Sorting system for sorting objects, and identification element

Abstract

This invention provides a sorting system for objects that allows a transport vehicle to sort objects quickly and accurately without using GNSS or beacons. [Solution] This sorting system comprises a transport vehicle for transporting objects to be sorted, an identifier arranged in a plurality of sorting areas for separating the objects to be sorted and for identifying the plurality of sorting areas, the identifier having a three-dimensional shape that appears to be the same two-dimensional shape when viewed from different horizontal directions, and an imaging device capable of capturing an image of the entire identifier. The transport vehicle is configured to identify the type of object to be sorted, identify the sorting area to which the object should be sorted based on the image of the identifier captured by the imaging device, and transport the object to be sorted to the identified sorting area.

Description

Technical Field

[0001] The present invention relates to a sorting object selection system for sorting sorting objects (for example, industrial waste products such as discarded household appliances), and an identifier.

Background Art

[0002] Based on the deepening of environmental problems and the like, the development of a system for sorting industrial waste products such as discarded household appliances has been underway (for example, see Patent Document 1). This type of device picks up industrial waste products using a robot or a carrier vehicle, discriminates their types, and transports the industrial waste products to sorting areas for each type using a carrier vehicle, a belt conveyor, or the like. Similar systems can also be applied to various sorting objects other than industrial waste products.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an industrial waste product sorting system using a carrier vehicle, it is necessary for the carrier vehicle to accurately estimate its own position, accurately grasp the position of the sorting area, and sort the sorting objects. As a technology for self-position estimation, SLAM (Simultaneous Localization and Mapping) is known. However, when attempting to apply it to the sorting of industrial waste products, since the surrounding shape changes greatly depending on the industrial waste products carried into the stockyard, the application of SLAM is not easy. In addition, technologies for positioning the carrier vehicle using GNSS, beacons, or the like are also known, but it is difficult to ensure sufficient stability and accuracy.

[0005] In view of the above problems, the present invention aims to provide a sorting system that enables a transport vehicle to sort objects at high speed and with high accuracy without using GNSS, beacons, or the like. [Means for solving the problem]

[0006] The sorting system according to the present invention is a sorting system for sorting objects, comprising: a transport vehicle for transporting the objects to be sorted; an identifier arranged in a plurality of sorting areas for separating the objects to be sorted, for identifying the plurality of sorting areas, the identifier having a three-dimensional shape that appears to be the same two-dimensional shape when viewed from different horizontal directions; and an imaging device capable of capturing an image of the entire identifier. The transport vehicle is configured to identify the type of object to be sorted, identify the sorting area to which the object should be sorted based on the image of the identifier captured by the imaging device, and transport the object to be sorted to the identified sorting area.

[0007] In this sorting system for objects to be sorted, the identifier may be composed of multiple types of unit structures having different shapes from each other. In this case, the multiple types of unit structures may have the same color. Alternatively, the multiple types of unit structures may have different colors. The imaging device may be configured to capture images in all directions.

[0008] Furthermore, an identifier according to one aspect of the present invention is an identifier that is arranged in a plurality of sorting areas for separating objects to be sorted, and is configured to identify the plurality of sorting areas, and has a three-dimensional shape that appears to be the same two-dimensional shape when viewed from different horizontal directions. The plurality of identifiers have different three-dimensional shapes from each other. Here, the identifier can be composed of a combination of a plurality of different types of unit structures having different shapes from each other. The plurality of different types of unit structures may have the same color. Alternatively, the plurality of different types of unit structures may have different colors. [Brief explanation of the drawing]

[0009] [Figure 1] This is a schematic diagram illustrating the overall configuration of the industrial waste sorting system 1 according to the first embodiment. [Figure 2] This is a schematic perspective view that further explains the configuration of the transport vehicle 20. [Figure 3] This is a schematic diagram illustrating an example of the structure of Landmark 30. [Figure 4] This is a table showing the relationship between the various landmarks (30) and landmark IDs. [Figure 5] This is an example of a sorting area data storage unit 11 included in server 10. [Figure 6] This is an example of a sorting area location storage unit 27 included in the transport vehicle 20. [Figure 7] This is a flowchart illustrating the operation of the industrial waste sorting system 1 according to the first embodiment. [Figure 8] This section explains the detection of landmark 30. [Figure 9] This section explains the detection of landmark 30. [Figure 10] This section explains the detection of landmark 30. [Figure 11] This section explains how to set the threshold used for detecting landmark 30. [Figure 12] This section explains how to set the threshold used for detecting landmark 30. [Figure 13] This section explains how to set the threshold used for detecting landmark 30. [Figure 14] This is a schematic diagram illustrating the landmark 30 according to the second embodiment. [Modes for carrying out the invention]

