Interference checking device

Abstract

A shape of a robot and a surrounding obstacle is modeled with a small amount of data, and the accuracy of interference checking is improved. An interference checking device performs interference checking between a robot and a surrounding obstacle, wherein the interference checking device includes: a cuboid set conversion section that converts the robot and the surrounding obstacle into a three-dimensional model of a set of cuboids, respectively; and an interference determination section that determines whether the three-dimensional model of the robot interferes with the three-dimensional model of the surrounding obstacle by simulating movement of the three-dimensional models of the robot and the surrounding obstacle based on a movement program.

Description

Technical Field

[0001] This invention relates to an interference detection device. Background Technology

[0002] In order to search for the robot's movement path, interference checks are performed on the robot and surrounding obstacles such as other machines or safety fences in a virtual environment.

[0003] In this regard, a known technique uses triangular mesh data of a three-dimensional model comprising the robot and the surrounding obstacles to perform interference checks in order to determine interference between the robot and surrounding obstacles. See, for example, Patent Document 1.

[0004] Additionally, a technique is known that utilizes a polyhedron, equivalent to a simplified convex hull, to perform high-speed processing accompanied by interference calculations involving three-dimensional shapes, which are three-dimensional shapes composed of free-form surfaces or multiple free-form surfaces expressed in a high-order curved surface manner. See, for example, Patent Document 2.

[0005] Additionally, a technique is known that uses voxel models to detect interference between a robot and surrounding obstacles. These voxel models have multiple voxels, or voxelized spheres and cylinders. See, for example, Patent Document 3.

[0006] Existing technical documents

[0007] Patent documents

[0008] Patent Document 1: Japanese Patent Application Publication No. 2020-179441

[0009] Patent Document 2: Japanese Patent Application Publication No. 2002-342395

[0010] Patent Document 3: Japanese Patent Application Publication No. 2012-232408 Summary of the Invention

[0011] The problem that the invention aims to solve

[0012] In Patent Document 1, when performing interference checks on a 3D model using triangular meshes, although the correct judgment result can be obtained, there is a problem of very high computational cost (especially when there are many triangular meshes).

[0013] In addition, in Patent Document 2, depending on the shape of the surrounding obstacles, the space occupied by the convex hull is sometimes larger than the actual shape, which can sometimes reduce the accuracy of interference detection.

[0014] In addition, in Patent Document 3, when the sphere or cylinder contained in the voxel is larger than the actual robot or surrounding obstacles, there is a problem that the robot will always be in an interfering state when it approaches the surrounding obstacles.

[0015] Therefore, it is desirable to model the shape of the robot and surrounding obstacles with less data and improve the accuracy of interference detection.

[0016] Methods for solving problems

[0017] One aspect of the interference detection device disclosed herein is an interference detection device for detecting interference between a robot and surrounding obstacles, wherein the interference detection device comprises: a cuboid set conversion unit that converts the robot and the surrounding obstacles into three-dimensional models of cuboid sets respectively; and an interference determination unit that determines whether there is interference between the three-dimensional models of the robot and the three-dimensional models of the surrounding obstacles by simulating the movements of the three-dimensional models of the robot and the surrounding obstacles based on an action program.

[0018] Invention Effects

[0019] According to one approach, the shape of the robot and surrounding obstacles can be modeled with a smaller amount of data, and the accuracy of interference detection can be improved. Attached Figure Description

[0020] Figure 1 This is a functional block diagram illustrating a functional structural example of an interference detection device according to one embodiment.

[0021] Figure 2 This is an example of 3D CAD data representing surrounding obstacles.

[0022] Figure 3 This is a diagram illustrating an example of a cut surface in the case of a robot.

[0023] Figure 4 It means Figure 2 The diagram shows an example of a three-dimensional model of a set of cuboids representing surrounding obstacles.

[0024] Figure 5 It means Figure 2 A diagram showing an example of a 3D model of the convex hull of a surrounding obstacle.

[0025] Figure 6 It means to Figure 4 The diagram shows an example of a set of cuboids representing surrounding obstacles with a margin.

