Information processing device, information processing method, and information processing program

Abstract

This invention provides an information processing device, an information processing method, and an information processing program that enable the creation of offline teaching programs for robots in a short time while minimizing human error and work processes. [Solution] An information processing device for specifying the posture of a robot that processes a vehicle body, comprising: a comparison unit that compares the position information of each of a plurality of tags assigned along line data indicating the processing order in the vehicle body design data with the position information of a plurality of tags assigned to the design data of an existing vehicle for each movement step of the robot's posture given by the vehicle body's offline program; a detection unit that detects one or more tags that satisfy predetermined criteria; and an aggregation unit that aggregates the detected one or more tags.

Description

[Technical Field]

[0001] This disclosure relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] In offline teaching of robots for tasks such as processing automobile bodies, humans have traditionally created offline teaching programs based on their experience and past data. However, this conventional method suffers from significant human variability and complex work processes. Furthermore, the offline teaching process involves manually rewriting data based on existing vehicle data to match the structure of new vehicle models, which is extremely time-consuming.

[0003] Patent documents 1 to 3 disclose techniques for creating robot programs by matching data of existing products with data of new products. Patent document 1 discloses a technique for searching for the optimal existing data that has the highest degree of match with new workpiece feature information obtained from the 3D shape data of a new workpiece, using the existing data obtained from the 3D shape data of a workpiece, and generating a robot transport work program for the new workpiece using this data. Patent document 2 discloses a technique for setting new teaching data based on existing teaching data, in which a work point is selected that is within a predetermined distance from the target work point, that the difference in posture from the posture of the end effector relative to the target work point is within a predetermined range, and that the work point has the smallest difference in posture of the joints of the articulated robot when moved from the target work point. Patent document 3 discloses a technique for comparing voxel data of a measured 3D model of a component on an actual robot system with voxel data of a virtual 3D model of a robot's surrounding structure in a virtual space, obtaining information on the shape error of the virtual 3D model, and correcting the robot's movement path based on this information. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2008-015683 [Patent Document 2] Japanese Patent Publication No. 2007-144537 [Patent Document 3] Japanese Patent Publication No. 2016-140958 [Overview of the project] [Problems that the invention aims to solve]

[0005] However, the technologies disclosed in Patent Documents 1 to 3 were unable to fully utilize data from existing products.

[0006] In view of the above problems, the present invention aims to provide an information processing device, an information processing method, and an information processing program that can reduce human error and work processes, and enable the creation of an offline teach program for a robot in a short amount of time. [Means for solving the problem]

[0007] A first aspect of the present invention is an information processing device for specifying the posture of a robot that processes a vehicle body, the information processing device comprising: a comparison unit that compares the position information of each of a plurality of tags assigned along line data indicating the order of processing in the vehicle body design data with the position information of a plurality of tags assigned to existing vehicle design data for each movement step of the robot's posture given by the vehicle body's offline program; a detection unit that detects one or more tags that satisfy predetermined criteria; and an aggregation unit that aggregates the detected one or more tags.

[0008] In a first embodiment of the present invention, the processing may be painting of the vehicle body.

[0009] In the first embodiment of the present invention, the vehicle body may be a new vehicle or an existing vehicle.

[0010] In a first aspect of the present invention, the comparison in the comparison unit is a comparison between the distance between the position of each of the plurality of tags in the vehicle body design data and a threshold value, wherein the predetermined criterion may be the one or more tags whose distance is smaller than the threshold value, and which has the smallest distance.

[0011] In a first aspect of the present invention, the comparison in the comparison unit involves voxelizing the plurality of tags assigned along line data in the design data of a new vehicle body and the shape of an existing vehicle body that includes each of the plurality of tags in the design data of an existing vehicle body, and comparing the voxelized shape of the new vehicle with the voxelized shape of the existing vehicle. The predetermined criterion is that the proportion of voxelized shapes of the new vehicle and the existing vehicle whose positions match exceeds a predetermined threshold. The voxelized shapes of the new vehicle and the existing vehicle may be generated by cutting out mesh data from mesh data obtained by meshing the design data of the new vehicle and the existing vehicle, and centering each of the plurality of tags on a cube of a predetermined size.

[0012] In a first aspect of the present invention, the system may further include a database storage unit that stores a database generated by assigning tags along line data indicating the processing order in the design data of multiple existing vehicles.