[0010] Hereinafter, this embodiment will be described with reference to the attached drawings. In the attached drawings, functionally identical elements may sometimes be denoted by the same number. Although the attached drawings show embodiments and implementation examples in accordance with the principles of the present disclosure, these are for the purpose of understanding the present disclosure and are not used to limit the interpretation of the present disclosure in any way. The description in this specification is merely a typical example and does not limit the scope or application examples of the claims of the present disclosure in any sense.

[0011] In this embodiment, the description is provided in sufficient detail for those skilled in the art to implement the present disclosure. However, other implementations and forms are possible, and it is necessary to understand that configuration and structural changes and replacement of various elements can be made without departing from the scope and spirit of the technical idea of the present disclosure. Therefore, the following description should not be construed as limited thereto.

[0012] [First Embodiment] Referring to FIG. 1, the overall configuration of the industrial waste product sorting system 1 according to the first embodiment will be described. Hereinafter, industrial waste products such as home appliances, personal computers, and other electrical products will be described as an example of the sorting target, but the following description is also applicable to other sorting targets.

[0013] This industrial waste product sorting system 1 is roughly composed of, as an example, a server 10, a transport vehicle 20, a landmark 30 as an identifier, and a management computer 50.

[0014] Server 10 is a central processing unit that controls the operation of this system 1. It stores various types of information and controls the carrier vehicle 20 through communication via a network NW (such as the Internet, LAN, WAN, etc.). The carrier vehicle 20 has the function of picking up industrial waste products from an accumulation area (stockyard) CP where industrial waste products are accumulated, discriminating their types, and transporting and sorting them to sorting areas DP1 to DPn corresponding to the discriminated types. The carrier vehicle 20 is equipped with an omnidirectional camera 41 and a LiDAR 42 to estimate its own position and to identify the accumulation area CP and the sorting areas DP1 to DPn. The LiDAR 42 irradiates a laser from a laser diode (not shown) towards an object and measures the direction and distance of the object by detecting the reflected light with an optical sensor (not shown).

[0015] The landmark 30 is arranged in the sorting areas DP1 to DPn and is an identifier for identifying the sorting areas DP1 to DPn. The landmarks 30 arranged in different sorting areas DP1 to DPn have different three-dimensional shapes from each other. Therefore, when the landmark 30 is imaged by the omnidirectional camera 41 and further measured by the LiDAR 42, the positional relationship between the carrier vehicle 20 and the landmark 30 is grasped. For the accurate determination of the three-dimensional shape of the landmark 30, it is preferable that the height of the landmark 30 and the height of the omnidirectional camera 41 are substantially the same. Note that the landmark 30 may be installed not only in the sorting area DP but also in the accumulation area CP, a temporary storage place (not shown), a relay point, etc.

[0016] As an example, the server 10 includes a sorting area data storage unit 11, a carrier vehicle control unit 12, an image analysis unit 13, a histogram generation unit 14, a threshold setting unit 15, and a communication control unit 16.

[0017] The sorting area data storage unit 11 stores the relationship between the sorting areas DP1 to DPn, the types of industrial waste products sorted there, and the shape data of the landmarks 30 arranged in the sorting areas DP1 to DPn. The carrier vehicle control unit 12 generates control signals such as drive signals and emergency stop signals for the carrier vehicle 20 to control the carrier vehicle 20.

[0018] The image analysis unit 13 analyzes image signals captured by the omnidirectional camera 41 and laser point cloud data acquired by the LiDAR 42. The histogram generation unit 14 generates a histogram used to set a threshold for detecting landmarks 30, although this will be described in detail later. The threshold setting unit 15 sets a threshold for detecting landmarks 30 according to the generated histogram. The threshold may be set by an operator of the management computer 50 by inputting it from an input device such as a keyboard according to the obtained histogram, or it may be set automatically by an image processing program. The communication control unit 16 controls the transmission and reception of data and control signals between the server 10 and the transport vehicle 20.