[0026] Figure 7 This is a flowchart illustrating the interference detection process of the interference detection device.

[0027] Figure 8 This is a flowchart illustrating the 3D model conversion process of the interference detection device. Detailed Implementation

[0028] <One Implementation Method>

[0029] Figure 1 This is a functional block diagram illustrating a functional structural example of an interference detection device according to one embodiment.

[0030] like Figure 1 As shown, the interference detection device 1 is a known computer, including a control unit 10, an input unit 11, a display unit 12, and a storage unit 13. The control unit 10 includes a cuboid set conversion unit 101 and an analog execution unit 102. The cuboid set conversion unit 101 includes a simplified range setting unit 111, a point group data conversion unit 112, a genetic point group data segmentation unit 113, a minimum total volume retrieval unit 114, and a margin setting unit 115. The analog execution unit 102 includes a coordinate system conversion unit 121 and an interference determination unit 122.

[0031] Furthermore, the interference detection device 1 can also be interconnected with the robot control device (not shown) that controls the actions of the robot (not shown) via a network such as a LAN (Local Area Network) or the Internet. Alternatively, the interference detection device 1 can also be directly interconnected with the robot control device (not shown) via a connection interface (not shown).

[0032] <Input Section 11>

[0033] The input unit 11 may be, for example, a keyboard, or a touch panel configured in the display unit 12 described later. As described later, the input unit 11 receives from users such as operators the number of cuboids when a three-dimensional model of a robot (not shown), a surrounding machine (not shown), or a safety fence or other surrounding obstacle is set as a collection of cuboids.

[0034] <Display Unit 12>

[0035] The display unit 12 is, for example, a liquid crystal display, which displays the determination result of the interference detection device 1.

[0036] <Storage Department 13>

[0037] Storage unit 13 can be an SSD (Solid State Drive) or HDD (Hard Disk Drive), and can also store various motion programs that enable a robot or peripheral machine (not shown) to perform actions. Additionally, storage unit 13 includes an action log storage unit 131 and a shape data storage unit 132.

[0038] As described later, the action log storage unit 131 stores three-dimensional coordinate values ​​and time as an action log. The three-dimensional coordinate values ​​are used by the interference determination unit 122 to simulate the action program by using a three-dimensional model of the robot and the set of cuboids of the surrounding obstacles, which are converted by the cuboid set conversion unit 101 based on shape data. This indicates the position and posture of the set of cuboids of the robot and the surrounding obstacles. The shape data is 3D CAD data of the robot (not shown) and the surrounding obstacles such as the robot's surrounding machines or safety fences, etc., in a three-dimensional manner.

[0039] The shape data storage unit 132 stores shape data such as 3D CAD data of the robot (not shown) and 3D CAD data of surrounding obstacles (not shown).

[0040] <Control Unit 10>

[0041] The control unit 10 is a control unit known to those skilled in the art, and includes a CPU (Central Processing Unit), ROM (Read Only Memory), RAM (Random Access Memory), CMOS (Complementary Metal-Oxide-Semiconductor) memory, etc., and these structures are capable of communicating with each other via a bus.

[0042] The CPU is the processor that controls the interference detection device 1 as a whole. The CPU reads the system program and application program stored in ROM via the bus and controls the interference detection device 1 according to the system program and application program. Thus, as... Figure 1 As shown, the control unit 10 is configured to perform the functions of a cuboid set conversion unit 101 and an analog execution unit 102. Furthermore, the cuboid set conversion unit 101 is configured to perform the functions of a simplified range setting unit 111, a point group data conversion unit 112, a genetic point group data segmentation unit 113, a minimum total volume retrieval unit 114, and a margin setting unit 115. Furthermore, the analog execution unit 102 is configured to perform the functions of a coordinate system conversion unit 121 and an interference determination unit 122. Various data, such as temporary calculation data and display data, are stored in the RAM. Additionally, the CMOS memory is configured as a non-volatile memory, which is backed up by a battery (not shown), and can maintain its storage state even when the power supply to the interference detection device 1 is disconnected.