[0013] In a first embodiment of the present invention, the system further comprises an input unit which may accept input to select from a database an existing vehicle that corresponds to design data to which a tag has been assigned and whose location has been compared with the tag of a new vehicle.

[0014] In a first embodiment of the present invention, the system further comprises an input unit which may accept input specifying that existing vehicles corresponding to design data to which tags have been compared with the tags and locations of new vehicles should be all existing vehicles included in the database.

[0015] In a first aspect of the present invention, an input unit may be further provided, and the input unit may receive an input for designating that all existing vehicles in a selected factory, which is selected by selecting an existing vehicle corresponding to design data with a tag to which a tag of a new vehicle is compared in position, from a plurality of existing vehicles included in a database, be used.

[0016] In a first aspect of the present invention, the plurality of tags in the design data of the existing vehicle may include robot coating information, existing vehicle information of the existing vehicle, and factory information of the existing vehicle.

[0017] A second aspect of the present invention is an information processing method in an information processing apparatus for designating the posture of a robot that processes a vehicle body, and a computer is configured to compare the position information of each of a plurality of tags assigned along line data indicating the order of processing in the design data of the vehicle body with the position information of a plurality of tags assigned to the design data of the existing vehicle for each operation step of the posture of the robot given by an offline program of the vehicle body in a comparison step, detect one or more tags that satisfy a predetermined criterion in a detection step, and summarize the detected one or more tags in a summarization step.

[0018] A third aspect of the present invention is an information processing program in an information processing apparatus for designating the posture of a robot that processes a vehicle body, and a computer is caused to realize a comparison function for comparing the position information of each of a plurality of tags assigned along line data indicating the order of processing in the design data of the vehicle body with the position information of a plurality of tags assigned to the design data of the existing vehicle for each operation step of the posture of the robot given by an offline program of the vehicle body, a detection function for detecting one or more tags that satisfy a predetermined criterion, and a summarization function for summarizing the detected one or more tags.

[0019] According to the present invention, it is possible to provide an information processing apparatus, an information processing method, and an information processing program that can suppress variations caused by humans and work processes and create an offline teach program for a robot in a short time.

Brief Description of Drawings

[0020] [Figure 1] It is a schematic diagram of an example applicable to the information processing apparatus 10 according to the present embodiment. [Figure 2] It is a block diagram showing an example of the configuration and functional units of the information processing apparatus 10 according to the present embodiment. [Figure 3] It is a diagram for comparing the design data of the body of an existing vehicle and the design data of the body of a new vehicle. [Figure 4] It is a cross-sectional view along the line data of the design data of the bodies of an existing vehicle and a new vehicle. [Figure 5] It is a graph showing the voxels that match and the voxels that do not match when the voxelized design data of a new vehicle and the voxelized design data of an existing vehicle are overlaid. [Figure 6] It is a graph showing the voxels that match and the voxels that do not match when the voxelized design data of a new vehicle and the voxelized design data of an existing vehicle are overlaid. [Figure 7] It is a graph showing the voxels that match and the voxels that do not match when the voxelized design data of a new vehicle and the voxelized design data of an existing vehicle are overlaid. [Figure 8] It is a flowchart for explaining the operation of the information processing apparatus according to the present embodiment when comparing the position information of tags of a new vehicle and the position information of tags of an existing vehicle using the first comparison unit, the second comparison unit, and the third comparison unit.

Modes for Carrying Out the Invention

[0021] Next, embodiments of the present invention will be described with reference to the drawings. In the drawings of the embodiments, identical or similar parts are denoted by the same or similar reference numerals. However, it should be noted that the drawings are schematic and the relationships with planar dimensions etc. may differ from those in reality. Therefore, specific dimensions should be determined by referring to the following explanation. Furthermore, it goes without saying that there are parts in the drawings where the relationships and ratios of dimensions differ from those of other parts.

[0022] Furthermore, the embodiments are illustrative of apparatus and methods for realizing the technical concept of the present invention, and the technical concept of the present invention does not limit the configuration, arrangement, layout, etc., of each component to those described below. The technical concept of the present invention can be modified in various ways within the technical scope defined by the claims described in the patent claims.

