Automated guided vehicles and automated guided systems

Abstract

This facilitates the verification of the quality of the guide elements used to direct the movement of automated guided vehicles. [Solution] The automated guided vehicle 1 includes a camera 13 that photographs the guide line 3, a calculation unit 20 that calculates the area (actual area S) of the effective region of the guide line 3 in the image 30 taken by the camera 13, a storage unit 21 that stores the area (preset area S0) of the effective region of a normal guide line 3, and a determination unit 22 that compares the actual area S calculated by the calculation unit 20 with the preset area S0 stored in the storage unit 21 to determine whether the guide line 3 is good or bad.

Description

Technical Field

[0001] The present invention relates to an automatic guided vehicle and an automatic transport system including the same.

Background Art

[0002] In order to transport parts and the like within a factory, an automatic guided vehicle (AGV: Automatic Guided Vehicle) that travels while being guided by a guide (such as a guide line or a barcode) may be used. The automatic guided vehicle has, for example, a sensor that detects a guide laid on the floor of the factory, and automatically travels while detecting the guide with this sensor. For example, when using an electromagnetic induction method as the guidance method of the automatic guided vehicle, a pickup coil is used as the sensor, when using a magnetic induction method, a magnetic sensor is used, and when using an image recognition method, a camera is used as the sensor (for example, Patent Document 1 below).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Since the guide for guiding the automatic guided vehicle is laid on the floor of the factory, it may suffer from defects or dirt due to aging deterioration or the like. When such defects or dirt of the guide are significant, the sensor of the automatic guided vehicle may not be able to detect the guide, which may hinder automatic travel. Therefore, currently, workers visually check the condition of the guide regularly. However, when the travel route of the automatic guided vehicle is long or when a large number of automatic guided vehicles are installed in the factory, it takes a great deal of labor to visually check all the guides.

[0005] Therefore, an object of the present invention is to facilitate the confirmation of the quality of the guide for guiding the travel of the automatic guided vehicle. [Means for solving the problem]

[0006] To solve the above problems, the present invention provides an automated guided vehicle that travels while being guided by a dielectric provided on a travel path, The present invention provides an automated guided vehicle comprising: an imaging device for imaging the aforementioned derivative; a calculation unit for calculating the area of ​​the effective region of the derivative in the image captured by the imaging device; a storage unit for storing the area of ​​the effective region of a normal derivative; and a determination unit for determining whether the derivative is good or bad by comparing the area calculated by the calculation unit with the area stored in the storage unit.

[0007] This automated guided vehicle (AGV) calculates the area of ​​the effective region (actual area) of the dielectric in the image captured by the imaging device. By comparing this actual area with the area of ​​the effective region (preset area) of a normal dielectric image stored in the memory unit, the quality of the dielectric can be determined. As a result, the quality of the dielectric can be checked while the AGV is in motion, reducing the burden on workers who maintain the dielectrics.

[0008] The imaging device used to guide the automated guided vehicle (AGV) with a dielectric may also be used as an imaging device to capture images for the above-mentioned quality judgment. In this case, the AGV has a driving control unit that controls its movement based on information obtained from the images captured by the imaging device. The driving control unit controls the movement of the AGV so that the dielectric in the captured image is positioned at a predetermined location, that is, so that the imaging device is positioned at a predetermined location relative to the dielectric. In this case, since the imaging device is always positioned at a constant position (distance) from the dielectric, it becomes easier to compare the actual area of ​​the dielectric in the captured image with the preset area, improving the reliability of the dielectric quality judgment.

[0009] Even if the judgment unit of an automated guided vehicle (AGV) detects a defect in a transducer, if the defect is not severe enough to hinder the AGV's operation, it is not necessary to immediately stop the AGV and repair the transducer. Instead, the information can be transmitted to and stored in the operations management unit that manages the AGV's operation. Specifically, in an automated guided vehicle system comprising the AGV described above, an operations management unit that manages the AGV's operation, and an information transmission means for transmitting information from the AGV to the operations management unit, if the judgment unit of the AGV determines that a transducer is defective, it transmits the judgment result to the operations management unit, which then stores the judgment result and the AGV's position at that time.

[0010] Even if the judgment unit of the above-mentioned automated guided vehicle determines that there is a defect in a predetermined part of the transducer, when an operator visually inspects that part of the transducer during subsequent periodic maintenance, it may be found that no defect has occurred. Since such a part is likely to be misjudged as defective due to some factor, it is preferable to ignore the pass / fail judgment result for that part. Specifically, it is preferable that the operation management unit be able to be set to ignore the pass / fail judgment result for a predetermined location of the transducer (the part where the pass / fail judgment occurred) for a certain period of time.