[0043] <Includes cuboid set transformation part 101>

[0044] For example, the preprocessing of the main processing of the simulation execution unit 102 described later includes the cuboid set conversion unit 101, which converts the robot and the surrounding obstacles into three-dimensional models of cuboid sets based on the shape data (3D CAD data) of the robot and the surrounding obstacles (not shown) stored in the shape data storage unit 132 of the storage unit 13.

[0045] The following describes the operation of the cuboid set conversion unit 101, and the functions of the simplified range setting unit 111, point group data conversion unit 112, genetic point group data segmentation unit 113, minimum total volume retrieval unit 114, and margin setting unit 115 constituting the cuboid set conversion unit 101.

[0046] <Simplified Range Setting Section 111>

[0047] The simplified range setting unit 111 displays 3D CAD data as shape data of surrounding obstacles on the display unit 12, and sets the range for interference detection in the shape data of surrounding obstacles based on the input operation of the input unit 11 performed by the user.

[0048] Figure 2 This is a diagram representing an example of the shape data of surrounding obstacles. Furthermore, in Figure 2 The figure shows the shape of the surrounding obstacles on the XY plane as viewed from the positive Z-axis direction in the shape data (3D CAD data) of the surrounding obstacles.

[0049] like Figure 2 As shown, the simplified range setting unit 111 accepts the specification of the range shown by dashed lines in the shape data (3D CAD data) of the surrounding obstacles based, for example, on the input operation performed by the user on the input unit 11. The simplified range setting unit 111 sets the accepted range as the specified range.

[0050] Therefore, interference detection device 1 only performs interference detection on the range in which the robot (not shown) actually performs its actions, thereby simplifying interference detection.

[0051] <Point Group Data Conversion Department 112>

[0052] The dot group data conversion unit 112 uses, for example, a known conversion method to convert dot group data, based on the 3D CAD data of the robot and the surrounding obstacles that are not labeled and stored in the shape data storage unit 132 of the storage unit 13, to convert them into dot group data of the robot and dot group data of the surrounding obstacles in the specified range set by the simplified range setting unit 111.

[0053] <Genetic Pit Cluster Data Segmentation Section 113>

[0054] For example, the genetic point group data segmentation unit 113 calculates an evaluation value for each combination of multiple cut surfaces of multiple cuboids input by the user, which are segmented by the point group data conversion unit 112 into point group data of the robot and surrounding obstacles respectively. It also repeats the calculation of the evaluation value of each combination of multiple cut surfaces newly generated based on a known genetic algorithm for a preset maximum number of repetitions.

[0055] Specifically, for example, when the user inputs the number of cuboids n via the input unit 11, the genetic point group data segmentation unit 113 randomly generates k combinations of (n-1) cut surfaces that divide the point group data of the robot (or surrounding obstacles) (not shown) into n (n-1) cut surfaces (n and k are integers of 2 or more).

[0056] Figure 3 This is a diagram illustrating an example of a cut surface in the case of a robot. Figure 3 In the image, the robot is represented by solid lines, and the collection of cuboids representing the robot is represented by dashed lines in a 3D model. Additionally, in... Figure 3 In the image, a three-dimensional model of a robot consisting of a collection of cuboids is shown, illustrating the case where the number of cuboids n, as input by the user, is 8, with 7 cut surfaces represented by thick solid lines.

[0057] The genetic point swarm data segmentation unit 113 calculates the volume of each cuboid in the n point swarm data segments, which are divided by (n-1) cut surfaces of each of the k combinations, and calculates the total volume of the set of cuboids of the robot (or surrounding obstacles) as the evaluation value for each combination of (n-1) cut surfaces. The genetic point swarm data segmentation unit 113 repeatedly recalculates the evaluation values ​​of the (n-1) cut surfaces of each of the k combinations newly generated through genetic operations based on the genetic algorithm for a preset maximum number of repetitions.

[0058] Furthermore, the number of cut surfaces that divide the point group data into n is not limited to (n-1) and can also be less than (n-1).