[0023] (Embodiment) In automobile manufacturing plants, when robots process car bodies, their movements are controlled by computer programs. These programs consist of multiple motion steps and the robot's movements between those steps. The robot's movements between steps are obtained using a robot simulator.

[0024] The multiple motion steps vary depending on the robot's task. For example, if the task is painting a car body, the multiple motion steps involve the robot spraying paint. As will be described later, when the robot sprays paint in the multiple motion steps, the spray gun used for spraying paint targets multiple points in the design data of the car body.

[0025] Each target point is tagged. The tag contains information about the robot at each target point, and by referring to the tag, information about the robot at the target point can be accessed. The robot information included in the tag includes the state of the robot at the target point. For example, if the task is painting a vehicle body, the state of the robot at the target point may include the robot's travel axis, robot axis values, robot nozzle tip direction, coating amount, etc.

[0026] A computer program for processing a new car body using a robot is first provided with the design data of the new car, which includes line data indicating the robot's painting route, and further, multiple tags are assigned along that line data. By specifying the robot's posture at each of the multiple tags and obtaining the robot's movements between the multiple tags using a robot simulator, a program is obtained to control the robot's movements in processing the new car body.

[0027] The information processing device according to this embodiment detects and aggregates robot posture information in tags from existing vehicle data to specify the posture of the robot that will perform the vehicle body processing.

[0028] Figure 1 shows a schematic diagram of an example to which the information processing device 10 according to this embodiment can be applied. As shown in Figure 1, when creating a new robot program for a painting robot 11 that sprays paint onto a target point 13 in the painting process of an automobile, the robot posture information is matched with the robot posture information from an existing robot program for an automobile, and the matched existing robot posture information is adopted as the new robot posture information.

[0029] Figure 2 shows an example of the configuration and functions of the information processing device 10 according to this embodiment. The information processing device 10 shown in Figure 2 includes a CPU 21 for executing various calculations, a ROM 22 for storing processing programs, a RAM 23 for storing data, a storage unit 24 for storing various data and calculation results, an I / O (input / output interface) 25, a display unit 26, an input unit 27, and the like.

[0030] I / O25 is an interface, buffer, etc., for communication (transmitting and receiving).

[0031] The information processing device 10 according to this embodiment may also be connected to an input keyboard, mouse, or the like.

[0032] The information processing device 10 is a so-called computer, and can be various types of electronic computing devices (computing resources), such as mobile terminals, personal computers (PCs), mainframes, workstations, and cloud computing systems.

[0033] Furthermore, the block diagram in Figure 2 shows the functional units within the CPU 21. When each functional unit of the CPU 21 is implemented by software, the CPU 21 implements these functions by executing instructions from the program, which is the software that realizes each function. Specifically, it includes a comparison unit 211, a detection unit 212, an aggregation unit 213, etc. The storage unit 24 includes a database storage unit 241, etc.

[0034] The comparison unit 211 compares the position information of each of the multiple tags attached along the line data, which is the painting instruction information within the design data of the new vehicle's body, with the position information of the multiple tags attached to the design data of the existing vehicle for each motion step of the robot's posture, which is provided by the offline program for the existing vehicle's body, and determines whether the tags of the new vehicle and the tags of the existing vehicle match.

[0035] The detection unit 212 detects one or more tags that meet predetermined criteria.

[0036] The aggregation unit 213 aggregates the one or more detected tags.

[0037] The operation of the comparison unit 211 will be explained with reference to Figures 3 to 7. Figure 3 shows, as an example, the design data 31 for the body of an existing vehicle and the design data 32 for the body of a new vehicle. The dotted areas 33 and 34 in the design data 31 and 32 are areas where the shape of each is compared by the comparison unit 211 and the position information of the tags is compared. If there are similar parts in the existing vehicle and the new vehicle, and tags exist at the same position within the similar parts, and there are no significant differences in the similar shapes, then the information processing device 10 according to this embodiment can detect identical tags within the similar parts with a high probability. Note that the circles 35 and 36 shown in Figure 3 indicate that the center of the circle is the position where the tag is attached.

[0038] The comparison unit 211 includes at least one of the first comparison unit 214, the second comparison unit 215, and the third comparison unit 216.

