Location tracking system

Abstract

To specify an edge position in an X-axis coordinate of an object adjacent to a cargo storage space in a relatively simple and versatile manner to determine a cargo handling position in the X-axis coordinate.SOLUTION: A position specifying system includes a point cloud acquisition unit 22 that horizontally emits light onto a space above a loading platform Ta to acquire a point cloud PG, an analysis unit, and a position specifying unit. The analysis unit analyzes the acquired point cloud PG by using a frequency distribution with a distance in an X-axis direction as an axis. Based on analysis results of the point cloud PG, the position specifying unit specifies an area D2 with virtually no frequency as a loading space, and specifies sections S1 and S2 adjacent to the loading space with a frequency equal to or greater than a predetermined value as an edge position in an X-axis coordinate of an object adjacent to the loading space.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to a position identification system for identifying the position of an edge of an object at a loading / unloading position of a transport vehicle.

Background Art

[0002] As disclosed in Patent Document 1, an automated guided vehicle that autonomously travels and performs loading / unloading operations is known. This type of automated guided vehicle includes forks, a lifting device for raising and lowering the forks, and a laser scanner for detecting the position of the vehicle itself. The automated guided vehicle is configured to move to a predetermined loading / unloading position while detecting its own position, and to perform loading / unloading operations by raising and lowering the forks.

[0003] By the way, as shown in FIG. 10, this type of automated guided vehicle 100 may perform loading / unloading operations on a loading platform Ta of a truck T or the like. However, unlike a fixed shelf or the like, the truck T may not stop at a fixed position. Therefore, the automated guided vehicle 100 cannot define the loading / unloading position in advance. In addition, since the length of the loading platform Ta of the truck T varies depending on the vehicle type, even if it stops at a fixed position, the loading / unloading position will be different for each truck T. Furthermore, in order to effectively utilize the loading platform Ta of the truck T, it is necessary to stack the loads L. For this purpose, the position of the load L already stacked on the loading platform Ta must be identified first in order to determine the position of the next load L to be placed.

[0004] For example, there is an automated guided vehicle (forklift) as disclosed in Reference 2. This automated guided vehicle is equipped with an external sensor that detects the position of an object in a 3D coordinate system, and (1) extracts points representing the horizontal plane from point cloud data, which is a set of points representing the position of an object, (2) extracts points within a predetermined range in the vertical direction from the horizontal plane as points representing the loading platform, and (3) extracts points representing the edge of the loading platform from the points representing the loading platform. Furthermore, this automated guided vehicle is configured to (4) detect a straight line representing the edge from the points representing the edge, (5) extract points that are more than a predetermined distance above the horizontal plane as points representing the load loaded on the loading platform, and (6) detect a position that is a predetermined distance from the load in the direction in which the straight line extends as the loading position for loading the load on the loading platform.

[0005] However, object detection (extraction) methods using this type of point cloud data involve processes such as clustering (distinguishing multiple point clouds into certain sets) and pattern matching (for example, extracting linear sections). This method requires high accuracy (high resolution of the point cloud data), and as a result, it necessitates the use of a computer with high data processing capabilities. Furthermore, this method requires adjusting the judgment algorithm and pattern for each object, making it difficult to achieve general applicability. [Prior art documents] [Patent Documents]

[0006] [Patent Document 1] Japanese Patent Publication No. 2020-030642 [Patent Document 2] Japanese Patent Publication No. 2023-030983 [Overview of the project] [Problems that the invention aims to solve]

[0007] Incidentally, as shown in Figure 10A, the truck T to be loaded and unloaded is usually parked in a predetermined orientation in the parking area SE. Also, the width W of the load L can be the width of a predetermined pallet on which the load is placed. Therefore, if the edge position Ex on the X axis of an object adjacent to the loading space (for example, load L) can be identified, the loading and unloading position LPx in X-axis coordinates at the time of loading can be determined by setting the position at a distance of half the width W from that edge position Ex in the opposite direction of the object. Also, as shown in Figure 10B, if the edge position Ex on the X axis of load L placed on the loading platform Ta can be identified, the loading and unloading position LPx in X-axis coordinates at the time of unloading can be determined by setting the position at a distance of half the width W from that edge position Ex toward the center of load L. Note that, in order to simplify the explanation in Figure 10, the width W of load L is used as the width of the pallet, but the width W of load L does not have to be the same as the width of the pallet. For example, even if the width W of the load L is longer or shorter than the width of the pallet, the loading / unloading position LPx can be determined if the width W of the load L is known or within a predetermined range.

