Automatic motion generation method and system for welding robots

Abstract

This invention provides a method and system for automatically generating the motions of a welding robot, which can reduce pre-work of the welding robot and automatically generate the robot's motions on-site. The method for automatically generating the motions of the welding robot (1) in this embodiment includes: a setting step (Step 1), in which a 3D measurement sensor (4) is configured in a predetermined location; a measurement step (Step 2), in which the welding location is divided into multiple measurement areas and measurement is performed using the 3D measurement sensor (4); a point group processing step (Step 3), in which an extraction surface is generated based on the point group data that can be identified as a plane from the measured point group data, and an imaginary surface is generated based on the invalid areas that cannot be identified due to the lack of point group data; a measurement confirmation step (Step 4), in which it is confirmed whether all measurements in the welding location have been completed; and a three-dimensional model generation step (Step 5), in which a three-dimensional model of the welding location is generated based on the extraction surface and the imaginary surface.

Description

Technical Field

[0001] This invention relates to a method and system for automatically generating the motions of a welding robot, and particularly to a method and system for automatically generating the welding motions of a welding robot that can be brought into the welding site on-site. Background Technology

[0002] For example, in shipbuilding, the hull structure, composed of many components such as longitudinal ribs, corrugated steel plates, and reinforcing ribs, becomes the object of welding operations. The initial stages of shipbuilding involve relatively simple welding operations, such as welding reinforcing materials to the panels, and automation is being promoted in this area. However, as later stages progress, the structures become more three-dimensional and large. Therefore, even with automated welding equipment fixed on the factory production line, the equipment itself must become larger, increasing initial costs and making equipment implementation difficult. Based on this situation, research has been conducted for many years on introducing miniaturized and lightweight welding robots into the welding field for automated welding.

[0003] For example, Patent Document 1 discloses a method that generates a workpiece model based on the workpiece shape information of CAD data, generates a welding model consisting of basic welding lines formed by the mounting lines of mounting components, generates a unit model by dividing the workpiece using area dividing lines that determine the motion range of the welding robot, checks for interference between the workpiece and the welding robot for each basic welding line, generates a welding line shortened to the range where interference does not occur if interference occurs, determines the welding direction, sequence, and path of the welding line for each area, specifies welding design information, and generates an action program.

[0004] Prior art literature

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2004-1226 Summary of the Invention

[0007] The technical problem that the invention aims to solve

[0008] However, as described in Patent Document 1, when using CAD data, the CAD data of the object to be welded must be prepared in advance, which results in a significant time commitment for this pre-processing. Furthermore, there are frequent issues such as delays in the response of CAD data modifications to the motion program, installation of structures not reflected in the CAD data, and discrepancies between the CAD data and the actual object to be welded and its surrounding parts. In such cases, problems arise such as welding areas that are not intended for welding, or collisions between the object to be welded and its surrounding parts with the welding robot.

[0009] The present invention was made in view of the aforementioned problems, and its object is to provide a method and system for automatically generating the motions of welding robots that can reduce pre-work of welding robots and automatically generate the motions of welding robots on site.

[0010] Means for solving technical problems

[0011] According to the present invention, an automatic motion generation method for a welding robot is provided, which automatically generates welding actions of a welding robot configured in a predetermined welding location. The automatic motion generation method for a welding robot is characterized by comprising: a setting step, in which a 3D measurement sensor is configured in the predetermined location; a measurement step, in which the welding location is divided into multiple measurement areas and measurement is performed using the 3D measurement sensor; a point group processing step, in which an extraction surface is generated based on the point group data that can be identified as a plane from the measured point group data, and an imaginary surface is generated based on the invalid areas that cannot be identified due to the lack of point group data; a three-dimensional model generation step, in which a three-dimensional model of the welding location is generated based on the extraction surface and the imaginary surface; and a welding action generation step, in which the welding action of the welding robot is generated using the three-dimensional model.

[0012] Alternatively, the point group processing step can be performed according to each of the measurement steps.