[0039] The first comparison unit 214 compares the distance between the position of one or more tags in the design data of the existing vehicle body and the position of one or more tags in the design data of the new vehicle body, and a threshold T. N The first comparison unit 214 compares the distance between the tag located on the existing vehicle's line data and the tag located on the new vehicle's line data for tags located on the existing vehicle's line data where multiple tags located on the new vehicle's line data are at a distance less than the threshold among the one or more tags located on the existing vehicle's line data. Similarly, the first comparison unit 214 compares the distance between the tag located on the existing vehicle's line data and the tag located on the new vehicle's line data for tags located on the new vehicle's line data where multiple tags located on the existing vehicle's line data are at a distance less than the threshold among the one or more tags located on the new vehicle's line data. The first comparison unit 214 compares the distance between the tag located on the existing vehicle's line data and this tag when the distance between this tag is below the threshold T. NThe smaller and closest tag located on the line data of a new vehicle is determined to be the matching tag.

[0040] The first comparison unit 214 selects one existing vehicle from the existing vehicle models and uses it as the existing vehicle for comparison with the new vehicle. The tag of the existing vehicle selected by the first comparison unit 214 for comparison with the new vehicle is designated as the existing vehicle tag. As will be described later, the tag assigned to the existing vehicle selected by the third comparison unit 216 for comparison with the new vehicle is also designated as the existing vehicle tag.

[0041] The operation of the first comparison unit 214 will be explained with reference to Figure 4. Figure 4 shows, as an example, a cross-sectional view along the line data of the design data of the body of an existing vehicle and a new vehicle. Parts of the line data 41 of the existing vehicle and the line data 42 of the new vehicle that have similar shapes are shown at predetermined intervals.

[0042] The first comparison unit 214 arranges parts of the existing vehicle's line data 41 and the new vehicle's line data 42 that have similar shapes at predetermined intervals, and sets a radius T centered on the existing vehicle tag located on the existing vehicle's line data 41. N The system searches for a tag located on the line data 42 of a new vehicle within that range.

[0043] In the example shown in Figure 4, the radius T is centered on the existing vehicle tag 411 on the existing vehicle line data 41. N Within this range, there is a tag 421 located on the new vehicle line data 42. Radius T is centered on existing vehicle tags 412, 413, and 415 on the existing vehicle line data 41. N Within this range, there are no tags located on the new vehicle line data 42. Radius T centered on tags 422 and 423 located on the new vehicle line data 42. N Within this range, there are no existing vehicle tags on the existing vehicle line data 41. Radius T centered on the existing vehicle tag 414 on the existing vehicle line data 41. NWithin the range, tags 424 and 425 are located on the line data 42 of the new vehicle, and the distance between the existing vehicle tag 414 and tag 424 is smaller than the distance between the existing vehicle tag 414 and tag 425. Therefore, the first comparison unit 214 may determine that the distance between the existing vehicle tag 411 and tag 421 is smaller than the threshold value T N Further, the first comparison unit 214 determines that the distances between the existing vehicle tag 414 and each of tag 424 and tag 425 are smaller than the threshold value T N Moreover, the first comparison unit 214 compares the distance between the existing vehicle tag 414 and tag 424 with the distance between the existing vehicle tag 414 and tag 425, and determines that the distance between the existing vehicle tag 414 and tag 424 is the smallest.

[0044] The detection unit 212 detects a tag on the line data of the new vehicle that the first comparison unit 214 determines to match an existing vehicle tag located on the line data of the existing vehicle, where the distance between this tag and the existing vehicle tag is smaller than the threshold value T N and is the closest.

[0045] The second comparison unit 215 voxelizes the shape of the new vehicle including each of a plurality of tags in the design data of the new vehicle of the vehicle body and the shape of the existing vehicle including each of a plurality of tags in the design data of the existing vehicle of the vehicle body, and compares the voxelized shape of the new vehicle with the voxelized shape of the existing vehicle. Here, the predetermined criterion is that the ratio of the ones with matching positions among the voxelized shapes of the new vehicle and the existing vehicle respectively exceeds a predetermined threshold value. The voxelized shapes of the new vehicle and the existing vehicle are generated by cutting out mesh data included in a cube of a predetermined size centered on each of the plurality of tags from the mesh data obtained by meshing the design data of the new vehicle and the existing vehicle respectively.

[0046] The second comparison unit 215 uses an existing vehicle selected by specifying all vehicle types or a factory as the existing vehicle to be compared with the new vehicle. The tag assigned to the existing vehicle selected by the second comparison unit 215 for comparison with the new vehicle is used as the database tag.

