Model generation device, model generation method, program

Abstract

A model generation device 100 according to the present disclosure is provided with: an acquisition means 121 that acquires first design information of a designed object; a first generation means 122 that generates occupancy information indicating whether or not the designed object occupies each location in a three-dimensional space, on the basis of the first design information; and a second generation means 123 that generates a model shape for the designed object on the basis of the occupancy information, said model shape consisting of a combination of a plurality of predetermined structures.

Description

Model generation device, model generation method, and program

[0001] The present disclosure relates to a model generation device, a model generation method, and a program.

[0002] Robots are being introduced in various settings, such as manufacturing sites, and before the robots are introduced, their operation is checked by simulation. Simulating the robot's operation requires design information for the robot. However, when the amount of design information data, such as that from CAD (Computer Aided Design), is enormous, the processing speed of the simulation decreases. Therefore, there is a demand to reduce the amount of design information data.

[0003] Here, Patent Document 1 describes a technology for classifying a three-dimensional object into a known shape from point cloud data consisting of three-dimensional coordinates of the target three-dimensional object. Specifically, Patent Document 1 calculates the center of gravity of the point cloud data of the three-dimensional object, performs principal component analysis with the center of gravity as the origin, and further calculates Fourier coefficients from the coordinates, and classifies the target three-dimensional object into one known shape from these values.

[0004] Japanese Patent Application Laid-Open No. 2002-99556

[0005] However, the technology described in Patent Document 1 only classifies three-dimensional objects into one known shape, which may result in significant differences in shape between the objects. Therefore, even if the technology described in Patent Document 1 is used to reduce the amount of design information data, the accuracy of the design information may be reduced. As a result, the accuracy of the simulation of the design object may be reduced, making it difficult to improve the efficiency of the simulation.

[0006] Therefore, an object of the present disclosure is to solve the above-mentioned problem of being unable to improve the efficiency of simulation of a design.

[0007] A model generation device according to one embodiment of the present disclosure includes: an acquisition means for acquiring first design information of a design; a first generation means for generating occupancy information representing whether or not each position in three-dimensional space is occupied by the design based on the first design information; and a second generation means for generating a model shape of the design consisting of a combination of a plurality of predetermined structures based on the occupancy information.

[0008] Furthermore, a model generation method according to one embodiment of the present disclosure is configured to: acquire first design information of a design; generate occupancy information indicating whether or not each position in three-dimensional space is occupied by the design based on the first design information; and generate a model shape of the design consisting of a combination of a plurality of predetermined structures based on the occupancy information.

[0009] Furthermore, a program according to one embodiment of the present disclosure is configured to cause a computer to execute the following processes: acquire first design information of a design object; generate occupancy information indicating whether or not each position in three-dimensional space is occupied by the design object based on the first design information; and generate a model shape of the design object consisting of a combination of multiple predetermined structures based on the occupancy information.

[0010] With the above-described configuration, the present disclosure can improve the efficiency of simulation of a design.

[0011] FIG. 1 is a block diagram showing the configuration of a model generation device according to a first embodiment of the present disclosure. FIG. 2 is a diagram showing the processing state of the model generation device disclosed in FIG. 1. FIG. 3 is a diagram showing the processing state of the model generation device disclosed in FIG. 1. FIG. 4 is a diagram showing the processing state of the model generation device disclosed in FIG. 1. FIG. 5 is a flowchart showing the operation of the model generation device disclosed in FIG. 1. FIG. 6 is a block diagram showing the hardware configuration of a model generation device according to a second embodiment of the present disclosure. FIG. 7 is a block diagram showing the configuration of a model generation device according to a second embodiment of the present disclosure.

[0012] <First Embodiment> A first embodiment of the present disclosure will be described with reference to Fig. 1 to Fig. 6. Fig. 1 is a diagram for explaining the configuration of a model generation device, and Fig. 2 to Fig. 6 are diagrams for explaining the processing operation of the model generation device.

[0013] [Configuration] The model generation device in this embodiment is a device for generating a model of a system to be used in a simulation to confirm the operation of the target system. In particular, the model generation device generates a model from design information, such as CAD, of the target system. In this case, the target system is a design object designed using CAD or the like, such as a robot to be introduced into a manufacturing site. In this case, simulating a robot means using a model generated from the robot's design information to confirm whether the robot performs the desired operation and whether unintended collisions occur. However, the system for which the model is generated is not limited to a robot, and any system may be used.