[0019] The transport vehicle 20, as an example, includes a movement control unit 21, a communication control unit 22, an image analysis unit 23, a distance / angle determination unit 24, a waste product type determination unit 25, a landmark shape determination unit 26, and a sorting area position storage unit 27.

[0020] The movement control unit 21 controls the movement of the transport vehicle 20 based on control signals from the transport vehicle control unit 12 or image signals from the omnidirectional camera 41 or LiDAR 42. The communication control unit 22 controls the transmission and reception of data and various signals with external devices such as the server 10. The image analysis unit 23 performs analysis of various image signals. The image analysis unit 23 and the image analysis unit 13 of the server 13 can appropriately share the image analysis roles according to their processing load, communication speed, etc.

[0021] The distance / angle determination unit 24 determines the distance r and angle θ to the landmark 30 and other structures based on the image signals acquired by the omnidirectional camera 41 and LiDAR 42. The waste product type determination unit 25 determines the type of picked-up industrial waste product (e.g., small electrical appliances, televisions, refrigerators, personal computers, etc.) according to the image signals obtained from the omnidirectional camera 41 and other imaging devices, using image analysis software. The image software may include learning data that includes the relationship between the appearance and type of numerous electrical products, and may determine the type according to the learning data. In addition to type determination by image analysis software, it may also include input of the results of visual inspection by an operator. The type can be classified by product type, but it can also be classified by its degree of danger, difficulty of recycling, whether it is a high-quality product or not, etc.

[0022] The landmark shape determination unit 26 detects the landmark 30 and determines its shape according to the image signals obtained from the omnidirectional camera 41 and LiDAR 42. The sorting area position storage unit 27 stores the position information of sorting areas DP1 to DPn according to the distance r and angle θ obtained by the distance / angle determination unit 24.

[0023] Referring to Figure 2, the configuration of the transport vehicle 20 will be further explained. The transport vehicle 20 is configured to move between the collection area CP and the sorting area DP by means of wheels attached to the main body and a control unit that controls them. As an example, the transport vehicle 20 is equipped with an omnidirectional camera 41 and a LiDAR 42, as well as a robot arm 43, a small waste product sorting unit 44, a first waste product storage unit 45, and a second waste product storage unit 46.

[0024] The robot arm 43 is equipped with a gripping section for grasping industrial waste products and an arm section for moving the gripping section in three dimensions, and picks up industrial waste products from the collection area CP. The small waste product sorting section 44 has the function of grasping, placing, or sorting small waste products (e.g., hair dryers, shavers, smartphones, etc.) from among the industrial waste products. The first waste product storage section 45 and the second waste product storage section 46 are storage sections for storing waste products sorted by the small waste product sorting section 44. The first waste product storage section 45 and the second waste product storage section 46 can store multiple small industrial waste products, thereby making transportation to the sorting area DP more efficient.

[0025] Referring to Figure 3, an example of the structure of the landmark 30 will be explained. As mentioned above, the landmark 30 is an identifier placed in multiple sorting areas DP for separating industrial waste products, which are the objects to be sorted, and is used to identify the multiple sorting areas DP1 to DPn. Different three-dimensional landmarks 30 are placed in each sorting area DP1 to DPn, and these are detected by the omnidirectional camera 41 and LiDAR 42 of the transport vehicle 20, allowing the transport vehicle 20 to identify the sorting areas DP1 to DPn and transport the transport vehicle 20 to the corresponding sorting areas DP1 to DPn.

[0026] In order to give each of the multiple landmarks 30 a different three-dimensional shape, in this embodiment, the landmarks 30 are constructed by appropriately combining multiple types (for example, three types) of unit structures 31 to 33 and stacking them on a base 34, for example. Connections between the unit structures 31 to 33 can be made by insertion ribs or insertion holes (not shown). It is also possible to make the landmark 30 from which the unit structures 31 to 33 are integrally molded.