[0047] The operation of the second comparison unit 215 will be explained with reference to Figures 5 to 7. As an example, Figures 5 to 7 are graphs that show matching voxels and mismatched voxels by overlaying the voxelized design data of a new vehicle with the voxelized design data of an existing vehicle.

[0048] The procedure for voxelizing vehicle body design data is as follows: First, the second comparison unit 215 cuts out 3D mesh data from the 3D mesh data of vehicle body design data to which tags for existing vehicles and new vehicles are assigned, centering on the tags for existing vehicles and new vehicles. The cutting range is, for example, 100 mm centered on the tag. 3 Create a cube, and then cut out the resulting cube.

[0049] Next, the second comparison unit 215 voxels the extracted 3D mesh data. Voxelization, in this context, means dividing the cube containing the extracted 3D mesh data into multiple cubes, and then considering the multiple cubes containing the 3D mesh data as voxelized 3D mesh data.

[0050] The second comparison unit 215 compares the 3D mesh data of the design data of the new vehicle, which has been voxed around the tags on the line data of the design data, with the 3D mesh data of the existing vehicle, which corresponds to the parts of the vehicle body in the voxed 3D mesh data of the new vehicle. It classifies the voxels into matching voxels and mismatched voxels, and compares the ratio of matching voxels to the total number of voxels with a threshold. If the ratio of matching voxels to the total number of voxels is greater than the threshold, the second comparison unit 215 determines that the database tags included in the 3D mesh data of the existing vehicle, which corresponds to the parts of the vehicle body in the voxed 3D mesh data of the new vehicle, match the tags of the new vehicle.

[0051] The detection unit 212 detects, based on the second comparison unit 215, that database tags included in the voxelized 3D mesh data of a vehicle body part corresponding to the voxelized 3D mesh data of the new vehicle, where the proportion of matching voxels relative to all voxels is greater than a threshold, match the tags of the new vehicle.

[0052] The graph of voxelized 3D mesh data 51 shown in Figure 5 represents the voxelized 3D mesh data of new and existing vehicles where the positions match, represented by white voxels 52, and the 3D mesh data of existing vehicles where the positions do not match, represented by light gray voxels 53.

[0053] In Figure 6, the graph of voxelized 3D mesh data 61 shows that the voxelized 3D mesh data for new and existing vehicles that match in position are represented by white voxels 62, while the 3D mesh data for existing vehicles that do not match in position are represented by light gray voxels 63. In Figure 6, the 3D mesh data for new vehicles that do not match in position are represented by dark gray voxels 64.

[0054] As shown in Figure 7, the graph of voxelized 3D mesh data 71 represents the voxelized 3D mesh data of new and existing vehicles with matching positions as white voxels 72, the 3D mesh data of existing vehicles with mismatched positions as light gray voxels 73, and the 3D mesh data of new vehicles with mismatched positions as dark gray voxels 74.

[0055] Note that the existing vehicles shown in Figures 5, 6, and 7 represent all vehicle types, or existing vehicles selected by specifying a factory.

[0056] The 3D mesh data shown in Figures 5 and 6 has a high proportion of matching voxels compared to the total number of voxels, and the second comparison unit 215 determines that the tags of new and existing vehicles included in the 3D mesh data shown in Figures 5 and 6 are matching. The 3D mesh data shown in Figure 7 has a low proportion of matching voxels compared to the total number of voxels, and the second comparison unit 215 determines that the tags of new and existing vehicles included in the 3D mesh data shown in Figure 7 are not matching.

[0057] The extracted 3D mesh data is divided, for example, so that a cube with a 5mm grid becomes one voxel. The larger the size of the 3D mesh data extracted by the second comparison unit 215 around the tag, and the smaller the size of one voxel when the extracted mesh data is voxed, the greater the processing time when the vehicle body design data is databased and the processing time when searching for matching tags. The size of the 3D mesh data to be extracted and the size of one voxel when the extracted mesh data is voxed are determined taking these processing times into consideration. Note that the voxel sizes when voxing new vehicles and existing vehicles to be compared must match.