[0014] The model generation device 10 is composed of one or more information processing devices each including a calculation device and a storage device. As shown in FIG. 1 , the model generation device 10 includes an acquisition unit 11, an occupation information generation unit 12, a conversion unit 13, and an output unit 14. The functions of the acquisition unit 11, occupation information generation unit 12, conversion unit 13, and output unit 14 can be realized by the calculation device executing a program for realizing each function stored in the storage device. The model generation device 10 also includes a design information storage unit 16, a basic structure information storage unit 17, and a threshold storage unit 18. The design information storage unit 16, the basic structure information storage unit 17, and the threshold storage unit 18 are each composed of a storage device. Furthermore, a design information storage device 20 is connected to the model generation device 10. Each component will be described in detail below.

[0015] First, the design information storage device 20 stores design information such as CAD data of the robot, which is the design object. The design information includes shape information and motion information of the robot in three-dimensional space. As an example, the shape information represents the shape of each component of the robot in three-dimensional space, and the motion information represents the motion trajectory and range of motion of each component of the robot in three-dimensional space. However, the design information is not limited to the information described above.

[0016] The acquisition unit 11 (acquisition means) acquires design information (first design information) such as CAD data of a robot, which is a design object, from the design information storage device 20 and stores it in the design information storage unit 16. At this time, the acquired design information includes shape information and operation information of the robot in three-dimensional space, as described above. For example, the acquisition unit 11 acquires design information of a robot having various configurations, such as that shown by reference symbol D1 in FIG. 2.

[0017] The occupancy information generating unit 12 (first generating means) generates occupancy information indicating whether or not the robot configuration occupies each position in three-dimensional space, based on the acquired and stored robot design information. For example, the occupancy information generating unit 12 generates occupancy information as point cloud data in which a point is assigned to each position in three-dimensional space where the robot exists. In this way, the occupancy information generating unit 12 generates occupancy information consisting of point cloud data such as that shown by reference symbol D2 for the robot design information such as that shown by reference symbol D1 in FIG. 2.

[0018] Here, the details of the occupancy information generation process performed by the occupancy information generator 12 will be described with reference to FIG. 3 . The occupancy information generator 12 first divides the three-dimensional space into unit spaces at a predetermined density. Then, the occupancy information generator 12 assigns information indicating whether or not a robot configuration exists at the position of each divided unit space. For example, the occupancy information generator 12 divides the three-dimensional space in which the robot design information exists, as indicated by reference numeral d1 in FIG. 3 , into multiple unit spaces at a predetermined density, as indicated by the dotted rectangles indicated by reference numeral d2. As an example, the unit spaces are divided into cubes every 3 cm or 5 cm. Then, if a robot configuration exists at the position of each unit space, the occupancy information generator 12 assigns a point to the unit space indicating that the unit space is occupied. In this way, the occupancy information generator 12 generates occupancy information consisting of point cloud data, as indicated by reference numeral d2, for the robot design information, as indicated by reference numeral d1 in FIG. 3 . Note that FIG. 3 illustrates only a portion of the robot's structure.

[0019] The conversion unit 13 (second generation means) generates a robot model shape consisting of a combination of multiple basic structures (predetermined structures) based on the occupancy information. Specifically, the conversion unit 13 converts the occupancy information consisting of point cloud data into a model shape consisting of multiple basic structures by applying a basic structure that encompasses a unit space to which points indicating the occupied positions of the robot structure are assigned to the occupancy information consisting of point cloud data. In this way, the conversion unit 13 converts the occupancy information consisting of point cloud data such as that shown by reference symbol D2 in FIG. 2 into a model shape consisting of a combination of multiple rectangular parallelepipeds such as that shown by reference symbol D3.

[0020] The basic structures described above are predetermined structures, such as a rectangular parallelepiped, a pillar, a cone, or a sphere. The shapes of the basic structures are stored in advance in the basic structure information storage unit 17. The size (length, height, radius, etc.), position (reference point position), and attitude (angle) of the basic structures applied as the model shape are set according to the size and orientation of the included point cloud data.