[0027] As shown in Figure 3, the unit structures 31 to 33 can have three-dimensional shapes such as cones, inverted cones, and spheres, which appear substantially the same shape (two-dimensional shape in the image) when viewed from any direction of 360°. For example, the conical unit structure 31 appears triangular when viewed from any direction of 360°, with only its size varying depending on the distance. The same applies to inverted cones and spheres. However, the unit structures 31 to 33 are not limited to these shapes; similar effects can be obtained as long as they have a shape that is substantially rotationally symmetric about the vertical axis. For example, in addition to cones, inverted cones, and spheres, unit structures such as cylinders, ellipsoids, and polygonal pyramids (preferably with 6 or more sides) can also be used. In the case of a cone, the inclined surface does not have to be a straight line; for example, it can be a curved surface.

[0028] By appropriately combining these multiple types of unit structures 31 to 33, a large number of landmarks 30 with different three-dimensional shapes can be constructed. Each of the unit structures 31 to 33 can be assigned a different identification number (for example, 0, 1, 2).

[0029] The combination of unit structures 31 to 33 included in a single landmark 30 is arbitrary in both order and type, and multiple unit structures of the same type may be included in a single landmark 30. For example, as shown in Figure 4, when any three of the three types of unit structures 31 to 33 are appropriately combined to construct a landmark 30, 3 × 3 × 3 = 27 different shapes of landmark 30 can be constructed. In Figure 4, landmark IDs (identification numbers) 0 to 26 are assigned to each variation of the combination. Information showing the relationship between these landmark IDs 0 to 26 and the combination of the three unit structures 31 to 33 is stored in the sorting area data storage unit 11. In this first embodiment, all unit structures 31 to 33 have the same color (here, red), and therefore, all landmarks 30 are composed of a single color (here, one color, red).

[0030] Note that the configuration method of the landmark 30 shown in Figures 3 and 4 is just one example, and the types of unit structures are not limited to three; there may be two, or four or more. Also, the number of unit structures included in one landmark 30 is not limited to three; for example, it is possible to construct a landmark 30 by combining m units of n types of unit structures (in which case, (n × m) types of landmark 30 can be constructed). Furthermore, the number of unit structures may differ among multiple types of landmark 30 used in a single system 1.

[0031] Figure 5 shows an example of a sorting area data storage unit 11 included in the server 10. The sorting area data storage unit 11 stores the serial numbers 1 to n of sorting areas DP1 to DPn, landmark IDs (0 to 26) that identify the landmarks 30 placed in sorting areas DP1 to DPn, and the types of sorting targets (industrial waste products) to be separated into those sorting areas DP1 to DPn (e.g., small home appliances, televisions, refrigerators, personal computers, etc.). The data in the sorting area data storage unit 11 can be generated by appropriately placing landmarks 30 of different shapes (different landmark IDs) in sorting areas DP1 to DPn, and then inputting data from the management computer 50 that matches the placement.

[0032] Figure 6 shows an example of a sorting area location storage unit 27 included in the transport vehicle 20. The sorting area location storage unit 27 stores the location information (distance r, angle θ) of the detected landmarks 30 (sorting areas DP1 to DPn) in association with information that identifies the sorting areas DP1 to DPn (e.g., serial number, landmark ID, type of sorting target). In the example in Figure 1, the sorting area data storage unit 11 is included in the server 10 and the sorting area location storage unit 27 is included in the transport vehicle 20, but the mounting positions of the storage units 11 and 27 are arbitrary and can be changed as appropriate as long as similar operation can be performed. Alternatively, the sorting area data storage unit 11 and the sorting area location storage unit 27 may be integrated into a single storage unit.

[0033] Next, the operation of the industrial waste sorting system 1 of the first embodiment will be explained with reference to the flowchart in Figure 7. In this system 1, first, the omnidirectional camera 41 and LiDAR 42 of the transport vehicle 20 are used to detect landmarks 30 placed in the collection area CP and sorting area DP, and the positional relationship (distance r, angle θ) between the transport vehicle 20 and the collection area CP and sorting area DP is determined (step S10).

[0034] As mentioned above, the administrator of this system 1 places landmarks 30 in sorting areas DP1 to DPn, and stores the relationship between the shape of the landmarks 30 and the types of industrial waste products to be sorted in sorting areas DP1 to DPn in the sorting area data storage unit 11. Once the positional relationship (distance r, angle θ) between the transport vehicle 20, the accumulation area CP, and the sorting area DP is determined, that data is stored in the sorting area position storage unit 27. This allows the transport vehicle 20 to autonomously drive to the corresponding sorting area DP after determining the type of industrial waste product that has been picked up.