[0058] Furthermore, reducing the size of each voxel when voxelizing the extracted mesh data allows for a more accurate representation of the vehicle's shape. However, a smaller voxel size reduces the likelihood of the positions of the voxelized 3D mesh data for new and existing vehicles matching. A larger voxel size increases the likelihood of the positions of the voxelized 3D mesh data for new and existing vehicles matching, but increases the error in the vehicle's shape when the tags for new and existing vehicles in the 3D mesh data are deemed to match. Therefore, the size of each voxel is determined by the acceptable degree of error in the robot's posture, position information, etc., when processing the vehicle bodies of new and existing vehicles using a robot.

[0059] The third comparison unit 216, like the first comparison unit 214, compares the distance between the position of each of the multiple tags in the design data of the new vehicle body and the position of each of the multiple existing vehicle tags in the design data of the existing vehicle body, and a threshold value. However, the threshold value used by the third comparison unit 216 is a larger value than the threshold value used by the first comparison unit 214.

[0060] The aggregation unit 213 determines whether the tags of new vehicles match the existing vehicle tags of existing vehicles, based on the first comparison unit 214, the second comparison unit 215, and the third comparison unit 216, and aggregates the one or more tags detected by the detection unit 212.

[0061] In the procedure described above, the comparison unit 211 compares a given existing vehicle with a new vehicle. Alternatively, the comparison unit 211 may compare a conventional vehicle selected from among several existing vehicles for comparison with the new vehicle.

[0062] The database storage unit 241 may store a database generated by assigning tags according to line data that indicates the processing order in the design data of multiple existing vehicles. Multiple existing vehicles may be classified according to the information contained in the tags and stored as a database.

[0063] The information included in the tag may include, if the processing to which the information processing device 10 according to this embodiment is applied is painting of a vehicle body, the application information of the application robot, the existing vehicle information of the existing vehicle, and the factory information of the existing vehicle.

[0064] The user may, via the input unit 27, select an existing vehicle from among several existing vehicles listed in the database to be compared with the comparison unit 211, and input information about the selected existing vehicle. The input unit 27 receives the input information about the selected existing vehicle and transmits it to the comparison unit 211. The comparison unit 211 compares the tags of the received existing vehicle and the new vehicle.

[0065] When the second comparison unit 215 performs a comparison, the user may send information via the input unit 27 specifying that the existing vehicles to be considered are all existing vehicles included in the database that correspond to the design data with tags whose positions have been compared with the tags of the new vehicles. The input unit 27 receives the input of information about the selected existing vehicles for the comparison unit 211 and sends it to the comparison unit 211. The comparison unit 211 compares the tags of all existing vehicles included in the database with those of the new vehicle.

[0066] When the second comparison unit 215 performs a comparison, the user may transmit information via the input unit 27 specifying that the existing vehicles to be considered are all existing vehicles at the selected factory, by selecting each factory that applied paint to multiple existing vehicles included in the database. The input unit 27 receives the input information regarding the selected existing vehicles and transmits it to the comparison unit 211. The comparison unit 211 compares the tags of all existing vehicles and the new vehicle at the selected factory.

[0067] The comparison unit 211 may use the first comparison unit 214, the second comparison unit 215, and the third comparison unit 216 to compare the location information of the tag of the new vehicle with the location information of the tag of the existing vehicle, and determine whether the tag of the new vehicle and the tag of the existing vehicle match. Threshold T when using the first comparison unit 214 and the third comparison unit 216 N By adjusting the size of the 3D mesh data to be cropped when using the second comparison unit 215, the size of one voxel when converting the cropped mesh data into voxels, and the method for selecting existing vehicles, it is possible to reduce the number of tags of new vehicles for which the comparison unit 211 determines that there are no existing vehicle tags that match the tags of the new vehicles.

[0068] The comparison unit 211 uses the first comparison unit 214, the second comparison unit 215, and the third comparison unit 216 to compare the location information of the tag of a new vehicle with the location information of the tag of an existing vehicle. An example of the procedure for determining whether the tag of the new vehicle and the tag of an existing vehicle match is shown in the flowchart of Figure 8. The operation described in the flowchart of Figure 8 uses the threshold T when using the first comparison unit 214 and the third comparison unit 216. N This is an example of how the comparison unit 211 operates, which adjusts the order in which the first comparison unit 214, the second comparison unit 215, and the third comparison unit 216 are used, the method for selecting existing vehicles, etc., and reduces the number of tags of new vehicles for which it is determined that there are no existing vehicle tags that match the tags of the new vehicles.