[0021] Here, details of the conversion process by the conversion unit 13 will be described with reference to FIG. 3 . The conversion unit 13 applies the shape of a basic structure, such as a rectangular parallelepiped, to a collection area of ​​unit spaces to which points are assigned, for occupancy information that is point cloud data such as that shown by reference numeral d2 in FIG. 3 . At this time, the basic structure is applied to the points of the point cloud data without excess or deficiency. For example, as shown by reference numeral d3 in FIG. 3 , large rectangular parallelepipeds are applied to the left and right sides of the point cloud data, and a small rectangular parallelepiped is applied to the center. In this way, the conversion unit 13 converts occupancy information consisting of point cloud data such as that shown by reference numeral d2 in FIG. 3 into a model shape consisting of a combination of multiple rectangular parallelepipeds, as shown by reference numeral d3.

[0022] The conversion unit 13 also has a function of generating a model shape by modifying occupancy information, which is point cloud data, based on shape information and operation information included in the robot design information. Specifically, the conversion unit 13 determines whether a unit space in the point cloud data that is unoccupied by the robot's structure is a space into which a specific part of the robot cannot move, based on the shape information and operation information. The conversion unit 13 then treats the unoccupied space into which a specific part of the robot cannot move as occupied space. That is, the conversion unit 13 modifies the occupancy information by assigning a point to the unoccupied unit space indicating that the unit space is occupied. For example, if the shape of a collection area of ​​unoccupied spaces is smaller than the shape of the minimum configuration of the robot, the conversion unit 13 treats the unoccupied space as occupied space. Furthermore, if the operation information does not specify that other parts of the robot will move to the position of the collection area of ​​unoccupied spaces, the conversion unit 13 treats the unoccupied space as occupied space. Then, the conversion unit 13 generates a model shape of the robot made up of a combination of a plurality of basic structures in the same manner as described above, from the occupancy information corrected as described above.

[0023] Here, the details of the above-mentioned occupancy information correction process by the conversion unit 13 will be described with reference to FIG. 3. When a collection area of ​​unoccupied unit spaces to which no points are assigned is an area where other parts cannot move, the conversion unit 13 treats the area as an occupied space by assigning a point (gray dot) indicating occupation to each unit space in the area, as shown by reference numeral d2' in FIG. 3. Then, the conversion unit 13 applies a basic structure to the corrected occupancy information shown by reference numeral d2' in FIG. 3, thereby converting it into a model shape consisting of a single rectangular parallelepiped, as shown by reference numeral d3' in FIG. 3. Note that FIG. 3 illustrates only a portion of the robot's structure.

[0024] The output unit 14 generates and outputs model data representing the model shape converted by the conversion unit 13. Specifically, the output unit 14 acquires data specifying the shape in three-dimensional space of each basic structure included in the model shape, such as data in three-dimensional space such as the type of shape (rectangular parallelepiped, cylinder, etc.), size (rectangle: W (width) D (depth) H (height); cylinder: R (radius) H (height)), reference point position (XYZ), and attitude (RPY (roll angle, pitch angle, yaw angle)) as shown in FIG. 5 , and generates and outputs the data as model data. Note that the attitude shown in FIG. 5 does not necessarily have to be included in the model data. In this case, the attitude of each basic structure is treated as being in a preset direction.

[0025] The output unit 14 may also check whether the generated model data satisfies a predetermined condition. For example, the output unit 14 checks whether the data capacity of the model data is less than a data capacity threshold stored in the threshold storage unit 18, and whether the number of basic structures constituting the model data is less than a structure number threshold stored in the threshold storage unit 18. Here, the data capacity threshold and the structure threshold are upper limits of the data capacity and the number of basic structures that are expected to reduce the load on the simulation device and prevent a decrease in processing speed when performing a simulation using the model data, and are set in advance based on experience, a calculation formula, simulation, or the like and stored in the threshold storage unit 18. If at least one of the data capacity and the number of basic structures of the model data is equal to or greater than the threshold, the output unit 14 notifies the occupancy information generation unit 12 and the conversion unit 13 of this fact and regenerates the model shape.

[0026] Here, the functions of the occupancy information generating unit 12 and the converting unit 13 when regenerating a model shape will be described.

[0027] When the occupancy information generation unit 12 receives notification of the regeneration of the model shape, it changes the density of the unit spaces that divide the three-dimensional space, i.e., changes the size of the unit spaces, and re-divides the three-dimensional space into unit spaces. At this time, as described above, the model shape is regenerated because the data volume of the model data representing the previously generated model shape is large or the number of basic structures is large, so the data volume and the number of basic structures are reduced. Therefore, the occupancy information generation unit 12 decreases the density, which is the degree of density of the unit spaces. In other words, the occupancy information generation unit 12 significantly changes the size of the unit spaces to divide the three-dimensional space into unit spaces. As an example, the division of unit spaces consisting of cubes every 5 cm is changed to division into unit spaces every 10 cm.