[0008] Therefore, the problem that the present invention aims to solve is to provide a positioning system that can determine the edge position in the X-axis coordinates of an object adjacent to a loading space in a relatively simple and versatile manner, in order to determine the loading and unloading position in the X-axis coordinates. [Means for solving the problem]

[0009] To solve the above problems, the location identification system according to the present invention is A positioning system used in a transport vehicle, comprising: a point cloud acquisition unit that acquires a point cloud by irradiating light horizontally into the loading space; an analysis unit that analyzes the acquired point cloud using a frequency distribution with distance in the X-axis direction as the axis; and a positioning unit that, based on the analysis results of the point cloud, identifies a region with substantially no frequency as a loading space, and identifies a section adjacent to the loading space with a frequency of a predetermined level or higher as the position of the edge in the X-axis coordinate of an object adjacent to the loading space. In this invention, "horizontal" includes angles that are not perpendicular to the direction of gravity, such as when there is a slope in the ground.

[0010] The above-mentioned location identification system is preferably, The position identification unit identifies a section as the edge position in the X-axis coordinates of an object adjacent to the loading space only when the position of the point cloud acquisition unit is included in a region with substantially no degree on the X-axis, and there are sections with a predetermined or greater degree adjacent to this region with substantially no degree.

[0011] The above-mentioned location identification system is preferably, The system further includes a cargo handling target determination unit that determines objects adjacent to the cargo storage space as objects to be handled.

[0012] The above-mentioned location identification system is preferably, The positioning unit does not identify areas less than a predetermined distance from the region on the X-axis that is substantially without frequency as a loading space.

[0013] The above-mentioned location identification system is preferably, The point cloud acquisition unit illuminates the surrounding space, which includes the loading space and is wider horizontally than the loading space, with light projected horizontally.

[0014] The above-mentioned location identification system is preferably, The position identification unit identifies a substantially zero-degree region adjacent to the center of the loading space among multiple regions with degrees on the X-axis as a loading space, and identifies the position of the other section with a predetermined or higher degree adjacent to the identified loading space on the X-axis as a position related to the loading and unloading position of the transport vehicle.

[0015] The above-mentioned location identification system is preferably, The position identification unit identifies a substantially zero-degree area adjacent to the center of the loading space of the outermost region among multiple regions with degrees on the X axis as the loading space, and identifies the position of the section with a predetermined or higher degree within that outermost region as the position related to the loading and unloading position of the transport vehicle.

[0016] To solve the above problems, the transport vehicle according to the present invention is equipped with a location identification system as described in any of the above.

[0017] In order to solve the above problems, the position identification method according to the present invention is A position identification method used for a transport vehicle, including obtaining a point cloud by horizontally irradiating light into a loading space by a point cloud acquisition unit, analyzing the obtained point cloud using a frequency distribution with the distance in the X-axis direction as the axis, identifying a region with substantially no frequency as a loading space based on the analysis result of the point cloud, and identifying an interval with a predetermined or more frequency adjacent to the loading space as the position of an edge in the X-axis coordinate of an object adjacent to the loading space.

[0018] The above position identification method preferably includes the position of the point cloud acquisition unit in a region on the X-axis with substantially no frequency, and only when there are intervals with a predetermined or more frequency on both sides of the region with substantially no frequency at this time, identify the interval as the position of the edge in the X-axis of an object adjacent to the loading space.

[0019] The above position identification method preferably further includes determining an object adjacent to the loading space as a loading / unloading target.

[0020] The above position identification method preferably does not identify a region with a distance less than a predetermined distance in the region on the X-axis with substantially no frequency as a loading space.

[0021] The above position identification method preferably horizontally irradiates light into a surrounding space that includes the loading space and is wider in the horizontal direction than the loading space to obtain a point cloud, on the X-axis, identify a region with substantially no frequency adjacent to the center side of the loading space in the outermost region among a plurality of regions with frequency as a loading space, on the X-axis, identify the position of another interval with a predetermined or more frequency adjacent to the identified loading space as the position related to the loading / unloading position of the transport vehicle.

[0022] The above position identification method preferably By irradiating the surrounding space, which includes the loading space and is wider horizontally than the loading space, with light to acquire a point cloud, On the X-axis, among the regions with multiple frequencies, the region adjacent to the center of the loading space, which is essentially a region with no frequencies, is identified as the loading space. The location of the section with a degree above a predetermined level in the outermost region is identified as the location related to the loading and unloading position of the transport vehicle.