[0013] Alternatively, the setting step can be handled in such a way that the plurality of measurement areas have areas that overlap with adjacent measurement areas.

[0014] Alternatively, the point group processing step may identify the invalid region as being formed by a plane perpendicular to the extracted surface to generate the imaginary surface.

[0015] Alternatively, the three-dimensional model generation steps may include: an extraction and synthesis step, in which the extracted surfaces are converted into a robot coordinate system and synthesized; and an imaginary surface synthesis step, in which the imaginary surfaces are converted into a robot coordinate system and synthesized.

[0016] Alternatively, the three-dimensional model generation step may include a plate thickness shape confirmation step that treats a plane with a width equivalent to the thickness of the steel plate as the end face of the steel plate.

[0017] Alternatively, the three-dimensional model generation step may include: a shadow shape restoration step that extracts and restores the shadow parts that do not form the extracted surface and the imaginary surface.

[0018] Alternatively, the welding action generation step may include: generating the welding action by taking into account the tilt of the site where the welding robot is configured.

[0019] Furthermore, according to the present invention, an automatic motion generation system for welding robots is provided, which automatically generates welding actions of welding robots configured in a predetermined welding site. The automatic motion generation system for welding robots is characterized by comprising: a 3D measurement sensor that divides the welding site into multiple measurement areas and performs measurements; and a computing device that generates a three-dimensional model of the welding site based on the data from the 3D measurement sensor. The computing device is configured to generate an extraction surface based on point group data that can be identified as planes from the measured point group data, generate an imaginary surface based on invalid areas that cannot be identified due to the lack of point group data, generate a three-dimensional model of the welding site based on the extraction surface and the imaginary surface, and use the three-dimensional model to generate the welding actions of the welding robot.

[0020] Invention Effects

[0021] According to the above-described automatic motion generation method and system for welding robots of the present invention, a 3D measurement sensor is used to generate a three-dimensional model of the welding site, thus eliminating the need for prior use of CAD data to generate the welding robot's welding actions. Therefore, according to the present invention, pre-processing for welding robots can be reduced, and the welding robot's actions can be automatically generated on-site. Attached Figure Description

[0022] Figure 1 This is a perspective view illustrating an example of a welding robot used in an automatic motion generation system for a welding robot according to an embodiment of the present invention.

[0023] Figure 2 This is an overall flowchart illustrating an embodiment of the automatic motion generation method for a welding robot according to the present invention.

[0024] Figure 3 This is a flowchart illustrating the steps of point group processing.

[0025] Figure 4 This is a flowchart illustrating the steps involved in generating a 3D model.

[0026] Figure 5 The diagram shows an example of the planar shape after the point group processing steps. (A) shows the first measurement area, (B) shows the second measurement area, (C) shows the third measurement area, (D) shows the fourth measurement area, (E) shows the fifth measurement area, and (F) shows the sixth measurement area.

[0027] Figure 6 The figure shows an example of the planar shape after the point group processing steps. (A) shows the seventh measurement area, (B) shows the eighth measurement area, and (C) shows the ninth measurement area.

[0028] Figure 7The images show the surface extraction and compositing steps. (A) shows the compositing method, and (B) shows the compositing result.

[0029] Figure 8 This is an image illustrating the steps of compositing an imaginary surface.

[0030] Figure 9 This is an image illustrating the plate thickness and shape confirmation steps.

[0031] Figure 10 This is an image illustrating the steps involved in restoring the shape of the shadow area. Detailed Implementation

[0032] The following uses Figures 1-10 The embodiments of the present invention will be described here. Figure 1 This is a perspective view illustrating an example of a welding robot used in an automatic motion generation system for a welding robot according to an embodiment of the present invention.

[0033] Figure 1 The welding robot 1 shown is a portable welding robot equipped with a foldable multi-joint arm 2. The multi-joint arm 2 includes, for example, a base 21 disposed on a rotary table 2t, an upper arm 22 rotatably connected to the front end of the base 21, a lower arm 23 rotatably connected to the front end of the upper arm 22, a wrist 24 rotatably connected to the front end of the lower arm 23, and a tool 25 rotatably connected to the front end of the wrist 24.