[0059] <Minimum Total Volume Retrieval Unit 114>

[0060] The minimum total volume retrieval unit 114, for example, retrieves the (n-1) cut surfaces with the highest evaluation values ​​(i.e., the set of cuboids in the 3D model of the robot (or surrounding obstacles, not shown) with the smallest total volume) from among the evaluation values ​​of the (n-1) cut surfaces of each of the k combinations, and uses these as the optimal cut surfaces. Figure 3 As shown, the minimum total volume retrieval unit 114 selects a set of cuboids divided by the optimal (n-1) cut surfaces as a 3D model of the robot (or surrounding obstacles) not shown.

[0061] Figure 4 It means Figure 2The diagram shows an example of a three-dimensional model of a set of cuboids representing surrounding obstacles.

[0062] like Figure 4 As shown, Figure 2 The three-dimensional model of the surrounding obstacles shown is a collection of two cuboids 200(1) and 200(2) shown by thick dashed lines in a specified range 150 set by the simplified range setting unit 111, with the cuboids 200(1) and 200(2) being cut off.

[0063] Figure 5 It means Figure 2 A diagram showing an example of a 3D model of the convex hull of a surrounding obstacle.

[0064] like Figure 5 As shown above, the 3D model of the convex hull of the surrounding obstacle, indicated by the thick dashed line, is larger than the actual shape of the surrounding obstacle. In contrast, Figure 4 The three-dimensional model of the set of cuboids of the surrounding obstacles shows a size that is almost identical to the actual shape of the surrounding obstacles.

[0065] Therefore, the interference detection device 1 can perform interference detection with good accuracy.

[0066] <Balance Setting Section 115>

[0067] The margin setting unit 115 sets a margin for a three-dimensional model of a collection of cuboids of robots and surrounding obstacles (not shown).

[0068] Specifically, the margin setting unit 115, for example, targets each cuboid of a set of cuboids (not shown) containing the robot and surrounding obstacles, such as... Figure 6 As shown, a pre-defined margin d is set in each direction of the X-axis, Y-axis, and Z-axis.

[0069] By performing the preprocessing described above by the cuboid set conversion unit 101, the use of triangular meshes in the original 3D CAD data of the robot and surrounding obstacles can be avoided, thereby reducing the computational cost of interference inspection.

[0070] <Simulation Execution Department 102>

[0071] The simulation execution unit 102 performs the simulation as the main processing, and checks whether there is any interference between the robot's three-dimensional model and the three-dimensional models of the surrounding obstacles. The simulation is based on the action program, and as preprocessing, it enables the three-dimensional models of the robot and the surrounding obstacles, which are converted by the cuboid set conversion unit 101, to perform actions.

[0072] The following describes the operation of the simulation execution unit 102 and the functions of the coordinate system transformation unit 121 and the interference determination unit 122 constituting the simulation execution unit 102.

[0073] <Coordinate System Transformation Section 121>

[0074] The coordinate system transformation unit 121 updates the position and pose of the three-dimensional model of the set of cuboids of the robot and surrounding obstacles (not shown) based on the movements of the robot and surrounding obstacles, for example, when the simulation of the action program has been executed by the simulation execution unit 102.

[0075] <Interference Detection Unit 122>

[0076] The interference determination unit 122 determines whether there is interference between the robot's three-dimensional model and the three-dimensional model of the surrounding obstacles by simulating the motion of a three-dimensional model of a set of cuboids of the robot (not shown) and the surrounding obstacles based on the motion program performed by the simulation execution unit 102.

[0077] Specifically, the interference determination unit 122, for example, stores three-dimensional coordinate values ​​and times as an action log in the action log storage unit 131 during the simulation of the action program performed by the simulation execution unit 102. The three-dimensional coordinate values ​​represent the position and posture of the set of cuboids representing the robot and surrounding obstacles. The interference determination unit 122 detects cuboids that cause interference in space at the same time during the action log of the robot and surrounding obstacles, and determines whether interference exists. The interference determination unit 122 may also display the determination result on the display unit 12.