[0035] Once the transport vehicle 20 is ready for autonomous operation, it moves to the collection area CP and picks up industrial waste products as appropriate using the robotic arm 43 or the like (step S20). The movement to the collection area CP may be performed according to the already obtained position information of the collection area CP, or according to the image signals from the omnidirectional camera 41 and LiDAR 42.

[0036] The picked-up industrial waste products are imaged by the omnidirectional camera 41, and their type is detected by image analysis (step S30). Once the type is detected, pre-sorting processing according to the type is performed (step S40). For example, for waste products with power cables attached, the power cables are cut using a cutter (not shown). Subsequently, the location of the sorting area DP corresponding to the detected type is determined according to the information in the sorting area location storage unit 27, and the transport vehicle 20 is autonomously driven toward that sorting area, thereby transporting the industrial waste products to the corresponding sorting area DP and unloading them (step S50). The above operations This process is repeated for each industrial waste product.

[0037] The detection of landmark 30 will be explained with reference to Figure 8. In the detection of landmark 30, the transport vehicle 20 is placed, for example, at a reference position or initial position, and at that position, the omnidirectional camera 41 captures images in all directions, capturing an image that includes landmark 30. When capturing the image of landmark 30, the image information from the LiDAR 42 is also analyzed, and the distance r and angle θ to landmark 30 are measured.

[0038] As shown in Figure 9, when an image P1 containing the landmark 30 is captured by the omnidirectional camera 41, the image analysis unit 23 (or the image analysis unit 13 of the server 10) uses a threshold value previously set in the threshold setting unit 15 to binarize the image P1 and generate a binarized image P2, and extracts the image of the landmark 30. Then, it detects the contour of the extracted image of the landmark 30, and determines the combination of unit structures 31 to 33 contained in the landmark 30 based on the detected contour, thereby identifying the type of landmark 30 (landmark ID). In addition to the contour itself, the type of landmark can also be determined by the centroid of the closed surface defined by the contour, the aspect ratio, the ratio of the upper and lower areas, etc.

[0039] Furthermore, as explained in Figure 10, by superimposing the LiDAR point cloud image P6 obtained from LiDAR 42 onto the image obtained from the omnidirectional camera 41 to obtain a superimposed image P7, it becomes possible to more accurately determine the location of landmark 30. The location of landmark 30 can be more accurately determined by analyzing the image obtained from the omnidirectional camera 41 to obtain the detection position of landmark 30 and the nearest nearest laser image that is closest to that detection position.

[0040] Referring to Figures 11 to 13, the method for setting the threshold used to detect landmark 30 will be explained. The industrial waste collection area CP and sorting area DP may be installed outdoors, and some sorting areas DP may be indoors while others are outdoors. Thus, the images obtained by the omnidirectional camera 41 in this system 1 can be both indoor and outdoor images, and their brightness and darkness will vary depending on the situation.

[0041] Therefore, in System 1 of this embodiment, as shown in Figure 11(a), the shooting environment is switched in multiple ways: (1) bright outdoors (such as a sunny day), (2) bright indoors, (3) dark indoors, and (4) dark outdoors (such as at night). In each situation, landmarks 30 located at different distances and directions from the omnidirectional camera 41 (for example, positions b, e, h in the 0° direction, c, f, i in the +45° direction, and a, d, g in the -45° direction) are imaged, and a threshold is determined according to the obtained image. Although Figure 11 only shows images taken in the 0° direction, it goes without saying that images taken in the ±45° directions may also be included.

[0042] Figure 12 shows the histograms of red (R), green (G), and blue (B) pixels when landmark 30 is imaged at different positions b, e, and h, switching between brightness and indoor / outdoor conditions (in the histogram, the horizontal axis represents the brightness of the pixels, and the vertical axis represents the proportion of such pixels). The histograms are generated by the histogram generation unit 14 described above.