[0034] The upper arm 22, lower arm 23, wrist 24, and tool section 25 are configured to be folded and mounted on the base 21. A rotary table 2t is mounted on the base 3, configured to allow the multi-joint arm 2 to rotate about the Z-axis. A welding torch 2w is mounted at the front end of the tool section 25.

[0035] Furthermore, a 3D measurement sensor 4 capable of acquiring three-dimensional shape as point group data is disposed on the front surface of the upper arm 22. The 3D measurement sensor 4 is, for example, a distance image sensor capable of acquiring a distance image up to the object being welded. However, the 3D measurement sensor 4 is not limited to a distance image sensor; any sensor capable of acquiring three-dimensional point group data is acceptable. By disposing of the 3D measurement sensor 4 on the welding robot 1, the 3D measurement sensor 4 can be moved into the welding area along with the welding robot 1. Moreover, by disposing of the 3D measurement sensor 4 on the upper arm 22, compared to placing the sensor near the tool section 25, the effects of welding spatter and fumes can be reduced.

[0036] The aforementioned multi-joint arm 2 in Figure 1In the state shown, if expressed using orthogonal coordinates of the XYZ axes, it has a total of 5 degrees of freedom: the base 21 around the Z-axis, the upper arm 22 around the X-axis, the lower arm 23 around the X-axis, the wrist 24 around the X-axis, and the tool 25 around the Y-axis. It should be noted that the structure of the multi-joint arm 2 described above is only one example and is not limited to the structure shown in the figure.

[0037] A control box 5, housing the motion automatic generation system of the welding robot 1 and the control device of the welding robot 1, is mounted on the base 3. Furthermore, a handle 6 for moving the welding robot 1 is mounted on the base 3. During transport, the handle 6 rotates to the top of the folded multi-joint arm 2, and during setup, it rotates to the front of the base 3 as shown in the diagram.

[0038] Furthermore, handles 31 for lifting the welding robot device 1 placed on the floor can be provided on both sides of the base 3. A laser pointer 32 for positioning the welding robot device 1 can also be provided at the front of the base 3. A fixing magnet (not shown) made of a permanent magnet or electromagnet and legs 33 can also be provided at the bottom of the base 3. Although not shown, sensors such as accelerometers and inclinometers for measuring the tilt of the area where the welding robot 1 is placed can also be provided on the base 3.

[0039] The automatic motion generation system for the welding robot 1 in this embodiment is an automatic motion generation system that automatically generates the welding motions of the welding robot 1 configured in a predetermined welding site. It includes a 3D measurement sensor 4 that divides the welding site into multiple measurement areas and performs measurements, and a computing device that generates a three-dimensional model of the welding site based on the data from the 3D measurement sensor 4. The computing device is configured to automatically generate the welding motions of the welding robot 1 based on the process described later.

[0040] Next, refer to Figures 2 to 10 The invention also describes an automatic motion generation method for a welding robot 1 according to one embodiment. Figure 2 This is an overall flowchart illustrating an embodiment of the automatic motion generation method for a welding robot according to the present invention. Figure 3 This is a flowchart illustrating the steps of point group processing. Figure 4 This is a flowchart illustrating the steps involved in generating a 3D model.