[0078] This allows users to determine the positional relationship between the robot and surrounding obstacles.

[0079] <Interference Detection Processing of Interference Detection Device 1>

[0080] Then, while referring to Figure 7 The process of interference detection and processing of interference detection device 1 is explained.

[0081] Figure 7 This is a flowchart illustrating the interference detection process of interference detection device 1. The process shown here is executed whenever an interference detection instruction is received from a user-received robot (not shown) via input unit 11.

[0082] In step S1, when a conversion instruction for a 3D model of a set of cuboids of the robot is received from the user via the input unit 11, the cuboid set conversion unit 101 performs a 3D model conversion process. This process converts the robot's shape data (3D CAD data) stored in the shape data storage unit 132 into a 3D model of the robot as a set of cuboids. A detailed description of the 3D model conversion process will follow.

[0083] In step S2, the cuboid set conversion unit 101 performs a three-dimensional model conversion process in the same way as in the case of the robot in step S1. The three-dimensional model conversion process converts the shape data (3D CAD data) of the surrounding obstacles stored in the shape data storage unit 132 into a three-dimensional model of the cuboid set of the surrounding obstacles.

[0084] In step S3, the simulation execution unit 102 (interference determination unit 122) performs motion simulations based on the action program, simulating the actions of the three-dimensional model of the robot's cuboid set converted in step S1 and the three-dimensional model of the surrounding obstacles' cuboid set converted in step S2, in order to determine whether there is any interference between the robot's three-dimensional model and the three-dimensional model of the surrounding obstacles.

[0085] In step S4, the simulation execution unit 102 (interference determination unit 122) displays the determination result on the display unit 12.

[0086] <Three-dimensional model conversion processing of interference detection device 1>

[0087] Figure 8 This means that in Figure 7 The flowchart shows the detailed processing steps of the 3D model conversion process shown in steps S1 and S2.

[0088] In step S21, the cuboid set conversion unit 101 reads the shape data (3D CAD data) of the accepted robot (or surrounding obstacle) from the shape data storage unit 132 via the input unit 11, based on the conversion instruction of the three-dimensional model of the cuboid set of the user-accepted robot (or surrounding obstacle).

[0089] In step S22, the cuboid set conversion unit 101 determines whether the shape data read in step S21 is a surrounding obstacle. If the shape data is a surrounding obstacle, the process proceeds to step S23. On the other hand, if the shape data is not a surrounding obstacle, i.e., it is a robot, the process proceeds to step S24.

[0090] In step S23, the simplified range setting unit 111 displays, for example, the shape data (3DCAD data) of the surrounding obstacles on the display unit 12, and accepts and sets the specified range for interference detection in the shape data of the surrounding obstacles based on the input operation of the input unit 11 performed by the user.

[0091] In step S24, the point group data conversion unit 112 converts the shape data (3DCAD data) of the robot (or the point group data of the surrounding obstacles within the specified range set in step S23) into the robot's point group data.

[0092] In step S25, the genetic point group data segmentation unit 113 randomly generates k combinations of (n-1) cut surfaces, representing the robot (or surrounding obstacle) point group data converted in step S24, into combinations of the number of cuboids n input by the user via the input unit 11. The genetic point group data segmentation unit 113 calculates the total volume of the set of cuboids of the robot (or surrounding obstacle) segmented by the (n-1) cut surfaces of each of the k combinations, and uses this volume as the evaluation value for each combination of (n-1) cut surfaces.

[0093] In step S26, the genetic point group data segmentation unit 113 recalculates the evaluation values ​​of the (n-1) cut surfaces of each of the k newly generated combinations based on the genetic algorithm through genetic operations.

[0094] In step S27, the genetic locus data segmentation unit 113 determines whether the maximum number of repetitions m has been repeated and finds m candidate locus sets. If m candidate locus sets have been found, the process proceeds to step S28. On the other hand, if m candidate locus sets have not been found, the process returns to step S26.