[0028] Here, the above-mentioned process of changing the density of the unit space will be described with reference to FIG. 4. In FIG. 4, reference symbol d2 indicates the state before the change in density of the unit space, and reference symbol d2" indicates the state after the change in density of the unit space. As shown in this figure, the occupancy information generation unit 12 divides the three-dimensional space into unit spaces by significantly changing the size of the unit spaces. Then, the occupancy information generation unit 12 checks whether or not the robot configuration occupies each unit space divided by significantly changing the size, and if the robot configuration exists at the position of each unit space, it assigns a point to that unit space indicating that the unit space is occupied. In this way, the occupancy information generation unit 12 generates occupancy information consisting of point cloud data after the density of the unit space has been changed, as indicated by reference symbol d2", for the robot design information as indicated by reference symbol d1 in FIG. 3.

[0029] The occupancy information generation unit 12 may aggregate unit spaces from point cloud data before the density change such as that shown by symbol d2 in Figure 4, and generate occupancy information consisting of point cloud data after the density change such as that shown by symbol d2". As an example, the occupancy information generation unit 12 may aggregate four adjacent unit spaces shown by symbol d2 in Figure 4 into one unit space, and set the occupation status of the unit space after the aggregation based on statistical values ​​such as the average value of the occupation status of the unit spaces before the aggregation, thereby generating corrected point cloud data such as that shown by symbol d2" in Figure 4.

[0030] The conversion unit 13 then converts the corrected occupancy information into a model shape consisting of a combination of multiple basic structures, as described above, and generates model data. For example, for a part of the structure of the robot shown in FIG. 4, the pre-correction occupancy information shown by reference symbol d2 is converted into three basic structures shown by reference symbol d3, but the corrected occupancy information shown by reference symbol d2" is converted into one basic structure shown by reference symbol d3". This reduces the amount of data in the model data and the number of basic structures.

[0031] Note that the regeneration of the model shape may be performed so that the data capacity and the number of basic structures constituting the model data approach the respective thresholds when the data capacity and the number of basic structures constituting the model data are less than the respective thresholds stored in the threshold storage unit 18. In this case, the occupancy information generation unit 12 divides the three-dimensional space into unit spaces by increasing the density, which is the degree of density of the unit spaces, that is, by reducing the size of the unit spaces. However, even in this case, the data capacity of the model data and the number of basic structures constituting the model data remain less than the respective thresholds.

[0032] Here, in order to reduce the amount of data in the model data and the number of basic structures, the occupancy information generation unit 12 may identify the motion range of the robot configuration based on the motion information included in the robot design information, and generate the above-mentioned occupancy information only for the robot configuration located within this motion range. In this case, the conversion unit 13 may convert the above-mentioned occupancy information into a model shape only for the robot configuration located within the motion range, and generate model data. In this case, other robot configurations do not need to be included in the model data.

[0033] [Operation] Next, the operation of the above-described model generating device 10 will be described mainly with reference to the flowchart of FIG.

[0034] First, the model generating device 10 acquires design information, such as CAD data of a robot, which is a design object, from the design information storage device 20 (step S1), and stores the design information in the design information storage unit 16. At this time, as described above, the acquired design information includes shape information and operation information of the robot in three-dimensional space. For example, the model generating device 10 acquires design information of a robot such as that shown by reference symbol D1 in FIG. 2.

[0035] Next, the model generating device 10 sets the density of unit spaces that divide the three-dimensional space (step S2), and generates occupancy information that indicates whether or not each position in the unit space of the three-dimensional space is occupied by the robot configuration, based on the robot design information (step S3). For example, the model generating device 10 divides the three-dimensional space into unit spaces using the density set as an initial value, and generates occupancy information as point cloud data in which each unit space occupied by the robot configuration is assigned a point. For example, for robot design information such as that shown by reference symbol D1 in FIG. 2, the model generating device 10 generates occupancy information consisting of point cloud data such as that shown by reference symbol D2.