[0041] The automatic motion generation method for the welding robot 1 in this embodiment is an automatic motion generation method for the welding robot 1 configured in a predetermined welding site. It includes: a setting step (Step 1) of configuring a 3D measurement sensor 4 in the predetermined site; a measurement step (Step 2) of dividing the welding site into multiple measurement areas and performing measurements using the 3D measurement sensor 4; a point group processing step (Step 3) of generating an extraction surface based on the point group data that can be identified as planes, and generating a hypothetical surface based on the invalid areas where point group data cannot be obtained; a measurement confirmation step (Step 4) of confirming whether all measurements in the welding site have been completed; a three-dimensional model generation step (Step 5) of generating a three-dimensional model of the welding site based on the extraction surface and the hypothetical surface; a job setting step (Step 6) of inputting welding-related data such as welding location and weld web height; a welding motion generation step (Step 7) of using the three-dimensional model to generate the welding motion of the welding robot; a construction sequence setting step (Step 8) of setting the construction sequence of the welding locations included in the three-dimensional model; and a welding operation step (Step 9) of executing a predetermined welding operation by the welding robot 1 based on the automatically generated welding motion and construction sequence.

[0042] Step 1 is a step of configuring the 3D measurement sensor 4 for measuring a predetermined area of ​​the welding site. In this embodiment, the welding robot 1 is positioned in the predetermined location, its orientation is adjusted, and the 3D measurement sensor 4 is configured accordingly.

[0043] The measurement step Step 2 is, for example, the following steps: a pattern light (infrared light) of random points is irradiated from the 3D measurement sensor 4, and a sensor capable of obtaining a distance image by taking pictures of the pattern light (infrared light) using one or more infrared cameras is used to obtain point group data of the measurement area based on the distance image.

[0044] Step 3 of the point group processing is a step of extracting or generating a planar shape based on the point group data. Regularly arranged point group data is obtained from a plane (e.g., the web surface of a longitudinal rib) that is arranged perpendicular to the floor surface and positioned opposite the 3D measuring sensor 4. On the other hand, in the case of horizontal planes such as those with stiffeners arranged perpendicular to the web surface of longitudinal ribs, the incident angle of the illumination light from the 3D measuring sensor 4 becomes shallower, and point groups cannot be measured in this plane, resulting in areas lacking point group data from the viewpoint of the 3D measuring sensor 4.

[0045] In this embodiment, the plane extracted from the point group data that can be identified as a plane is defined as an "extracted surface," and the unidentifiable region where point group data cannot be obtained from the viewpoint of the 3D measurement sensor 4 is defined as an "invalid region." Furthermore, the plane generated from the invalid region is defined as an "imaginary surface."

[0046] Point group processing step Step 3 includes the process of generating extraction surfaces based on the point group data, including the process of identifying invalid regions as being formed by planes perpendicular to the extraction surfaces and generating imaginary surfaces. Specifically, point group processing step Step 3 is based on... Figure 3 The process shown is used for handling this.

[0047] like Figure 3 As shown, the point group processing step Step 3 includes: Step 31, data acquisition step of obtaining distance image data; Step 32, coordinate transformation step of converting distance image data into three-dimensional coordinates of the sensor coordinate system; Step 33, local plane calculation step of calculating local planes based on the coordinate transformed data; Step 34, marking step of marking local planes; Step 35, shape extraction step of extracting plane shapes (extracting surfaces); Step 36, invalid region extraction step of extracting invalid regions based on distance image data from the viewpoint of the 3D measurement sensor 4; and Step 37, imaginary surface generation step of generating imaginary surfaces based on invalid regions.

[0048] From coordinate transformation step 32 to shape extraction step 35, after compressing and processing the data acquired using the 3D measurement sensor 4, general methods such as calculating the normal direction of a small region and distance-based judgment can be used; detailed explanations are omitted here. It should be noted that in the marking step 34, the data is distinguished and marked according to each plane, for example, based on the angle formed by the surface normals and the distance between surfaces. Furthermore, if the 3D measurement sensor 4 is a 3D sensor capable of directly acquiring point group data, the data acquisition step 31 and the coordinate transformation step 32 are the same step.

[0049] Invalid regions are formed by surfaces with shallow laser incident angles or areas without objects within the measurable range of the 3D measuring sensor 4. Therefore, in the invalid region extraction step 36, small areas are ignored, and elongated regions are cut out (such as relatively slender components like large steel structures that become the measurement objects), thereby reducing the amount of data processing and sorting out invalid regions. It should be noted that in the invalid region extraction step 36, if the 3D measuring sensor 4 is a 3D sensor capable of directly acquiring point group data, a data creation step based on the distance image of the viewpoint of the 3D measuring sensor 4 can also be performed first.