[0023] To solve the above problems, the location identification program according to the present invention is A program used in a transport vehicle equipped with a point cloud acquisition unit that acquires a point cloud by horizontally irradiating light into the loading space, and a computer, On the computer, The acquired point cloud is analyzed using a frequency distribution with distance along the X-axis as the axis, Based on the point cloud analysis results, we identify areas with virtually no frequency as storage spaces, The system is configured to identify a section with a certain degree or higher adjacent to the loading space as the edge position in the X-axis coordinates of an object adjacent to the loading space. [Effects of the Invention]

[0024] The positioning system according to the present invention can identify the edge position in the X-axis coordinates of an object adjacent to a storage space in a relatively simple and versatile manner. [Brief explanation of the drawing]

[0025] [Figure 1] This is a plan view showing a truck and an automated guided vehicle according to one embodiment of the present invention. [Figure 2] Figure 1 is a side view showing the automated guided vehicle. [Figure 3] This is a block diagram of an automated guided vehicle (AGV). [Figure 4] Figure 2 shows the connecting section, where A is a perspective view from above on the rear, B is a plan view, and C is a rear view. [Figure 5]A is a plan view showing the laser irradiation of the 2D LiDAR sensor on the right, B is a diagram showing the point cloud acquired by the 2D LiDAR sensor on the right, C is a diagram showing the masking process of the point cloud in B within a predetermined range, and D is a diagram showing the point cloud in C as a histogram in the X-axis direction. [Figure 6] A shows the point cloud data obtained when the point cloud was acquired at the rear of the truck, and B shows the point cloud data obtained when the point cloud was acquired at the side of the truck's cab. [Figure 7] A is a plan view showing a loading platform with protective materials and pallets arranged on it, B is a diagram showing the point cloud acquired by a 2D LiDAR sensor on the right side of the loading platform in A, and C is a diagram showing the point cloud in B as a histogram in the X-axis direction. [Figure 8] A is a plan view showing a loading platform with cylindrical members arranged on it, B is a diagram showing the point cloud acquired by a 2D LiDAR sensor to the right of the loading platform in A, and C is a diagram showing the point cloud in B as a histogram in the X-axis direction. [Figure 9] This is a flowchart showing the operation flow of an automated guided vehicle (AGV). [Figure 10] This is a plan view showing a conventional automated guided vehicle and the cargo bed of a truck. [Modes for carrying out the invention]

[0026] Hereinafter, an embodiment of the positioning system and a transport vehicle equipped with the positioning system of the present invention will be described with reference to the attached figures. In the figures, the double arrow X indicates the front-to-back direction (X-axis), the double arrow Y indicates the left-to-right direction, and the double arrow Z indicates the up-to-down direction.

[0027] Figure 1 is a plan view showing a truck T and a transport vehicle 1 according to this embodiment. As shown in Figure 1, the truck T has a cargo bed Ta at its rear and parks in a parking area SE enclosed by a white line WL. The space on the cargo bed Ta corresponds to the "loading space" of the present invention, and the space on the parking area SE corresponds to the "surrounding space" of the present invention. However, these are merely examples, and the location where cargo handling is performed in the present invention is not limited to the cargo bed Ta of the truck T. For example, if cargo handling is performed on shelves or containers, the space on the shelves or containers corresponds to the "loading space" of the present invention, and the surrounding space including these shelves or containers corresponds to the "surrounding space" of the present invention.

[0028] In this embodiment, the transport vehicle 1 is configured to acquire point cloud data PG while traveling along the left and right sides of the truck T from the rear to the front of the truck T, and to perform cargo handling operations at the cargo handling position LPx determined based on the acquired point cloud data PG. In this embodiment, the transport vehicle 1 is configured to place cargo from the front space of the loading platform Ta when loading cargo, and to pick up cargo from the rear cargo L of the multiple cargoes L loaded on the loading platform Ta when picking up cargo. The transport vehicle 1 in this embodiment is an autonomous transport vehicle that drives and handles cargo autonomously, but this is merely an example, and the transport vehicle according to the present invention is not limited to this. For example, the transport vehicle according to the present invention may be a transport vehicle that can be used both with and without a driver.

[0029] <Transport vehicle configuration> Figure 2 is a side view of the transport vehicle 1, and Figure 3 is a block diagram of the transport vehicle 1. As shown in Figures 2 and 3, the transport vehicle 1 comprises a plurality of wheels 10, a body 11, a drive unit 12, a laser scanner 13, left and right masts 14, a lift bracket 15, left and right forks 16, a lifting unit 17, a backrest 18, left and right two-dimensional LiDAR sensors 22, left and right connecting units 23, and a control unit 30. The transport vehicle 1 is a reach forklift, but this is merely one example, and the transport vehicle 1 according to the present invention may also be a counterbalanced forklift.

[0030] The vehicle body 11 is positioned above the wheels 10, and the drive unit 12 is located inside the vehicle body 11. The drive unit 12 is configured to rotate and stop the wheels 10.

[0031] The laser scanner 13 is positioned above the vehicle body 11 and rotates horizontally to emit a laser beam while scanning the reflected laser light.