[0036] Next, the model generating device 10 performs a process of correcting unoccupied spaces regarding the occupancy information (step S4). Specifically, if a collection area of ​​unoccupied unit spaces to which no points are assigned in the generated point cloud data is an area to which other parts cannot move, the model generating device 10 assigns a point indicating occupation to each unit space in the area and treats it as occupied space. Note that the model generating device 10 does not necessarily have to perform the process of correcting unoccupied spaces in step S4.

[0037] Next, the model generating device 10 converts the occupancy information consisting of the point cloud data into a robot model shape consisting of a combination of multiple basic structures (step S5). For example, the model generating device 10 converts the occupancy information consisting of the point cloud data as shown in reference symbol D2 in Fig. 2 into a model shape consisting of a combination of multiple rectangular parallelepipeds as shown in reference symbol D3.

[0038] Next, the model generating device 10 generates model data representing the converted model shape (step S6). Specifically, the model generating device 10 acquires data specifying the shape in three-dimensional space of each basic structure included in the model shape, such as data as shown in Fig. 5, and generates the model data.

[0039] At this time, the model generating device 10 checks whether the generated model data satisfies a preset condition. For example, the model generating device 10 checks whether the data capacity of the model data is less than the data capacity threshold stored in the threshold storage unit 18, or whether the number of basic structures constituting the model data is less than the structure number threshold stored in the threshold storage unit 18 (step S7). If at least one of the data capacity of the model data and the number of basic structures is equal to or greater than the threshold (No in step S7), the model generating device 10 regenerates the model shape (returns to step S2).

[0040] When regenerating the model shape, the model generating device 10 changes the density of the unit spaces that divide the three-dimensional space (step S2), and again divides the three-dimensional space into unit spaces and generates occupancy information (step S3). For example, in order to reduce the data capacity of the model data and the number of basic structures, the model generating device 10 divides the three-dimensional space into unit spaces by lowering the density, which is the degree of density of the unit spaces, i.e., by significantly changing the size of the unit spaces. Then, the model generating device 10 again generates occupancy information using the changed unit spaces.

[0041] Thereafter, the model generating device 10 corrects the unoccupied space of the occupancy information (step S4), generates a model shape by converting the occupancy information into a combination of a plurality of basic structures (step S5), and generates model data (step S6).Then, the above-mentioned process is repeated until the data capacity of the model data and the number of basic structures become less than the threshold (step S7), and when they become less than the threshold (Yes in step S7), the model data is output (step S8).

[0042] As described above, in this embodiment, a model shape consisting of a combination of multiple basic structures is generated from design information such as robot CAD data, etc. Therefore, model data with a reduced data volume from the design information can be used in the simulation, preventing a decrease in simulation accuracy and processing speed, and improving the efficiency of the simulation.

[0043] Furthermore, in this embodiment, unoccupied spaces that have little effect on the accuracy of the robot simulation are treated as occupied spaces, and the number of basic structures that can be combined can be reduced, thereby further improving the efficiency of the simulation.

[0044] In addition, in this embodiment, a model shape made up of basic structures is formed at a spatial density that allows the desired accuracy and speed of the simulation to be obtained, which reduces the number of basic structures to be combined, thereby further improving the efficiency of the simulation.

[0045] <Embodiment 2> Next, a second embodiment of the present disclosure will be described with reference to Fig. 7 and Fig. 8. Fig. 7 and Fig. 8 are block diagrams showing the configuration of a model generation device in embodiment 2. Note that this embodiment shows an outline of the configuration of the model generation device described in the above embodiment.

[0046] First, the hardware configuration of the model generating device 100 in this embodiment will be described with reference to Fig. 7. The model generating device 100 is configured as a general information processing device, and is equipped with the following hardware configuration, for example: CPU (Central Processing Unit) 101 (arithmetic unit); ROM (Read Only Memory) 102 (storage device); RAM (Random Access Memory) 103 (storage device); programs 104 loaded into RAM 103; storage device 105 storing programs 104; drive device 106 for reading and writing data from and to a storage medium 110 external to the information processing device; communication interface 107 for connecting to a communication network 111 external to the information processing device; input / output interface 108 for inputting and outputting data; and bus 109 for connecting the various components.

[0047] 7 shows an example of the hardware configuration of the information processing device that is the model generation device 100, and the hardware configuration of the information processing device is not limited to the above-described case. For example, the information processing device may be configured with a part of the above-described configuration, such as not including the drive device 106. Furthermore, instead of the above-described CPU, the information processing device may use a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an MPU (Micro Processing Unit), an FPU (Floating Point Number Processing Unit), a PPU (Physics Processing Unit), a TPU (Tensor Processing Unit), a quantum processor, a microcontroller, or a combination thereof.