[0050] In the imaginary surface generation step 37, candidate surface shapes orthogonal to the surrounding surfaces of the invalid area are created based on the position of the 3D measurement sensor 4, generating a surface shape where the invalid area is shaded. For example, if the surface is higher than the viewpoint of the 3D measurement sensor 4, the invalid area is a trapezoidal shape with a lower base longer than the upper base; if the surface is lower than the viewpoint of the 3D measurement sensor 4, the invalid area is a trapezoidal shape with an upper base longer than the lower base.

[0051] Step 4 of the measurement confirmation process is to confirm whether the overall measurement of the welding site has been completed (whether the predetermined number of measurements has been completed). If all measurements have not been completed (N: No), return to Step 1 of the setup process. If all measurements have been completed (Y: Yes), proceed to the next step.

[0052] In Step 1, the welding robot 1 is reconfigured to a predetermined location, or the multi-joint arm 2 is moved to change the orientation and posture of the 3D measurement sensor 4, thus setting the 3D measurement sensor 4. At this time, the 3D measurement sensor 4 is set up such that multiple measurement areas have regions that overlap with adjacent measurement areas. Through this process, point group data of the same surface can be obtained in multiple measurement areas, making it easy to identify the same surface and easily process the synthesis of planes.

[0053] Here, Figure 5 The diagram shows an example of the planar shape after the point group processing steps. (A) shows the first measurement area, (B) shows the second measurement area, (C) shows the third measurement area, (D) shows the fourth measurement area, (E) shows the fifth measurement area, and (F) shows the sixth measurement area. Figure 6 The figure shows an example of the planar shape after the point group processing steps. (A) shows the seventh measurement area, (B) shows the eighth measurement area, and (C) shows the ninth measurement area.

[0054] Figure 5 (A)~ Figure 6 (C) shows a planar shape extracted or generated based on point group data of the measurement area measured in the Nth measurement (N is an integer from 1 to 9). Figure 5 (A)~ Figure 5 In the planar shapes of the first to sixth measurement areas shown in (F), no invalid areas were extracted; the areas consisted only of extracted surfaces. Furthermore, Figure 6 (A)~ Figure 6 The planar shape shown in the right figure of (C) includes imaginary surfaces S1 to S3 generated from the invalid areas (blank parts) shown in the left figures of each figure.

[0055] In this embodiment, after synthesizing all the measured point group data based on coordinate transformation, the planar shape is not extracted or generated. The point group data is processed for each measurement area. That is, the point group processing step Step 3 is performed according to each measurement step Step 2. Therefore, the amount of data processed in the point group processing step Step 3 can be reduced, and the processing time for extracting or generating the planar shape can be shortened.

[0056] Step 5 of the 3D model generation process is, for example, based on... Figure 5 (A)~ Figure 6 The steps for generating a 3D model based on the planar shape shown in (C). Specifically, based on Figure 4 The process shown is used for handling this.

[0057] like Figure 4 As shown, the 3D model generation step 5 includes, for example, the extraction surface synthesis step 51, which converts the extracted surface into the robot coordinate system and synthesizes it; the imaginary surface synthesis step 52, which converts the imaginary surface into the robot coordinate system and synthesizes it; the plate thickness shape confirmation step 53, which treats a plane with a width equivalent to the plate thickness as the end face of the steel plate; and the shadow shape restoration step 54, which extracts and restores the shadow areas that were not formed by the extraction surface and the imaginary surface.

[0058] Step 51, the surface extraction and synthesis step, is a step of synthesizing the extracted surfaces obtained through Step 3, the point group processing step. Specifically, Step 51 includes the following steps: Step 511, obtaining all the shapes of the extracted surfaces; Step 512, converting the extracted surface shapes into the robot coordinate system; Step 513, confirming whether the extracted surface is the same surface region as the registered surface; Step 514, registering the extracted surface as a new surface if it is not the same surface region as the registered surface (N); Step 515, re-merging and recalculating the plane and shape if the extracted surface is the same surface region as the registered surface (Y); and Step 516, confirming whether all the extracted surfaces have been processed.