[0032] The left and right masts 14 extend vertically and are positioned at the rear of the vehicle body 11. The lift bracket 15 has finger bars for fixing the left and right forks 16 and is configured to be raised and lowered along the left and right masts 14 by the lifting section 17. In this embodiment, the number of forks 16 consists of four, but it can be two or six and is not particularly limited.

[0033] The backrest 18 is formed in a frame shape and extends vertically and horizontally, and is configured to support the loaded cargo L. In Figure 4, only the outer frame of the backrest 18 is shown, and this outer frame is positioned outward in the left-right direction from the vehicle body 11.

[0034] The left and right 2D LiDAR sensors 22 are composed of laser scanners that rotate horizontally while emitting a laser, and are configured to scan the reflected laser light to acquire the distance to the object irradiated with the laser in a point cloud PG. The 2D LiDAR sensors 22 correspond to the "point cloud acquisition unit" of the present invention. The point cloud acquisition unit according to the present invention may be a 3D LiDAR sensor or a 3D ToF (Time of Flight) camera instead of the 2D LiDAR sensors 22, and is not limited to a 2D LiDAR sensor. Thus, "light" in the present invention includes not only visible light but also invisible light.

[0035] Figure 4 shows the connecting section 23, where A is a perspective view from above on the rear side (fork 16 side), B is a plan view, and C is a rear view. The connecting section 23 has a first end 23a, an intermediate section 23b, and a second end 23c. The first end 23a is fixed to the left and right ends of the backrest 18, and the intermediate section 23b extends diagonally forward from the first end 23a to the backrest 18 in a plan view. The second end 23c has a horizontal plane that is continuous with the intermediate section 23b, and the two-dimensional LiDAR sensor 22 is supported by this horizontal plane. The position of the second end 23c is configured to be located outward in the left and right directions from the load L loaded on the fork 16 and the sides of the vehicle body 11. As a result, the laser of the two-dimensional LiDAR sensor 22 is not obstructed by the loaded load L or the vehicle body 11, and is directed toward the parking area SE including the loading platform Ta.

[0036] Figure 5A shows an example of the laser irradiation range LE of the 2D LiDAR sensor 22, as well as the direction of travel of the transport vehicle 1. Note that the laser irradiation range LE is not limited to this range; the laser may be irradiated to reach the entire stopping area SE. As shown in Figure 5A, the transport vehicle 1 travels along the side of the truck T from rear to front, and the 2D LiDAR sensor 22 rotates horizontally, irradiating with a laser and receiving reflected light to acquire the distance to the object at each irradiation angle. This distance data is acquired as a point cloud PG, as shown in Figure 5B. The intersection of the X and Y axes in Figure 5B indicates the position of the 2D LiDAR, which is the origin X0. Note that the point cloud PG in the attached figure is an illustrative diagram to show an example of the acquired point cloud PG, and is not the actual acquired point cloud PG.

[0037] In this invention, the acquisition of the point cloud PG by the 2D LiDAR sensor 22 may be performed while the transport vehicle 1 is stopped, and the transport vehicle 1 does not necessarily need to be running parallel to the truck T in order to acquire the point cloud PG.

[0038] Naturally, if the absolute position coordinates of the transport vehicle 1 can be recognized using known technology, the absolute position coordinates of the 2D LiDAR sensor 22 can also be determined, and the position of the acquired point cloud PG can also be determined on the absolute coordinate axis. In other words, Figure 5B is also a diagram showing the positional relationship between the point cloud PG shown on the absolute coordinate axis and the current position of the transport vehicle 1 relative to that point cloud PG.

[0039] As shown in Figure 2, the control unit 30 is located inside the vehicle body 11. The control unit 30 is composed of a computer having a storage device, an arithmetic unit, and memory. The storage device stores a location identification program that causes the computer to execute an edge location identification method.

[0040] <Functional configuration of the control unit> Next, the functional configuration of the control unit 30 will be described. As shown in Figure 3, the control unit 30 includes a storage unit 301, a self-position recognition unit 302, an analysis unit 303, a position determination unit 305, a cargo handling target determination unit 306, a cargo handling position determination unit 307, a travel control unit 308, and a lifting / lowering control unit 309. A system comprising a 2D LiDAR sensor 22 (point cloud acquisition unit), an analysis unit 303, a position determination unit 305, and a cargo handling target determination unit 306 corresponds to the "position determination system" of the present invention.

[0041] The memory unit 301 stores the position (X coordinate, Y coordinate) of the parking area SE, the orientation of the parked truck T (either front or rear), the width W of the load L, and the height to which the forks 16 are raised during loading and unloading.

[0042] The self-position recognition unit 302 recognizes the current position of the transport vehicle 1 by detecting the position of reflectors placed within the facility from the reflected light scanned by the laser scanner 13.