[0048] The model generating device 100 can be equipped with an acquisition unit 121, a first generation unit 122, and a second generation unit 123 shown in FIG. 8 by having the CPU 101 acquire and execute the group of programs 104. The group of programs 104 may be stored in advance in the storage device 105 or the ROM 102, for example, and loaded into the RAM 103 by the CPU 101 as needed for execution. The group of programs 104 may be supplied to the CPU 101 via the communication network 111, or may be stored in advance in the storage medium 110, with the drive device 106 reading out the programs and supplying them to the CPU 101. However, the acquisition unit 121, the first generation unit 122, and the second generation unit 123 described above may be constructed using dedicated electronic circuits for realizing such means.

[0049] The acquiring means 121 acquires first design information of the design object, and the first generating means 122 generates occupancy information indicating whether or not each position in the three-dimensional space is occupied by the design object based on the first design information. For example, the first generating means 122 generates point cloud data as the occupancy information, which is configured by assigning points indicating that each position in the three-dimensional space is occupied by the design object.

[0050] Then, the second generation means 123 generates a model shape of the design object consisting of a combination of a plurality of predetermined structures based on the occupancy information. At this time, the second generation means 123 treats even unoccupied positions as occupied positions based on the shape of the design object, thereby reducing the number of structures to be combined.

[0051] As described above, the model generation device of the present disclosure generates a model shape consisting of a combination of multiple structures from the design information of the design object. This allows for the use of model data with reduced data volume from the design information in a simulation, thereby preventing a decrease in simulation accuracy and processing speed and improving the efficiency of the simulation.

[0052] The above-described program can be stored and supplied to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program may also be supplied to a computer by various types of transitory computer-readable media. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer-readable media can be supplied to a computer via wired communication paths such as electric wires and optical fibers, or via wireless communication paths.

[0053] Although the present disclosure has been described above with reference to the above-described embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that are understandable to those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, at least one or more of the functions of the acquisition unit 121, the first generation unit 122, and the second generation unit 123 described above may be executed by an information processing device installed and connected anywhere on a network, i.e., may be executed by so-called cloud computing.

[0054] <Supplementary Notes> Some or all of the above embodiments can also be described as in the following supplementary notes. Below, an outline of the configurations of the model generation device, model generation method, and program according to the present disclosure will be described. However, the present disclosure is not limited to the following configurations. (Supplementary Note 1) A model generation device comprising: an acquisition means for acquiring first design information of a design; a first generation means for generating, based on the first design information, occupancy information indicating whether or not each position in three-dimensional space of the design is occupied by the design; and a second generation means for generating, based on the occupancy information, a model shape of the design consisting of a combination of a plurality of predetermined structures. (Supplementary Note 2) The model generation device according to Supplementary Note 1, wherein the second generation means generates, based on the occupancy information, the model shape consisting of a combination of the structures that encompasses an occupied position of the design in three-dimensional space. (Supplementary Note 3) The model generation device according to Supplementary Note 2, wherein the second generation means treats an unoccupied position of the design as an occupied position in accordance with the shape of the unoccupied position in three-dimensional space of the design based on the occupancy information. (Supplementary Note 4) The model generation device according to Supplementary Note 2, wherein the acquisition means acquires motion information of the design, and the second generation means treats unoccupied positions of the design in three-dimensional space as occupied positions based on the occupancy information and the motion information. (Supplementary Note 5) The model generation device according to Supplementary Note 4, wherein the second generation means treats unoccupied positions of the design, where a predetermined portion of the design cannot move in three-dimensional space, as occupied positions based on the occupancy information and the motion information. (Supplementary Note 6) The model generation device according to Supplementary Note 1, wherein the acquisition means acquires motion information of the design, and the first generation means generates the occupancy information only for a motion range of the design in three-dimensional space based on the motion information. (Supplementary Note 7) The model generation device according to Supplementary Note 1, wherein the first generation means generates the occupancy information by setting a density of each position in three-dimensional space so that the model shape to be generated satisfies a preset condition.(Supplementary Note 8) The model generation device according to Supplementary Note 7, wherein the first generation means generates the occupancy information by setting a density at each position in three-dimensional space so that the data volume of the model shape to be generated is less than a preset threshold. (Supplementary Note 9) The model generation device according to Supplementary Note 7, wherein the first generation means generates the occupancy information by setting a density at each position in three-dimensional space so that the number of structures constituting the model shape to be generated is less than a preset threshold. (Supplementary Note 10) A model generation method comprising: acquiring first design information of a design; generating occupancy information indicating whether or not each position in three-dimensional space is occupied by the design based on the first design information; and generating a model shape of the design consisting of a combination of a plurality of predetermined structures based on the occupancy information. (Supplementary Note 11) The model generation method according to Supplementary Note 10, wherein the model shape consisting of a combination of the structures that encompasses the occupied position of the design in three-dimensional space is generated based on the occupancy information. (Supplementary Note 12) The model generation method according to Supplementary Note 11, wherein an unoccupied position of the design object is treated as an occupied position in accordance with a shape of the unoccupied position in three-dimensional space based on the occupancy information. (Supplementary Note 13) The model generation method according to Supplementary Note 11, wherein: acquiring operation information of the design object; and treating an unoccupied position of the design object in three-dimensional space as an occupied position based on the occupancy information and the operation information. (Supplementary Note 14) The model generation method according to Supplementary Note 10, wherein: generating the occupancy information by setting a density of each position in three-dimensional space so that the generated model shape satisfies a preset condition. (Supplementary Note 15) A computer-readable storage medium storing a program for causing a computer to execute processes of acquiring first design information of a design object; generating occupancy information indicating whether each position in three-dimensional space is occupied by the design object based on the first design information; and generating a model shape of the design object consisting of a combination of a plurality of predetermined structures based on the occupancy information.

