point cloud data of different photographing areas are spliced ​​into overall point cloud data; the point cloud data of the plane carrying tool is filtered out, and the remaining mold point cloud data is clustered and divided to obtain separate point cloud data of each tire mold, which is convenient for subsequent processing.

[0058] Based on the above-mentioned robot arm trajectory planning method for fully automatic laser cleaning of tire molds, the present disclosure also provides a robot arm trajectory planning device for fully automatic laser cleaning of tire molds. The robot arm trajectory planning device for fully automatic laser cleaning of tire molds in this embodiment includes a data acquisition module, a data processing module, a size recognition module and a trajectory planning module.

[0059] The data acquisition module is used to photograph the tire mold using a 3D structured light camera to obtain regional point cloud data.

[0060] The data processing module is used to use the camera hand-eye calibration area point cloud data to obtain the point cloud data of the tire mold in the robotic arm coordinate system; a laser cleaning head is installed at the end of the robotic arm; the laser cleaning head moves around the end auxiliary axis according to the motion trajectory of the robotic arm; the end auxiliary axis is a horizontal line located at the end of the laser cleaning head.

[0061] The size recognition module is used to perform feature recognition on the point cloud data to obtain the size of the tire mold; the size of the tire mold includes at least one of the characteristic size of the tread block mold and the cross-sectional size of the side panel mold.

[0062] The trajectory planning module is used to calculate the motion trajectory of the robot arm and the rotation angle of the laser cleaning head around the terminal auxiliary axis according to the size of the tire mold, and complete the laser cleaning trajectory planning.

[0063] Further, Fig.10 The schematic diagram of the device for the robot arm trajectory planning device for fully automatic laser cleaning of tire molds according to the embodiment of the present disclosure is schematically shown. Fig.10 As shown, the robot arm trajectory planning method for fully automatic laser cleaning of tire molds can be applied to a fully automatic laser cleaning equipment for tire molds, including a robot arm, a 3D camera, a laser cleaning head, a tray, a conveyor belt, and a positioning device.

[0064] Among them, the robotic arm is a 6-axis robotic arm installed on a horizontal base. The laser cleaning head and 3D camera are both installed at the end of the robotic arm. The 3D camera is a surface structured light camera that can capture 3D point cloud data of objects in a rectangular area. The 3D camera and laser cleaning head are installed at the end of the robotic arm, and the tire mold to be cleaned is placed on a pallet. When performing the cleaning work, the conveyor belt transports the pallet to the cleaning area. The robotic arm carries a 3D camera to take pictures of the tire mold on the pallet to obtain 3D point cloud data. Through the point cloud processing algorithm, the 3D point cloud data is processed by coordinate transformation, splicing, downsampling, filtering, clustering segmentation, posture calculation, straight line and arc fitting, etc., to obtain the motion trajectory of the robotic arm and the end auxiliary axis angle, and then the robotic arm carries the laser cleaning head to execute the trajectory to achieve fully automatic laser cleaning.

[0065] The main working process of the fully automatic laser cleaning equipment for tire molds is as follows: a pallet containing several tire molds to be cleaned (including several tread blocks and side panels) is transported to the cleaning area, and the robotic arm carries a 3D camera to take photos at several points above the pallet. The number of photo points must ensure that the area of ​​multiple photos taken can cover the entire pallet. Secondly, the system will obtain several 3D point cloud data from multiple photos, perform coordinate transformation according to the hand-eye calibration results of the camera, and then perform point cloud splicing to obtain the overall 3D point cloud data of the tire mold on the pallet. After that, the system will perform point cloud segmentation and registration on the overall point cloud data to obtain the position and angle information of each mold. Then, the 3D point cloud of each mold is feature recognized to obtain the characteristic dimensions of the tread blocks (including but not limited to total length, total width, total height, cross-sectional width, cross-sectional depth, etc.) and the cross-sectional dimensions of the side panels (including radius, height, etc.). Finally, the position, angle and characteristic dimension data of each mold are used to calculate the motion trajectory points of the robotic arm and the angle of the auxiliary axis at the end to realize the laser cleaning trajectory planning.

[0066] Fig.11A block diagram of an electronic device suitable for implementing a robot arm trajectory planning method for fully automatic laser cleaning of tire molds according to an embodiment of the present disclosure is schematically shown.

[0067] like Fig.11 As shown, the electronic device 1100 according to an embodiment of the present disclosure includes a processor 1101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1102 or a program loaded from a storage part 1108 to a random access memory (RAM) 1103. The processor 1101 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1101 may also include an onboard memory for caching purposes. The processor 1101 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0068] In RAM1103, various programs and data required for the operation of electronic device 1100 are stored. Processor 1101, ROM1102 and RAM1103 are connected to each other via bus 1104. Processor 1101 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM1102 and / or RAM1103. It should be noted that the program may also be stored in one or more memories other than ROM1102 and RAM1103. Processor 1101 may also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0069] According to an embodiment of the present disclosure, the electronic device 1100 may further include an input / output (I / O) interface 1105, which is also connected to the bus 1104. The electronic device 1100 may further include one or more of the following components connected to the I / O interface 1105: an input portion 1106 including a keyboard, a mouse, etc.; an output portion 1107 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1108 including a hard disk, etc.; and a communication portion 1109 including a network interface card such as a LAN card, a modem, etc. The communication portion 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the I / O interface 1105 as needed. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1110 as needed, so that a computer program read therefrom is installed into the storage portion 1108 as needed.

[0070] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0071] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method provided by the embodiment of the present disclosure.