[0043] The analysis unit 303 analyzes the acquired point cloud PG using a frequency distribution with distance in the X-axis direction as the axis. Specifically, as shown in Figure 5C, the analysis unit 303 first limits the acquired point cloud PG to the ROI (Region of Internet) indicated by the dashed line in Figure 5. In this way, the analysis unit 303 eliminates unnecessary point cloud PG. Next, as shown in Figure 5D, the analysis unit 303 analyzes the point cloud PG within the ROI using a frequency distribution with distance in the X-axis direction from the origin X0 in the X-axis direction (forward / backward direction).

[0044] In Figure 5D, region D2, which has no frequency, indicates a region where the laser reflection by the 2D LiDAR sensor 22 is extremely low or absent compared to other regions. In this invention, "substantially frequency-free" means excluding cases where there is frequency in a region that is otherwise empty due to noise, etc. The analysis unit 303 may remove frequencies due to noise, etc., or ignore low frequencies, using known techniques. Hereafter, the phrase "substantially frequency-free" will be abbreviated to "frequency-free".

[0045] Since each interval has a numerical range, the average value of the values ​​in each interval may be used as the position (X coordinate) of that interval, or the minimum or maximum value in each interval may be used as the position (X coordinate) of that interval.

[0046] Based on the analysis results of the point cloud PG, the position identification unit 305 identifies the region D2 without a degree as a loading space, as shown in Figure 5D, and identifies the sections S1 and S2 adjacent to the loading space that have a degree of a predetermined value or higher as the edge positions of the object adjacent to the loading space in the X-axis coordinates. By setting the degree to "a predetermined value or higher," the position identification unit 305 can appropriately identify the section related to the edge of the object on the X-axis from among the sections of the region D1 and D2 that have a degree. The minimum degree for identifying the edge position is set appropriately in advance so as to identify the section of the object's edge without mistakenly recognizing other sections as edge sections.

[0047] Preferably, the position identification unit 305 identifies the sections S1 and S2 as the edge positions in the X-axis coordinates of an object adjacent to the loading space only when the position of the 2D LiDAR sensor 22 (origin X0) is included in a region D2 on the X-axis that has no degree, and when there are sections S1 and S2 with a degree of a predetermined or higher adjacent to the region D2 that has no degree.

[0048] This prevents the positioning unit 305 from mistakenly identifying mere empty space as a cargo storage space. For example, since the parking area SE is long enough to accommodate the entire length of the truck T, it is assumed that there is space in front of and behind the parked truck T. The 2D LiDAR sensor 22 does not acquire point cloud PG from the space in this area. Therefore, the positioning unit 305 will not mistakenly identify mere empty space in front of and behind the truck T as a cargo storage space. As an example of misidentification, the parked position of the truck T extends far behind the parking area SE, and as a result, the cab portion of the truck T is recognized as the outermost region D1 on the side where it starts moving, and the mere space in front of the cab is mistakenly identified as a cargo storage space. Even in such cases, the position identification unit 305 identifies sections S1 and S2 as the edge positions in the X-axis coordinates of an object adjacent to the loading space only if the position of the 2D LiDAR sensor 22 (origin X0) is included in the region D2 without a degree, and there are sections S1 and S2 with a degree of a predetermined or higher on both sides of the region D2. In other words, the position identification unit 305 identifies the region D2 without a degree as a loading space only if the position of the 2D LiDAR sensor 22 (origin X0) is included in the region D2 without a degree, and there are sections S1 and S2 with a degree of a predetermined or higher on both sides of the region D2. In this way, the position identification unit 305 prevents misidentification of mere space as a loading space.

[0049] Furthermore, with reference to Figure 6, the effectiveness of the edge positioning method by the positioning unit 305 will be explained. Figure 6A shows the point cloud PG data when the point cloud PG is acquired at a position behind the truck T, and Figure 6B shows the point cloud PG data when the position of the 2D LiDAR sensor 22 is to the side of the truck T's cab. As shown in Figures 6A and 6B, gaps in the point cloud PG occur in the region of the point cloud PG relating to objects that are in a blind spot from the position of the 2D LiDAR sensor 22. However, as shown in Figure 5D, the positioning unit 305 identifies the region D2 without a degree as a cargo space only when the origin X0 is included in the region D2 without a degree, and there are sections S1 and S2 with a degree of a predetermined or higher on both sides of the region D2. Therefore, it does not misidentify the apparent gaps in the point cloud PG relating to the blind spot as cargo spaces.