[0011] In the above automated transport system, it is preferable that the operation management unit prioritizes the urgency of maintenance based on the number of times a defect has been detected in each part of the transporter. This allows workers to prioritize maintenance on parts of the transporter that are most likely to be defective, based on the information displayed on the operation management unit, thereby improving work efficiency. [Effects of the Invention]

[0012] As described above, according to the present invention, the quality of the derivative that guides the movement of an automated guided vehicle can be easily confirmed. [Brief explanation of the drawing]

[0013] [Figure 1] This is a plan view of an automated transport system according to one embodiment of the present invention. [Figure 2] It is a side view of the automatic guided vehicle that constitutes the above automatic conveyance system. [Figure 3] It is a front view of the above automatic guided vehicle. [Figure 4] It is a bottom view of the above automatic guided vehicle. [Figure 5] It is a block diagram of the control unit of the above automatic guided vehicle. [Figure 6] It is a diagram showing a photographed image of the guidance line. [Figure 7] It is a diagram showing a photographed image of the guidance line. [Figure 8] It is a diagram showing a photographed image of the guidance line. [Figure 9] It is a diagram showing a photographed image of the guidance line. [Figure 10] It is a flowchart showing the procedure for determining the quality of the guidance line.

Mode for Carrying Out the Invention

[0014] Hereinafter, embodiments of the present invention will be described based on the drawings.

[0015] As shown in FIG. 1, an automatic conveyance system according to an embodiment of the present invention includes a plurality of automatic guided vehicles 1 and an operation management unit 2 that manages their operations. A guidance line 3 as a guide is provided on the travel path of the automatic guided vehicles 1, and each automatic guided vehicle 1 travels while being guided by the guidance line 3. The operation management unit 2 and each automatic guided vehicle 1 can communicate bidirectionally. The operation management unit 2 issues commands for starting, stopping, and traveling speed to each automatic guided vehicle 1 and grasps the positions of each automatic guided vehicle 1. Each automatic guided vehicle 1 transmits various information to the operation management unit 2. In the illustrated example, a loop-shaped guidance line 3 with a constant width is provided on the floor surface, and a plurality of automatic guided vehicles 1 travel along this guidance line 3.

[0016] As shown in FIGS. 2 to 4, the automatic guided vehicle 1 includes a main body 10, drive wheels 11, driven wheels 12, a camera 13 as a photographing device, a control unit 14, a drive motor 15, a battery 16, and communication means 17. Power is supplied from the battery 16 to the camera 13, the control unit 14, the drive motor 15, and the communication means 17.

[0017] As shown in FIG. 5, the control unit 14 includes a travel control unit 18 and a guide line inspection unit 19.

[0018] The travel control unit 18 controls the drive motor 15 based on a command from the operation management unit 2 to control the start, stop, and travel speed of the automatic guided vehicle 1. Further, the travel control unit 18 controls the travel direction of the automatic guided vehicle 1 based on an image of the guide line 3 photographed by the camera 13. The camera 13 is provided on the lower surface of the main body 10 and photographs the guide line 3 passing directly below it at every micro time. As shown in FIG. 6, the position of the guide line 3 in the photographed image 30 (solid line) is compared with the position of the guide line 3 when traveling along the planned route (dotted line), and the travel control unit 18 controls the travel direction of the automatic guided vehicle 1 so that they match. For example, the travel control unit 18 controls the travel direction of the automatic guided vehicle 1 by adjusting the torque of the left and right drive wheels 11. As described above, while the travel control unit 18 corrects the travel direction, the automatic guided vehicle 1 travels along the guide line 3. Note that the area shown by hatching in FIG. 6 is the floor surface 4 on which the guide line 3 is laid and has a color different from that of the guide line 3.

[0019] The guide line inspection unit 19 inspects the presence or absence of dirt or defects in the guide line 3 and includes a calculation unit 20, a storage unit 21, and a determination unit 22. In the present embodiment, the camera 13 mounted on the automatic guided vehicle 1 functions as detection means for causing the automatic guided vehicle 1 to follow the guide line 3 and as a photographing device for determining the quality of the guide line 3. Therefore, the guide line inspection unit 19 inspects the guide line 3 based on the photographed image 30 by the camera 13.

[0020] The calculation unit 20 calculates the area of ​​the effective region of the guide line 3 from the image 30 captured by the camera 13. For example, the calculation unit 20 calculates the area of ​​the effective region of the guide line 3 in a predetermined target region A in the image 30 shown in Figure 7. Target region A is the region that includes the entire width of the guide line 3. In the illustrated example, even if the guide line 3 is slightly shifted from the set position (center) in the image 30, the entire area of ​​the guide line 3 is included in target region A. To this end, target region A has a width greater than the width dimension of the guide line 3, preferably 1.2 times or more the width dimension of the guide line 3.