[0072] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0073] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0074] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0075] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A robot arm trajectory planning method for fully automatic laser cleaning of tire molds, characterized in that: include: Using a 3D structured light camera to photograph the tire mold to obtain regional point cloud data; The point cloud data of the area is calibrated by using the camera hand-eye to obtain the point cloud data of the tire mold in the robotic arm coordinate system; a laser cleaning head is installed at the end of the robotic arm; the laser cleaning head moves around the terminal auxiliary axis according to the motion trajectory of the robotic arm; the terminal auxiliary axis is a horizontal line located at the end of the laser cleaning head; Performing feature recognition on the point cloud data to obtain the size of the tire mold; the size of the tire mold at least includes one of the characteristic size of the tread block mold and the cross-sectional size of the side panel mold; According to the size of the tire mold, the motion trajectory of the robot arm and the rotation angle of the laser cleaning head around the terminal auxiliary axis are calculated to complete the laser cleaning trajectory planning.

2. The method according to claim 1, wherein: The step of performing feature recognition on the point cloud data to obtain the size of the tire mold includes: Extracting the edge profile of the pattern block mold by recognizing straight line and rectangular features; identifying the characteristic size of the pattern block mold according to the edge profile of the pattern block mold; the characteristic size of the pattern block mold includes at least one of the total length, total width, total height, cross-sectional width, and cross-sectional depth; And / or extract the edge contour of the side panel mold through arc recognition; identify the cross-sectional dimensions of the side panel mold based on the edge contour of the side panel mold; the cross-sectional dimensions of the side panel mold include at least one of the radius and the height.

3. The method according to claim 2, wherein: The extraction of the edge contour of the pattern block mold by recognizing straight line and rectangular features includes: According to the laser line width of the laser cleaning head, a cross section of the point cloud data is extracted along the first direction coordinate axis and the second direction coordinate axis of the robot arm coordinate system; the first direction coordinate axis and the second direction coordinate axis are perpendicular; the horizontal plane where the first direction coordinate axis and the second direction coordinate axis are located is parallel to the ground of the tire mold; the distance between adjacent cross sections is the laser line width; Using the cross section, the edge profile of the tread block mold is extracted.

4. The method according to claim 2, wherein: The step of extracting the edge contour of the side panel mold by arc recognition includes: Identify the point cloud data of the side panel mold to obtain a series of full circle curves; For every two adjacent full circle curves, averaging the two adjacent full circle curves to obtain an average full circle curve for identifying the cross-sectional size of the side panel mold; The step of averaging two adjacent full circle curves to obtain an average full circle curve includes: Calculating an average value of the radii of the two adjacent full circle curves as an average radius; Calculate the midpoint of the centers of the two adjacent full circle curves as the average center; The average full circle curve is obtained using the average radius and the average center of the circle.

5. The method according to claim 2, wherein: The step of calculating the motion trajectory of the robot arm and the rotation angle of the laser cleaning head around the terminal auxiliary axis according to the size of the tire mold includes: According to the edge profile of the tread block mold and / or the side panel mold, the rotation angle of the terminal auxiliary shaft movement is determined according to the normal vector; Determining the motion trajectory of the robot arm according to the cross-sectional dimensions and edge profiles of the pattern block mold and / or the side panel mold and the rotation angle of the terminal auxiliary shaft; Wherein, the movements of the terminal auxiliary axis and the robotic arm are independent of each other.

6. The method according to claim 1, wherein: The method of photographing the tire mold using a 3D structured light camera to obtain regional point cloud data includes: Using a flat carrying tool and a conveyor belt, the tire mold is moved to the cleaning area where the robot arm is located; The planar carrier is divided into a preset number of photographing areas, and a 3D camera is called to photograph the planar carrier and the tire mold to obtain the regional point cloud data; the union of the preset number of photographing areas includes the complete planar carrier and the tire mold.

7. The method according to claim 6, wherein: The method of calibrating the regional point cloud data by using the camera hand-eye to obtain the point cloud data of the tire mold in the robot arm coordinate system includes: Using the camera hand-eye calibration result, stitching the point cloud data of the preset number of photographing areas to obtain the overall point cloud data; Obtain a plane according to the overall point cloud data, and identify the plane with the largest area as the plane carrying tool; Filtering the point cloud data of the plane carrying tool to obtain the point cloud data of all molds; The point cloud data of all the molds are divided by clustering to obtain separate point cloud data for each tire mold.

8. A robot arm trajectory planning device for fully automatic laser cleaning of tire molds, characterized in that: The device can be used to implement the method according to any one of claims 1 to 7, and the device comprises: A data acquisition module, used for photographing the tire mold using a 3D structured light camera to obtain regional point cloud data; A data processing module is used to calibrate the regional point cloud data using a camera hand-eye to obtain the point cloud data of the tire mold in a robotic arm coordinate system; a laser cleaning head is installed at the end of the robotic arm; the laser cleaning head moves around the terminal auxiliary axis according to the motion trajectory of the robotic arm; the terminal auxiliary axis is a horizontal line located at the end of the laser cleaning head; A size recognition module, used for performing feature recognition on the point cloud data to obtain the size of the tire mold; the size of the tire mold at least includes one of the characteristic size of the tread block mold and the cross-sectional size of the side panel mold; The trajectory planning module is used to calculate the motion trajectory of the robot arm and the rotation angle of the laser cleaning head around the terminal auxiliary axis according to the size of the tire mold, so as to complete the laser cleaning trajectory planning.

9. An electronic device, comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to perform the method according to any one of claims 1 to 7.

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