[0050] In particular, as shown in Figure 5D, the position identification unit 305 (1) identifies a region D2 on the X-axis that has no degrees and is adjacent to the center of the loading space of the outermost region D1 on the side where travel for point cloud acquisition begins, among multiple regions with degrees, as the loading space, and (2) then identifies the position of a section S2 on the X-axis that has degrees above a predetermined level in the other region D3 (on the travel direction side) adjacent to the loading space as the position Ex related to the loading position LPx of the transport vehicle 1. This further prevents the position identification unit 305 from mistakenly recognizing the gaps in the apparent point cloud PG in Figures 6A and 6B as the loading space.

[0051] The cargo handling target determination unit 306 determines, during cargo retrieval, to be the object on the travel direction side (front side) of the two objects adjacent to the cargo storage space on the X-axis. In another embodiment, if the setting is to retrieve multiple loads L loaded on the cargo bed Ta in order from the front load L, the cargo handling target determination unit 306 determines to be the object on the opposite side (rear side) of the travel direction on the X-axis of the two objects adjacent to the cargo storage space on the X-axis.

[0052] The cargo handling position determination unit 307 determines the cargo handling position LPx in the X-axis coordinate system at a distance of half the width W of the cargo L, in the opposite direction of travel from position Ex, when loading cargo. Similarly, when unloading cargo, the cargo handling position determination unit 307 determines the cargo handling position LPx in the X-axis coordinate system at a distance of half the width W, in the direction of travel from position Ex. The cargo handling position determination unit 307 determines the Y-coordinate by referring to the Y-coordinate of the stopping area SE stored in the memory unit 301. Alternatively, the cargo handling position determination unit 307 may determine the Y-coordinate by other known techniques, and is not particularly limited to the method of determining the Y-coordinate of the cargo handling position.

[0053] The travel control unit 308 is configured to control the drive unit 12. When the cargo handling position determination unit 307 determines the cargo handling position LPx, the travel control unit 308 drives the transport vehicle 1 to the cargo handling position LPx while referring to the current position acquired by the laser scanner 13.

[0054] The lifting control unit 309 is configured to control the lifting unit 17, and the lifting unit 17 raises the forks 16 to the height used during cargo handling, which is stored in the memory unit 301, allowing the transport vehicle 1 to perform cargo handling operations.

[0055] In this way, the transport vehicle 1 can identify the edge position Ex on the X-axis of an object adjacent to the loading space by analyzing the point cloud PG acquired by the 2D LiDAR sensor 22 using a frequency distribution. This allows the transport vehicle 1 to identify the edge position Ex relatively easily and versatility and determine the loading / unloading position LPx. Note that the histogram in Figure 5D is for the purpose of explaining the frequency distribution in this specification, and the control unit 30 does not particularly need to create a histogram.

[0056] Figures 7 and 8 show another example in which the edge position Ex can be obtained by frequency distribution analysis using the transport vehicle 1.

[0057] Figure 7A is a plan view showing an example where a pallet P is loaded on the front of the loading platform Ta as a spacer, and protective material PM is placed on the rear of the loading platform Ta. In this case as well, the transport vehicle 1 acquires a point cloud PG using the 2D LiDAR sensor 22 as shown in Figure 7B, and analyzes it using the frequency distribution as shown in Figure 7C to appropriately identify the loading space. Moreover, the transport vehicle 1 appropriately identifies the edge positions of the pallet P and the protective material PM at the front and rear of the loading space as the edge positions of the objects adjacent to the loading space. As a result, the transport vehicle 1 can appropriately place the load L in the loading space even when irregularly shaped obstacles are placed on the loading platform Ta.

[0058] Figure 8A is a plan view showing an example where a cylindrical member RM is placed at the front of the loading platform Ta. In this case as well, the transport vehicle 1 acquires a point cloud PG as shown in Figure 8B and analyzes it using a frequency distribution as shown in Figure 8C to appropriately identify the loading space. Furthermore, the transport vehicle 1 appropriately identifies the edge position of the cylindrical member RM in front of the loading space as the edge position of the object adjacent to the loading space in front. As a result, the transport vehicle 1 can appropriately place the load L in the loading space even when an obstacle with a rounded side is placed on the loading platform Ta.

[0059] <Flowchart of the transport vehicle's operation> Next, with reference to Figure 9, the operation flow of the transport vehicle 1 according to this embodiment will be described.

[0060] (1) The transport vehicle 1 first travels from the rear to the front of the truck T, acquiring the point cloud PG of the stopping area SE (see S (step) 1 in Figure 9).

[0061] (2) Next, the transport vehicle 1 analyzes the acquired point cloud PG using a frequency distribution (see S2 in Figure 9).

[0062] (3) Next, the transport vehicle 1 identifies the region D2 without a frequency (S3 in Figure 9) as a loading / unloading space if the position of the 2D LiDAR sensor 22 (origin X0) is included in the region D2 without a frequency, and if there are sections S1 and S2 with a frequency of a predetermined level or higher adjacent to the region D2 without a frequency (Yes).