[0095] In step S28, the minimum total volume retrieval unit 114 retrieves the (n-1) cut surfaces with the highest evaluation values ​​from the evaluation values ​​of the (n-1) cut surfaces of each of the k combinations, i.e., the set of cuboids with the smallest total volume in the 3D model of the robot (or surrounding obstacle) not shown, and selects them as the optimal cut surfaces. The minimum total volume retrieval unit 114 selects the set of cuboids divided by the optimal (n-1) cut surfaces as the 3D model of the robot (or surrounding obstacle) not shown.

[0096] In step S29, the margin setting unit 115 sets a margin for the three-dimensional model of the set of cuboids of the robot (or surrounding obstacles) selected in step S28.

[0097] Therefore, the interference detection device 1 according to one embodiment converts the robot and surrounding obstacles into a three-dimensional model of a set of cuboids based on the shape data (3D CAD data) of the robot and surrounding obstacles. Thus, the interference detection device 1 can model the shape of the robot and surrounding obstacles with less data and improve the accuracy of interference detection.

[0098] In addition, by using a three-dimensional model of a set of cuboids, the interference detection device 1 can reduce the computational cost of interference detection and can realize interference detection at high speed.

[0099] The above describes one embodiment, but the interference detection device 1 is not limited to the above embodiment, and also includes variations and improvements within the scope of achieving the purpose.

[0100] <Variation Example 1>

[0101] In the above embodiment, although the interference detection device 1 is configured as a different device from the robot control device (not shown), it is not limited thereto. For example, the interference detection device 1 may also be included in the robot control device (not shown).

[0102] <Variation Example 2>

[0103] For example, in the above embodiment, the cuboid set conversion unit 101 converts the shape data (3D CAD data) of the robot (or surrounding obstacles) stored in the shape data storage unit 132 into point group data, and divides the point group data into n point group data based on a genetic algorithm and converts it into a three-dimensional model of a set of cuboids of the robot (or surrounding obstacles), but it is not limited to this. For example, the cuboid set conversion unit 101 may also convert the robot (or surrounding obstacles) into a three-dimensional model of a set of cuboids based on the number of cuboids, the position of each cuboid, the size of each cuboid, etc., specified by the user based on the shape data (3D CAD data).

[0104] Furthermore, the various functions included in the interference detection device 1 of one embodiment can be implemented separately by hardware, software, or a combination thereof. Here, implementation by software means implementation by reading and executing a program by a computer.

[0105] Programs 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., floppy disks, magnetic tapes, hard disk drives), optical-magnetic recording media (e.g., magneto-optical disks), CD-ROMs (Read-Only Memory), CD-Rs, CD-R / Ws, semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash memory, and RAM). Alternatively, programs can also be supplied to a computer using various types of transient computer-readable media. Examples of transient computer-readable media include electrical signals, optical signals, and electromagnetic waves. Transient computer-readable media can supply programs to a computer via wired communication paths such as electrical wires and optical fibers, or via wireless communication paths.

[0106] Furthermore, the steps describing a program recorded on a recording medium certainly include processing performed sequentially in time, but not necessarily in time, and also include processing performed in parallel or individually.

[0107] In other words, the interference detection device disclosed herein can be implemented in various ways with the following structure.

[0108] (1) The interference detection device 1 disclosed herein is an interference detection device for detecting interference between a robot and surrounding obstacles. The interference detection device 1 includes: a cuboid set conversion unit 101, which converts the robot and surrounding obstacles into three-dimensional models of cuboid sets respectively; and an interference determination unit 122, which determines whether there is interference between the three-dimensional model of the robot and the three-dimensional model of the surrounding obstacles by simulating the actions of the three-dimensional models of the robot and the surrounding obstacles based on the motion program.

[0109] According to the interference detection device 1, the shape of the robot and surrounding obstacles can be modeled with less data and the accuracy of interference detection can be improved.