[0069] In step S801, matching is initiated by the information processing device 10 according to this embodiment.

[0070] In step S802, the input unit 27 receives input of information about an existing vehicle selected by the user from among several existing vehicles listed in the database, to be compared with the comparison unit 211, and transmits it to the comparison unit 211.

[0071] In step S803, the first comparison unit 214 compares the distance between the position of each of the one or more existing vehicle tags of the selected existing vehicle and the position of each of the one or more tags on the body of the new vehicle and a threshold T N The distance between the tags of new cars and the tags of new cars is compared, and the distance between them is threshold T. N The detection unit 212 determines that the smaller and closest existing vehicle tag matches the existing vehicle tag of the existing vehicle. The detection unit 212 detects the tag of the new vehicle that the first comparison unit 214 determined to match the existing vehicle tag of the existing vehicle and transmits it to the aggregation unit 213. The process performed in step S803 is denoted as L1.

[0072] In step S804, the input unit 27 receives input to determine the selection method when selecting the comparison target for the new vehicle from the database when comparing using the second comparison unit 215 and the third comparison unit 216. In step S804, the input unit 27 receives input to determine whether or not to use all vehicle models registered in the database as the comparison target for the new vehicle when comparing using the second comparison unit 215 and the third comparison unit 216.

[0073] In step S804, if the comparison target with the new vehicle when using the second comparison unit 215 and the third comparison unit 216 is not all vehicle models registered in the database, in step S805, the input unit 27 accepts input information specifying the factory that performed the processing, from among all vehicle models registered in the database, when comparing with the new vehicle using the second comparison unit 215 and the third comparison unit 216.

[0074] In step S806, the second comparison unit 215 compares the voxelized shape of the 3D mesh data of the new vehicle with the voxelized shape of the 3D mesh data of the existing vehicle. If the proportion of matching voxels to the total number of voxels is greater than a threshold, it determines that the tags included in the voxelized 3D mesh data of the existing vehicle corresponding to the vehicle body part of the new vehicle's voxelized 3D mesh data match the tags of the new vehicle. Here, the second comparison unit 215 performs a comparison with the tags of the new vehicle that were not detected in step L1. The step executed in step S806 is referred to as L2.

[0075] In step S807, the third comparison unit 216 compares the distance between the position of each of the multiple tags in the design data of the new vehicle body and the position of each of the multiple existing vehicle tags in the design data of the existing vehicle body, and a threshold T. N Compare with the threshold T. N ' is the threshold T NThis value is greater than [value]. Here, the third comparison unit 216 performs a comparison with the tags of new cars that were not detected in step L1. The step performed in step S807 is denoted as L3.

[0076] In step S808, the detection unit 212 detects the tags of new vehicles that were determined to match the tags of existing vehicles in process L2. For new vehicle tags that were determined not to match the tags of existing vehicles in process L2, the detection unit 212 detects the tags of new vehicles that were determined not to match the tags of existing vehicles in process L2 and that were determined to match the tags of existing vehicles in process L3. The detection unit 212 transmits the detected new vehicle tags to the aggregation unit 213.

[0077] In step S809, the detection unit 212 transmits tags for new vehicles that were not detected in any of processes L1, L2, or L3 to the aggregation unit 213 as undetected tags.

[0078] In step S810, the aggregation unit 213 aggregates the tags transmitted from the detection unit 212.

[0079] In step S811, the aggregation unit 213 performs a conversion on the aggregated tags for use in the robot simulator.

[0080] In step S812, the robot simulator is executed.

[0081] This invention makes it possible to provide an information processing device, an information processing method, and an information processing program that can reduce human error and work processes, and enable the creation of offline teaching programs for robots in a short amount of time.