[0063] (4) Next, the transport vehicle 1 identifies the position of a section S2 adjacent to the travel direction of the loading space that has a predetermined or greater degree as the position Ex related to the loading position LPx of the transport vehicle 1 (S5 in Figure 9).

[0064] (5) Next, when unloading (Yes in S6 of Figure 9), the transport vehicle 1 determines the loading / unloading position LPx to be at a distance of half the width W of the load L from the position in section S2 in the direction of travel (S7 in Figure 9), and when placing the load (No in S6 of Figure 9), it determines the loading / unloading position LPx to be at a distance of half the width W of the load L from the position in section S2 in the opposite direction of travel (S8 in Figure 9).

[0065] (6) Next, the transport vehicle 1 moves to the determined loading / unloading position LPx and performs the loading / unloading operation (S9 in Figure 9).

[0066] By operating in this manner, the transport vehicle 1 can relatively easily identify the edge position Ex on the X-axis of an object adjacent to the loading space, determine the loading / unloading position LPx, and autonomously perform loading / unloading operations. Furthermore, even if the length of the loading platform Ta differs for each truck T, or if the front and rear parking positions of the truck T differ for each driver, the transport vehicle 1 can reliably identify the edge position Ex and determine the loading / unloading position LPx.

[0067] Although one embodiment of the location identification system and transport vehicle equipped with the location identification system of the present invention has been described above, the present invention is not limited to the above embodiment. For example, the location identification system and transport vehicle according to the present invention may be implemented by each of the following modifications or by appropriately combining the following modifications.

[0068] <Variation>

[0069] (1) When the load L is to be placed from the rear of the loading platform Ta, the position identification unit 305 identifies a substantially zero-degree region D2 (see Figure 5D) adjacent to the center of the loading space of region D1 (see Figure 5D) on the side where travel begins, among multiple regions with degrees on the X axis, as the loading space, and then identifies the position of the section S1 (see Figure 5D) in region D2 with a degree of a predetermined level or higher as the position Ex related to the loading position LPx of the transport vehicle 1. Then, when unloading, the loading position determination unit 307 determines the loading position LPx to be a position half the width W of the load L from position Ex in the opposite direction of travel, and when placing the load, it determines the loading position LPx to be a position half the width W of the load L from position Ex in the direction of travel.

[0070] (2) The transport vehicle 1 may be configured to travel in the opposite direction to the above embodiment, i.e., from the front of the truck T, and acquire the point cloud PG. In this case, the position identification unit 305 identifies a substantially zero-degree region D2 (see Figure 5D) adjacent to the center of the loading space of the region D1 (see Figure 5D) at the outermost edge in the direction of travel, among multiple regions with degrees on the X axis, as the loading space, and then identifies the position of the other section S2 (see Figure 5D) with a predetermined or greater degree adjacent to the identified loading space as the position Ex related to the loading position LPx of the transport vehicle 1. The loading position determination unit 307 then determines the loading position LPx as a position at a distance of half the width W of the load L from position Ex in the opposite direction of travel when unloading, and as a position at a distance of half the width W of the load L from position Ex in the direction of travel when loading.

[0071] (3) The position identification unit 305 may be configured not to identify areas less than a predetermined distance from the region without a degree on the X axis as a loading space. This prevents the position identification unit 305 from identifying the region related to this gap as a loading space, for example, when a region without a degree is generated in the gap between the cab and the loading platform Ta. Furthermore, even in cases where there is a gap between members arranged on the loading platform Ta, for example as shown in Figure 8, the position identification unit 305 can prevent identifying this gap as a loading space.

[0072] (4) The transport vehicle 1 may be equipped with the point cloud acquisition unit 22 on only one side, either the left or the right. In this case, if the transport vehicle 1 has the point cloud acquisition unit 22 only on the right side, it can acquire the point cloud PG appropriately by traveling from the rear to the front of the truck T on the left side of the truck T, and from the front to the rear of the truck T on the right side of the truck T. [Explanation of symbols]

[0073] L load LE laser irradiation range LPx loading / unloading location P Palette PG point cloud PM curing material ROI (Region of Internet) RM cylindrical member SE Stopping Area T-Track Ta cargo bed WL white line W Load width 1. Transport vehicle 10 wheels 11 Car body 12 Drive unit 13. Laser Scanner 14 Mast 15 Lift Bracket 16 Forks 17 Lifting section 18 Backrest 22. 2D LiDAR sensor (point cloud acquisition unit) 23 Connecting part 23a First end 23b Middle part 23c 2nd end 30 Control Unit 301 Storage section 302 Self-position recognition unit 303 Analysis Department 305 Location identification part 306 Cargo Handling Target Determination Section 307 Cargo handling position determination unit 308 Driving Control Unit 309 Lifting control unit

Claims

1. A location identification system used in transport vehicles, A point cloud acquisition unit that horizontally illuminates the loading space with light to acquire a point cloud, An analysis unit analyzes the acquired point cloud using a frequency distribution with distance in the X-axis direction as the axis, A position identification system comprising: a position identification unit that, based on the analysis results of the point cloud, identifies a region with virtually no frequency as a loading space, and identifies a section adjacent to the loading space with a frequency of a predetermined level or higher as the position of the edge in the X-axis coordinate of an object adjacent to the loading space.