[0021] The effective area of ​​the guide line 3 is the detection target area that is distinguished from other areas. For example, if the guide line 3 is a white line, the white area is the effective area, and areas of other colors, such as the floor surface 4, or the dirty parts P (see Figure 8) or missing parts Q (see Figure 9) of the guide line 3, are excluded. For example, the actual area of ​​the effective area (cm²) can be calculated from the dimensions (pixels) of the effective area in image 30 and the distance between the guide line 3 and the camera 13. 2 The area of ​​the effective region can be calculated using pixels, without converting the dimensions (pixels) of the effective region in image 30 to actual dimensions (cm).

[0022] The memory unit 21 stores the area of ​​the effective region in the captured image of a normal guide line 3. For example, a normal portion of the guide line 3 is captured by the camera 13 (see Figure 7), and the calculation unit 20 calculates the area of ​​the effective region in the target region A of this captured image and stores it in the memory unit 21.

[0023] The determination unit 22 compares the area of ​​the effective region of the guide line 3 calculated by the calculation unit 20 (hereinafter referred to as "actual area S") with the area of ​​the effective region of a normal guide line 3 stored in the storage unit 21 (hereinafter referred to as "preset area S0"). Specifically, it calculates the defect rate Y of the effective region, defined as Y = 100·(S0-S) / S0, and determines whether the guide line 3 is good or bad based on whether the defect rate Y exceeds a predetermined threshold.

[0024] The procedure for inspecting the quality of the induction line 3 using the derivative inspection unit 19 will be explained below with reference to Figure 10.

[0025] First, it is checked whether an image has been captured and output from the camera 13 of the automated guided vehicle 1 (step X1). If an image has been captured and output, the image is subjected to image processing to calculate the area of ​​the effective region. In this embodiment, the image captured and output from the camera 13 is subjected to binarization processing to clarify the distinction between the color of the effective region and other colors (step X2), and then the image is subjected to noise reduction processing (step X3).

[0026] Then, the calculation unit 20 calculates the area of ​​the effective region (actual area S) in the target region A of the captured image that has undergone the above image processing (step X4). Subsequently, the determination unit 22 calculates the defect rate Y of the guide line 3 in the image from the actual area S calculated by the calculation unit 20 and the preset area S0 stored in advance in the storage unit 21 (step X5). Then, it determines whether this defect rate Y is greater than or equal to a threshold stored in advance in the storage unit 21 (step X6). If the defect rate Y is less than the threshold, the process returns to step X1. If the defect rate Y is greater than or equal to the threshold, the relevant part of the guide line 3 is determined to be "defective," and this "defective" determination result is notified to the operation management unit 2 via the communication means 17 (step X7).

[0027] When the operation management unit 2 receives a "defective" judgment result for the guidance line 3 from the automated guided vehicle 1, it stores the judgment result along with the position of the automated guided vehicle 1 at that time. Note that since the automated guided vehicle 1 is continuously moving, the position of the automated guided vehicle 1 when the "defective" judgment result is received has moved slightly from the position of the automated guided vehicle 1 when the image 30 used for the judgment was taken. However, since the processing time for steps X1 to X6 in Figure 10 is extremely short, the position of the automated guided vehicle 1 when the "defective" judgment result is received can be considered as the position where the defect in the guidance line 3 occurred.

[0028] In this embodiment, even if the automated guided vehicle 1 notifies the operation management unit 2 of the determination result that the guide line 3 is "defective," the operation management unit 2 does not immediately repair the guide line 3. Instead, it stores information on the location where the "defect" in the guide line 3 has occurred, and during periodic maintenance, it repairs the guide line 3 based on the information stored in the operation management unit 2. In other words, even if the guide line 3 is dirty or damaged, the automated guided vehicle 1 continues to follow the guide line 3.

[0029] In this case, parts of the guidance line 3 that are dirty or damaged may be difficult for the camera 13 of the automated guided vehicle 1 to read. Therefore, when the automated guided vehicle 1 transmits the result of determining that the guidance line 3 is "defective" to the operation management unit 2, the speed of the automated guided vehicle 1 may be reduced in the vicinity of the position of the automated guided vehicle 1 at that time (for example, a few meters before and after the position determined to be "defective") (for example, to about 50-75% of the speed just before). In this way, by having the automated guided vehicle 1 pass slowly over the defective part of the guidance line 3, it is possible to prevent the camera 13 from misreading the defective part of the guidance line 3 and avoid situations that would hinder the operation of the automated guided vehicle 1.