[0082] As stated above, the present invention naturally includes various embodiments and the like that are not described herein. Therefore, the technical scope of the present invention is determined solely by the inventive features relating to the claims that are reasonable based on the above description. [Explanation of symbols]

[0083] 10 Information Processing Devices 11 Painting robots 12 Automobiles 13 Target points 21 CPU 22 ROM 23 RAM 24 Memory section 25 I / O (Input / Output Interface) 26 Display section 27 Input section 211 Comparison Section 212 Detection Unit 213 Aggregation Department 214 First Comparative Section 215 Second Comparative Section 216 Third Comparative Section 241 Database Storage Unit 31, 32 Design data 33, 34 parts 35, 36 yen 41, 42 Line data 411, 412, 413, 414, 415, 421, 422, 423, 424, 425 tags 51, 61, 71 Voxelized 3D mesh data 52, 62, 72 White voxel 53, 63, 73 Light gray voxel 64, 74 Dark gray voxel

Claims

1. An information processing device for specifying the posture of a robot that processes a vehicle body, The aforementioned information processing device is A comparison unit compares the position information of each of the multiple tags assigned along the line data indicating the order of processing in the vehicle body design data with the position information of multiple tags assigned to the existing vehicle design data for each motion step of the robot's posture given by the vehicle body's offline program. A detection unit that detects one or more tags that meet predetermined criteria, The aggregation unit aggregates the one or more tags that have been detected. An information processing device characterized by comprising:

2. The information processing apparatus according to claim 1, characterized in that the processing is the painting of the vehicle body.

3. The information processing apparatus according to claim 1, characterized in that the vehicle body is a new vehicle or an existing vehicle.

4. The comparison in the comparison unit is a comparison between the position of each of the multiple tags in the vehicle body design data and the distance between the position of each of the multiple tags in the vehicle body design data and a threshold value. The predetermined criterion is that among one or more tags whose distance is less than the threshold, the one with the smallest distance. The information processing apparatus according to feature 1.

5. The comparison in the comparison unit involves voxelizing the multiple tags assigned to the design data of the new vehicle body along the line data, and the shape of the existing vehicle body, which includes each of the multiple tags in the design data of the existing vehicle body, and then comparing the voxelized shape of the new vehicle with the voxelized shape of the existing vehicle. The aforementioned predetermined criterion is that the proportion of voxelized shapes of the new vehicle and the existing vehicle whose positions match exceeds a predetermined threshold. The voxelized shapes of the new vehicle and the existing vehicle are generated by extracting mesh data from the mesh data obtained by meshing the design data of the new vehicle and the existing vehicle, and then cutting out the mesh data contained in a cube of a predetermined size, centered on each of the multiple tags. The information processing apparatus according to feature 1.

6. The information processing apparatus according to claim 1, further comprising a database storage unit that stores a database generated by assigning tags along line data indicating the order of processing in the design data of multiple existing vehicles.

7. It further includes an input section, The information processing apparatus according to claim 6, characterized in that the input unit receives input to select from the database an existing vehicle to which the tag has been assigned, and whose position has been compared with the tag of a new vehicle.

8. It further includes an input section, The information processing apparatus according to claim 6, characterized in that the input unit receives an input specifying that the existing vehicle corresponding to the design data to which the tag has been assigned, whose position has been compared with the tag of the new vehicle, should be all existing vehicles included in the database.

9. It further includes an input section, The information processing apparatus according to claim 6, characterized in that the input unit receives input specifying that the existing vehicles corresponding to the design data to which the tag has been assigned, whose position has been compared with the tag of the new vehicle, should be all existing vehicles at the selected factory, by selecting each factory that applied paint to the plurality of existing vehicles included in the database.

10. The information processing device according to claim 1, characterized in that the plurality of tags in the design data of the existing vehicle include the robot coating information, the existing vehicle information of the existing vehicle, and the factory information of the existing vehicle.

11. An information processing method in an information processing device for specifying the posture of a robot that processes a vehicle body, Computers A comparison step that compares the position information of each of the multiple tags assigned along the line data indicating the order of processing in the vehicle body design data with the position information of multiple tags assigned to the existing vehicle design data for each motion step of the robot's posture given by the vehicle body's offline program, A detection step that detects one or more tags that meet predetermined criteria, The aggregation step of aggregating the one or more tags detected: An information processing method characterized by performing the following.

12. An information processing program for an information processing device that specifies the posture of a robot that processes a vehicle body, On the computer, A comparison function that compares the position information of each of the multiple tags assigned along the line data indicating the order of processing in the vehicle body design data with the position information of multiple tags assigned to the existing vehicle design data for each motion step of the robot's posture given by the vehicle body's offline program, A detection function that detects one or more tags that meet predetermined criteria, The aggregation function aggregates the one or more tags detected. An information processing program characterized by achieving this.

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