[0110] (2) Alternatively, the interference detection device 1 described in (1) may include: an input unit 11 that inputs the number of cuboids in a set of cuboids; a cuboid set conversion unit 101 that includes: a point group data conversion unit 112 that converts the shape data of the robot and the surrounding obstacles into point group data for the robot and the surrounding obstacles respectively; a genetic point group data segmentation unit 113 that calculates an evaluation value for each combination of multiple cut surfaces that divide the point group data of the robot and the surrounding obstacles into the number of cuboids input to the input unit 11, and recalculates an evaluation value for each combination of multiple cut surfaces newly generated based on a genetic algorithm; and a minimum total volume retrieval unit 114 that retrieves the multiple cut surfaces with the highest evaluation values ​​among the evaluation values ​​of each combination of multiple cut surfaces of the robot and the surrounding obstacles.

[0111] Thus, the interference detection device 1 can be modeled as a set of cuboids that best fit the robot and the surrounding obstacles.

[0112] (3) Alternatively, in the interference detection device 1 described in (1) or (2), the cuboid set conversion unit 101 includes: a simplified range setting unit 111, which sets the range for simulation in the shape data of the surrounding obstacles; and a margin setting unit 115, which sets the margin for the three-dimensional model of the cuboid set of the robot and the surrounding obstacles.

[0113] Therefore, the interference detection device 1 only models the part of the interference detection required in the surrounding obstacles, thereby reducing the storage capacity required for simulation.

[0114] (4) Alternatively, in the interference detection device described in (2), the genetic point group data segmentation unit 113 recalculates the evaluation value of each combination of multiple cut surfaces with a preset number of repetitions.

[0115] Thus, the interference detection device 1 can generate a three-dimensional model of the optimal set of cuboids for both the robot and the surrounding obstacles.

[0116] Explanation of reference numerals in the attached figures

[0117] 1. Interference detection device

[0118] 10. Control Department

[0119] 101 contains a cuboid set transformation part.

[0120] 111 Simplified Range Setting Section

[0121] 112-point group data conversion department

[0122] 113 Genetic Locus Data Segmentation Section

[0123] 114 Minimum Total Volume Retrieval Unit

[0124] 115 Margin Setting Department

[0125] 102 Simulation Execution Unit

[0126] 121 Coordinate System Transformation Section

[0127] 122 Interference Detection Unit

[0128] 11 Input Section

[0129] 12 Display Section

[0130] 13 Storage Department

[0131] 131 Action Log Storage Department

[0132] 132 Shape Data Storage Unit.

Claims

1. An interference detection device for detecting interference between a robot and surrounding obstacles, characterized in that, The interference detection device includes: It includes a cuboid set conversion unit, which converts the robot and the surrounding obstacles into three-dimensional models of cuboid sets respectively; The interference determination unit determines whether there is interference between the 3D model of the robot and the 3D model of the surrounding obstacles by simulating the movements of the robot and the 3D model of the surrounding obstacles based on the motion program. The interference detection device includes an input unit that receives the number of cuboids in the set of cuboids. The cuboid-shaped set conversion unit includes: The dot group data conversion unit converts the shape data of the robot and the surrounding obstacles into dot group data for the robot and the surrounding obstacles respectively. The point group data segmentation unit segments the point group data of the robot and the surrounding obstacles into the number of cuboids that are input to the input unit. It calculates an evaluation value for each combination of multiple cut surfaces that segment the point group data of the robot and the surrounding obstacles into the number of cuboids, and calculates the evaluation value again for each combination of the multiple cut surfaces newly generated based on the genetic algorithm. The minimum total volume retrieval unit retrieves the plurality of cut surfaces with the highest evaluation values ​​from the evaluation values ​​of each combination of the plurality of cut surfaces of the robot and the surrounding obstacles.

2. The interference detection device according to claim 1, characterized in that, The cuboid-shaped set conversion unit includes: A simplified range setting unit sets the range for the simulation in the shape data of the surrounding obstacles; The margin setting unit sets a margin for the three-dimensional model of the set of cuboids of the robot and the surrounding obstacles.

3. The interference detection device according to claim 1, characterized in that, The point group data segmentation unit recalculates the evaluation value of each combination of the multiple cut surfaces by repeating the calculation a predetermined number of times.

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