2. The position identification system according to claim 1, wherein the position identification unit identifies the section as the edge position in the X-axis coordinates of an object adjacent to the loading space only when the position of the point cloud acquisition unit is included in a region with substantially no degree on the X axis, and there are sections with a predetermined or greater degree adjacent to the region with substantially no degree at that time.

3. The location identification system according to claim 1, further comprising a cargo handling target determination unit that determines an object adjacent to the cargo storage space as a cargo handling target.

4. The position identification system according to claim 1, wherein the position identification unit does not identify an area less than a predetermined distance from a substantially non-degree region on the X-axis as the loading space.

5. The position identification system according to claim 1, wherein the point cloud acquisition unit irradiates light horizontally into the surrounding space which includes the loading space and is wider horizontally than the loading space.

6. The aforementioned position identification unit is On the X-axis, among the regions with multiple degrees, the region with substantially no degrees adjacent to the central side of the outermost region of the loading space is identified as the loading space. The position identification system according to claim 5, wherein the position of another section with a predetermined or greater degree adjacent to the identified loading space on the X axis is identified as the position related to the loading and unloading position of the transport vehicle.

7. The aforementioned position identification unit is The position identification system according to claim 5, wherein, on the X-axis, a substantially zero-degree region adjacent to the center side of the loading space of the outermost region among multiple regions with degrees is identified as the loading space, and the position of the section of the outermost region with a degree of a predetermined value or more is identified as the position related to the loading and unloading position of the transport vehicle.

8. A transport vehicle equipped with a positioning system according to any one of claims 1 to 7.

9. A method for determining the location of a transport vehicle, The point cloud acquisition unit horizontally illuminates the loading space with light to acquire a point cloud, The acquired point cloud is analyzed using a frequency distribution with distance in the X-axis direction as the axis, Based on the analysis results of the point cloud, a region with virtually no frequency is identified as a storage space, A method for determining a location, comprising: identifying a section adjacent to the loading space that has a predetermined or greater degree as the position of the edge in the X-axis coordinates of an object adjacent to the loading space.

10. The position identification method according to claim 9, wherein the position of the point cloud acquisition unit is included in a region with substantially no degree on the X axis, and only when there are sections with a predetermined or greater degree adjacent to the region with substantially no degree, the section is identified as the position of the edge in the X-axis coordinates of an object adjacent to the loading space.

11. The location identification method according to claim 9, further comprising determining an object adjacent to the storage space as the object to be picked up.

12. The location identification method according to claim 9, wherein a region less than a predetermined distance from a region with virtually no frequency on the X-axis is not designated as the loading space.

13. The point cloud is acquired by irradiating light horizontally into the surrounding space, which includes the aforementioned loading space and is wider horizontally than the aforementioned loading space. On the X-axis, among the regions with multiple degrees, the region with substantially no degrees adjacent to the central side of the outermost region of the loading space is identified as the loading space. The position identification method according to claim 9, wherein the position of another section with a predetermined or greater degree adjacent to the identified loading space on the X axis is identified as the position related to the loading and unloading position of the transport vehicle.

14. The point cloud is acquired by irradiating light horizontally into the surrounding space, which includes the aforementioned loading space and is wider horizontally than the aforementioned loading space. The position identification method according to claim 9, wherein, on the X-axis, a substantially zero-degree region adjacent to the center side of the loading space of the outermost region among a plurality of regions with degrees is identified as the loading space, and the position of the section of the outermost region with a degree of a predetermined or higher is identified as the position related to the loading and unloading position of the transport vehicle.

15. A point cloud acquisition unit that horizontally illuminates the loading space with light to acquire a point cloud, A program used in a transport vehicle equipped with a computer, To the aforementioned computer, The acquired point cloud is analyzed using a frequency distribution with distance in the X-axis direction as the axis, Based on the analysis results of the point cloud, a region with virtually no frequency is identified as a storage space, A position identification program that performs the following: identifying a section with a predetermined or greater degree adjacent to the aforementioned storage space as the edge position in the X-axis coordinates of an object adjacent to the storage space.

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