[0043] Figure 13 shows the R / (R+B+G) histogram, the green G / (R+B+G) histogram, and the B / (R+B+G) histogram for each of the 12 (3×2×2) cases obtained by switching between outdoor / indoor and light / dark conditions at positions b, e, and h. Using image analysis software or visually by an administrator, the thresholds for distinguishing red, green, and blue can be determined from these histograms (in the case of histograms like those in Figure 13, the thresholds for red, green, and blue can be determined to be 44%, 32%, and 32%, respectively). By setting these thresholds in the threshold setting unit 15, it becomes possible to perform the binarization process described in Figure 9.

[0044] As described above, according to the industrial waste product sorting system 1 of the first embodiment, landmarks 30 of different shapes are placed in multiple sorting areas DP, and these are imaged by a transport vehicle 20 equipped with an omnidirectional camera 41, etc., so that the sorting area DP in which the industrial waste products to be sorted should be sorted can be identified and the transport vehicle 20 can be moved accordingly. Therefore, according to this system 1, it is possible to perform sorting of industrial waste products by the transport vehicle 20 with high precision without installing a positioning system such as GNSS.

[0045] [Second Embodiment] Next, a second embodiment of the industrial waste sorting system 1 will be described with reference to Figure 14. This second embodiment of the industrial waste sorting system 1 differs from the first embodiment in the form of the landmark 30. In this second embodiment, each of the unit structures 31 to 33 is given multiple color options (for example, red, green, and blue), thereby increasing the variations of the unit structure 31, which is different from the first embodiment. In the example in Figure 14, three types of unit structures 31 to 33 are given three color options, and a total of nine types of unit structures are provided. In this case, the landmark shape discrimination unit 26 can be configured to discriminate not only the shape but also the color of the unit structure. It is also possible to provide color options to only some of the unit structures 31 to 33. Furthermore, the number of color options may differ among the unit structures 31 to 33.

[0046] This disclosure is not limited to the embodiments described above, and includes various modifications. For example, the embodiments described above are described in detail for the purpose of explaining this disclosure clearly, and are not necessarily limited to having all the configurations described. Furthermore, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations. [Explanation of Symbols]

[0047] 1… Industrial waste product sorting system 10… Server 11... Sorting area data storage unit 12…Transport vehicle control unit 13…Image Analysis Department 14...Histogram generation section 15...Threshold setting section 16…Communication Control Unit 20... Transport vehicle 21...Movement Control Unit 22...Communications Control Unit 23…Image Analysis Department 24...Distance / angle determination section 25...Waste product type determination section 26... Landmark shape discrimination unit 27... Sorting area position storage unit 30… Landmark 31-33... Unit Structures 41... Omnidirectional camera 42…LiDAR 43…Robot arm 44…Small waste product sorting department 45...First waste product storage section 46... Second waste product storage section 50…Administrative computer CP…Integrated Area DP1~DPn…sorting area

Claims

1. In a sorting system for selecting items to be sorted, A transport vehicle for transporting the aforementioned sorting targets, An identifier arranged in a plurality of sorting areas for separating the objects to be sorted, and for identifying the plurality of sorting areas, the identifier has a three-dimensional shape that appears to be the same two-dimensional shape when viewed from different horizontal directions, An imaging device capable of capturing an image of the entire identifying object, Equipped with, A sorting system for objects, wherein the transport vehicle is configured to identify the type of object to be sorted, identify the sorting area to which the object should be sorted based on the image of the identified object captured by the imaging device, and transport the object to be sorted to the identified sorting area.

2. The sorting system for objects to be sorted according to claim 1, wherein the identifying body comprises a plurality of different types of unit structures having different shapes from each other.

3. The sorting system for sorting objects according to claim 2, wherein the multiple types of unit structures have the same color.

4. The sorting system for objects according to claim 2, wherein the multiple types of unit structures have different colors from each other.

5. The sorting system for objects to be sorted according to any one of claims 1 to 4, wherein the imaging device is configured to capture images in all directions.

6. It is arranged in multiple sorting areas for separating items to be sorted and is configured to identify the multiple sorting areas, An identifier having a three-dimensional shape that appears to be the same two-dimensional shape when viewed from different horizontal directions, The identifier is characterized in that the plurality of identifiers have different three-dimensional shapes from each other.

7. The identifier according to claim 6, which is composed of a combination of multiple types of unit structures having different shapes from each other.

8. The identifier according to claim 7, wherein the multiple types of unit structures have the same color.

9. The identifier according to claim 7, wherein the multiple types of unit structures have different colors from each other.

Images