[0059] For example, Figure 5 (A)~ Figure 6 (C) shows the planar shape of the first to ninth measurement areas, which is a sensor coordinate system. Therefore, the viewpoint differs for each measurement area. Thus, the coordinate system is unified by converting the coordinates from the sensor coordinate system to the robot coordinate system. Here, Figure 7 The images show the surface extraction and compositing steps. (A) shows the compositing method, and (B) shows the compositing result.

[0060] Figure 7The two planar shapes shown in the upper part of (A) are Figure 5 (A) and Figure 5 (B) shows the planar shapes of the first and second measurement regions. The process in Step 51, where the surface extraction and synthesis steps are performed sequentially starting from the first measurement region, will be explained. The coordinates of the planar shapes contained in the first measurement region A1 are converted to the robot coordinate system, and all the planes contained in the first measurement region A1 are registered as new surfaces in the motion automatic generation system. For example... Figure 7 As shown in (A), faces M1 to M4 are registered as new faces.

[0061] Next, the planar shape coordinates of the second measurement area A2 are converted into the robot coordinate system to confirm whether it has a surface area identical to the registered surfaces M1 to M4 registered in the automatic motion generation system. At this point, if... Figure 7 As shown in (A), if the second measurement area A2 contains a surface area that is the same as the registered surface M1, then the coordinates are used to... Figure 7 As shown in the lower paragraph of (A), the planar shape contained in the first measurement area A1 is combined with the planar shape contained in the second measurement area A2. It should be noted that surfaces that do not form the same surface area as the already registered surfaces M1 to M4 are registered as new surfaces in the automatic motion generation system.

[0062] The following process is repeated for all measurement areas, thereby enabling... Figure 7 As shown in (B), a quasi-3D model is generated by synthesizing all the extracted surfaces. Here, the diagram illustrates the process based on... Figure 5 (A)~ Figure 6 (C) shows the case where the plane shape of the first to ninth measurement areas is extracted to generate a quasi-3D model.

[0063] Step 52, the imaginary surface synthesis step, is the step of synthesizing the imaginary surfaces generated by Step 3, the point group processing step. Specifically, Step 52 includes the following steps: Step 521, obtaining all the imaginary surface shapes; Step 522, converting the imaginary surface shapes into the robot coordinate system; Step 523, verifying the validity of the imaginary surfaces; Step 524, synthesizing the imaginary surfaces; and Step 525, verifying whether all the imaginary surfaces have been processed.

[0064] The third step, Step 523, is to confirm whether the imaginary surface is not recognized as a normal plane in other measurement areas. For example, if the imaginary surface exists in the field of view area created by the sensor origin and the extracted surface outline of other measurement areas, the imaginary surface is set to invalid and excluded from the composite object. It should be noted that the fourth step, Step 524, is processed in essentially the same way as the extracted surface composite step, Step 51, described above.

[0065] Here, Figure 8 This is an image illustrating the steps of compositing an imaginary surface. Figure 8 The imaginary surface S1′ shown in the upper section represents a coordinate transformation and magnification of the imaginary surface S1 contained in the seventh measurement region. Furthermore, Figure 8 The imaginary surface S2′ shown in the middle section underwent coordinate transformation on the imaginary surface S2 contained in the eighth measurement region and was synthesized with the imaginary surface S1. Furthermore, Figure 8 The imaginary surface S3′ shown in the lower section performs coordinate transformation on the imaginary surface S3 contained in the ninth measurement area and is combined with the imaginary surface S2′.