[0055] REFERENCE SIGNS LIST 10 Model generation device 11 Acquisition unit 12 Occupancy information generation unit 13 Conversion unit 14 Output unit 16 Design information storage unit 17 Basic structure information storage unit 18 Threshold value storage unit 20 Design information storage device 100 Model generation device 101 CPU 102 ROM 103 RAM 104 Program group 105 Storage device 106 Drive device 107 Communication interface 108 Input / output interface 109 Bus 110 Storage medium 111 Communication network 121 Acquisition means 122 First generation means 123 Second generation means

Claims

1. An acquisition means for acquiring first design information of a design object; A first generation means for generating occupancy information indicating the presence or absence of occupancy of the design object at each position in a three-dimensional space based on the first design information; A second generation means for generating a model shape of the design object composed of a combination of a plurality of predetermined structures based on the occupancy information; A model generation device comprising the above.

2. The model generation device according to Claim 1, wherein the second generation means generates the model shape composed of a combination of the structures including the occupancy positions of the design object in the three-dimensional space based on the occupancy information. Model generation device.

3. The model generation device according to Claim 2, wherein the second generation means treats a non-occupancy position as an occupancy position according to the shape of the non-occupancy position of the design object in the three-dimensional space based on the occupancy information. Model generation device.

4. The model generation device according to Claim 2, wherein the acquisition means acquires operation information of the design object, and the second generation means treats a non-occupancy position of the design object in the three-dimensional space as an occupancy position based on the occupancy information and the operation information. Model generation device.

5. The model generation device according to Claim 4, wherein the second generation means treats a non-occupancy position of the design object in the three-dimensional space where a predetermined part of the design object cannot move as an occupancy position based on the occupancy information and the operation information. Model generation device.

6. The model generation device according to Claim 1, wherein the acquisition means acquires operation information of the design object, and the first generation means generates the occupancy information only for the operation range of the design object in the three-dimensional space based on the operation information. Model generation device.

7. The model generation device according to Claim 1, wherein the first generation means generates the occupancy information by setting the density of each position in the three-dimensional space so that the generated model shape satisfies a preset condition. Model generation device.

8. The model generation device according to Claim 7, wherein the first generation means generates the occupancy information by setting the density of each position in the three-dimensional space so that the data capacity of the generated model shape is less than a preset threshold. Model generation device.

9. The model generation device according to Claim 7, wherein The first generation means generates the occupancy information by setting the density at each position in the three-dimensional space so that the number of the structures constituting the model shape to be generated is less than a preset threshold value. Model generation device. **Claim 10**: An information processing apparatus acquires first design information of a design object, generates occupancy information representing the presence or absence of occupancy of the design object at each position in the three-dimensional space based on the first design information, generates a model shape of the design object composed of a combination of a plurality of predetermined structures based on the occupancy information. Model generation method.