[0030] Guide line 3 is regularly maintained by workers through visual inspection (for example, outside of factory operating hours). Even if a part of guide line 3 is judged as "defective" by the information stored in the operation management unit 2, when workers visually inspect it during maintenance, it may not actually be dirty or damaged. In this way, if there is a part of guide line 3 that is actually in a normal state but is prone to being mistakenly judged as "defective" due to some factor, the judgment result of "defective" will be stored in the operation management unit 2 every time that part is photographed by camera 13. When such judgment results due to misjudgments are accumulated, normal parts have to be visually checked again during regular maintenance, which reduces work efficiency.

[0031] Therefore, if a part of the guide line 3 that was judged as "defective" is found to be "normal" (i.e., a misjudgment) through subsequent maintenance, the operation management unit 2 may be informed that the part is "normal". In this way, if a part judged as "defective" is actually "normal", it is preferable to ignore the judgment result for such a part for a certain period of time, as that part is expected to be prone to misjudgments. By enabling the operation management unit 2 to ignore the judgment result for a certain part of the guide line 3 for a certain period of time, it is possible to prevent the accumulation of judgment results due to misjudgments, thereby avoiding a decrease in work efficiency.

[0032] In the automated transport system described above, the automated transport vehicle 1 passes over the same section of the guide line 3 multiple times while the factory is in operation, so the same section is photographed and judged as good or bad multiple times. In this case, it is expected that sections that are heavily soiled or damaged will be judged as "defective" more often. Therefore, it is preferable for the operation management unit 2 to prioritize the urgency of maintenance based on the number of times each section of the guide line 3 has been judged as "defective". In this case, the display unit of the operation management unit 2 will display the sections of the guide line 3 that have been judged as "defective" in descending order of frequency. Workers will then perform maintenance on the sections displayed on the operation management unit 2, starting with those that have been judged as "defective" the most times, and repair any dirt or damage that has actually occurred. This allows for repairs to be performed on the most soiled or damaged sections first, thus increasing the efficiency of maintenance.

[0033] The present invention is not limited to the embodiments described above. For example, although the above embodiment shows that the guide line 3 is a white line, it is not limited to this and may be a line of another color such as yellow, or a light-reflecting line. Furthermore, the derivative is not limited to a guide line, but may be an identification code such as a barcode (including a two-dimensional barcode). In this case, the camera 13 captures an image containing the identification code, and the area in this captured image containing the entire identification code is designated as the target area A. In addition, of the areas of different colors (for example, white and black) that constitute the identification code in the captured image, the area of ​​one color (for example, the black area) is designated as the effective area.

[0034] In the above embodiment, the camera 13 installed on the automated guided vehicle 1 is shown to function as a detection means for following the guide line 3 and as an imaging device for taking images to determine the quality of the guide line 3. However, the detection means and the imaging device may be provided separately. For example, if the guide line is a magnetic tape, the automated guided vehicle may be equipped with a magnetic sensor as a detection means for following the magnetic tape and an imaging device for taking images of the magnetic tape to determine the quality of the magnetic tape. [Explanation of symbols]

[0035] 1. Automated Guided Vehicle 2 Operation Management Department 3. Induction line (derivative) 4 Floor surface 10 Main Unit 11 Drive wheels 12 Driven wheels 13 Cameras 14 Control Unit 15 Electric motor 16 batteries 17. Means of communication 18. Driving Control Unit 19. Derivative Testing Department 20 Calculation Section 21 Memory section 22 Judgment section 30 captured images A. Target area

Claims

1. In an automated guided vehicle that travels while being guided by a guide installed on a travel path, A photographing apparatus for photographing the aforementioned derivative, A calculation unit that calculates the area of ​​the effective region of the derivative in the image captured by the aforementioned imaging device, A storage unit that stores the area of ​​the effective region of a normal derivative, An automated guided vehicle having a determination unit that compares the area calculated by the calculation unit with the area stored in the storage unit to determine whether the derivative is good or bad.

2. The automated guided vehicle according to claim 1, further comprising a driving control unit that controls driving based on information obtained from images captured by the aforementioned imaging device.

3. The system comprises an automated guided vehicle according to claim 1 or 2, and an operation management unit for managing the operation of the automated guided vehicle, An automated transport system in which, if the determination unit determines that a defect has occurred, the determination result is transmitted to the operation management unit, and the operation management unit stores the determination result and the position of the automated transport vehicle at that time.

4. The automated transport system according to claim 3, wherein the operation management unit is configured to ignore the good or bad judgment result at a predetermined position of the derivative for a certain period of time.

5. The automated transport system according to claim 3, wherein the operation management unit prioritizes the urgency of maintenance based on the number of times a defect is detected in each part of the dielectric.

Images