[0066] Step 53, the plate thickness shape confirmation step, is a process of estimating a plane based on the plate thickness shape. The portion of the steel plate considered as the plate thickness shape can be considered as forming the end face of a plane, thus allowing the estimation of the plane forming that end face. For example, relative to a slender shaped surface considered as the plate thickness (a surface with an external shape having a nearly parallel length direction), perpendicular double surfaces (both sides of the plate thickness) are created based on the length direction outline until a plane with the same surface normal direction and a structural range equal to the width of the component is obtained. Through the plate thickness shape confirmation step 53, surfaces that appear to float on the 3D model can be eliminated.

[0067] Here, Figure 9 This is an image illustrating the plate thickness and shape confirmation steps. Figure 9 The figure shown in the upper part is the 3D model after processing the extracted surface synthesis step 51 and the imaginary surface synthesis step 52. It should be noted that the imaginary surface L1 in the figure represents the upper measurement boundary surface, and the imaginary surfaces L2 and L3 represent the lateral measurement boundary surfaces. Figure 9 The diagram shown in the upper section is a three-dimensional model added by extracting the plate thickness shape part D from the diagram in the upper section and estimating the planar shape.

[0068] Step 54, the shadow shape restoration step, is a step to restore the shape of the part that is the shadow of the component and whose face is not in contact with it. For example, it is restored by extending the shape of one side that intersects at an imagined angle in a nearby plane. Through the processing of the shadow shape restoration step 54, faces that are unnaturally separated on the 3D model can be eliminated.

[0069] Here, Figure 10 This is an image illustrating the steps involved in restoring the shape of the shadow area. Figure 10 The image shown above is the 3D model after processing the extracted surface synthesis step 51 and the imaginary surface synthesis step 52. The image shows the shadow area H, the portion within the area enclosed by dashed lines where no surfaces were generated. Through the above processing, this portion is made as... Figure 10 The shadow area H is restored as shown in the lower section of the figure.

[0070] Step 6 of the job setup is the step of inputting the data required for welding, such as the welding location and weld web height. For example, in the case of welding a welding site with the same structure as a site that has already been welded, the data required for welding, such as the welding location and weld web height, is known. Therefore, Step 6 of the job setup can also be processed before Step 1 of the setup.

[0071] Step 7, the welding action generation step, is a step of generating welding actions for the welding robot 1 (multi-joint arm 2) based on the 3D model generated through the above processing. In Step 7, the tilt angle of the welding robot 1 can also be calculated using data from acceleration sensors and tilt sensors configured on the base 3, and the action can be generated using welding conditions that take into account the direction of gravity of the welding seam.

[0072] Furthermore, the welding motion generation step Step 7, for example, performs processes such as determining the posture of the welding robot 1 and confirming the motion and interference of the multi-joint arm 2, and generates motion data such as movement up to the welding start position, sensor detection motion, welding motion for each gap, and movement up to the retreat position. It should be noted that the welding motion generation step Step 7 is essentially the same as the motion generation process of a general robot with a multi-joint arm, so detailed descriptions are omitted here.

[0073] Step 8 of the construction sequence setting step is to set the welding sequence by taking into account factors such as the falling of welding slag, for example, prioritizing longitudinal welding over transverse welding or prioritizing lower welding over upper welding.

[0074] Welding operation step 9 is a step in which welding is performed by welding robot 1 based on the generated welding actions and construction sequence. For example, welding robot 1 is positioned at a predetermined position, welding torch 2w is moved to the starting position, joint sensing detection is performed, and welding corresponding to the gap length is carried out.

[0075] According to the automatic motion generation method of the welding robot 1 in this embodiment described above, a three-dimensional model of the welding site is generated using a 3D measurement sensor 4, thus eliminating the need to generate the welding motion of the welding robot 1 using CAD data in advance. Therefore, according to this embodiment, pre-processing of the welding robot 1 can be reduced, and the motion of the welding robot 1 can be automatically generated on-site.

[0076] Furthermore, the program for executing the automatic motion generation method of the welding robot 1 according to this embodiment using a computing device such as a computer can be stored in a storage device such as an SSD (Solid State Drive) or HDD (Hard Disk Drive) within the control box 5, or recorded on a recording medium that can be read using a reading device configured in the control box 5, or stored on an external control box connected to the control box 5 via a cable, and can also be executed using a computing device on the external control box side. Moreover, the program can also be configured to be installed on the computing device via a network such as the Internet.

[0077] Recording media can be, for example, USB (Universal Serial Bus) storage devices equipped with semiconductor memory such as flash memory. Recording media can also be magnetic disks and optical discs. Optical discs include, for example, CDs (Compact Discs) and DVDs (Digital Versatile Discs).

[0078] This invention is not limited to the embodiments described above, and it goes without saying that various modifications can be made without departing from the spirit of this invention.

[0079] Explanation of reference numerals in the attached figures

[0080] 1 Welding robot, 2 Multi-joint arm, 2t rotary table, 2w welding torch, 3 Base, 4 3D measuring sensor, 5 Control box, 6 Handle, 21 Base, 22 Upper arm, 23 Lower arm, 24 Wrist, 25 Tooling unit, 31 Handle, 32 Laser indicator, 33 Leg.

Claims

1. A method for automatically generating the motions of a welding robot, comprising automatically generating the welding motions of a welding robot configured in a predetermined welding location, the method comprising: The setup steps involve configuring the 3D measurement sensor at the designated location. The measurement step involves dividing the welding area into multiple measurement zones and performing measurements using the 3D measurement sensor. The point cluster processing steps generate an extraction surface based on the point cluster data that can be identified as a plane from the measured point cluster data, and generate an imaginary surface based on the invalid areas that cannot be identified due to the lack of point cluster data. The three-dimensional model generation step involves generating a three-dimensional model of the welding site based on the extracted surfaces and the hypothetical surfaces; and The welding motion generation step uses the aforementioned 3D model to generate the welding motion of the welding robot. The point group processing step identifies the invalid region as being formed by a plane perpendicular to the extracted surface and generates the imaginary surface.

2. The automatic motion generation method for a welding robot according to claim 1, wherein, The point group processing steps are performed according to each of the measurement steps.

3. The automatic motion generation method for a welding robot according to claim 1, wherein, The setup steps are performed in such a way that the plurality of measurement areas have areas that overlap with adjacent measurement areas.

4. The automatic motion generation method for a welding robot according to claim 1, wherein, The three-dimensional model generation steps include: an extraction and synthesis step, in which the extracted surfaces are converted into a robot coordinate system and synthesized; and an imaginary surface synthesis step, in which the imaginary surfaces are converted into a robot coordinate system and synthesized.

5. The automatic motion generation method for a welding robot according to claim 1, wherein, The three-dimensional model generation step includes: a plate thickness and shape confirmation step that treats a plane with a width equivalent to the thickness of the steel plate as the end face of the steel plate.

6. The automatic motion generation method for a welding robot according to claim 1, wherein, The three-dimensional model generation steps include: extracting and restoring the shadow parts that do not form the extracted surface and the imaginary surface, and restoring the shadow part shape.

7. The automatic motion generation method for a welding robot according to claim 1, wherein, The welding action generation step includes: taking into account the tilt of the site where the welding robot is configured to generate the welding action.

8. An automatic motion generation system for a welding robot, which automatically generates welding actions of a welding robot configured in a predetermined welding location, characterized in that it comprises: A 3D measurement sensor divides the welding area into multiple measurement zones and performs measurements; and The computing device generates a three-dimensional model of the welding site based on the data from the 3D measurement sensor. The computing device is configured to generate an extraction surface based on the point group data that can be identified as a plane from the measured point group data, generate an imaginary surface based on the invalid areas that cannot be identified due to the lack of point group data, generate a three-dimensional model of the welding site based on the extraction surface and the imaginary surface, and use the three-dimensional model to generate the welding actions of the welding robot. The computing device is further configured to generate the imaginary surface by recognizing that the invalid region is formed by a plane perpendicular to the extraction surface.

Images