JP7761884B2 - Unmanned forklift - Google Patents

Description

The present invention relates to an unmanned forklift that can self-propel and perform loading and unloading operations.

There is a technology for determining whether an unmanned forklift can insert a fork into a pallet's insertion opening (hole 20 in Patent Document 1) (see, for example, the first embodiment of Patent Document 1). In this technology, the unmanned forklift 11 is equipped with a laser range finder 18 that can be raised and lowered together with the fork 14. The laser range finder 18 scans the opening end surface 19a of the pallet 19 placed at the loading position with a laser beam, obtaining image data of the edge line of the hole 20. A pattern controller 21 then compares the obtained image data of the hole 20 line with image data of the hole 20 line captured in advance under multiple conditions in which the distance and inclination of the end surface of the pallet 19 relative to the unmanned forklift 11 are changed, thereby determining whether the fork 14 can be inserted into the hole 20 in the pallet 19.

Also, there is technology for unmanned forklifts that identifies the type of pallet and adjusts the spacing of the forks to match the spacing of the insertion openings (holes 20 in Patent Document 1) of the identified pallet (see, for example, the second embodiment of Patent Document 1). With this technology, multiple types of pallets 19 with different spacing between the holes 20 are used, and the unmanned forklift 11 is equipped with a fork shifter that can adjust the spacing between the pair of forks 14. The pattern controller 21 identifies the type of pallet 19 by specifying the width of the pallet 19 from image data of the edge lines of the holes 20 captured by the laser range finder 18. The control device 17 then controls the fork shifter to adjust the spacing between the pair of forks 14 to match the spacing of the holes 20 that corresponds to the identified width of the pallet 19.

Furthermore, there is technology for identifying the type of pallet in an unmanned forklift (see, for example, Patent Document 2). In this technology, a pattern 01 is provided on the pallet 05, and the unmanned forklift 5 is equipped with a pair of cameras 3 and 4. The cameras 3 and 4 photograph the pattern 01 on the pallet 05. A position determination means then determines the position of the pallet 05 from the photographed pattern, and a comparison means compares the photographed pattern with a reference pattern to identify the type of pallet 05.

JP 2017-19596 A Japanese Patent Application Publication No. 9-218014

The unmanned forklift truck in Patent Document 1 identifies the type of pallet by detecting an image of the line at the edge of the hole in the open end face of the pallet using laser distance measurement. Therefore, there is a risk that the type of pallet cannot be identified or may be identified incorrectly if, for example, there is cargo or another pallet directly next to the pallet, if part of the stretch film used to secure the cargo to the pallet blocks the hole in the pallet, or if a foreign object has entered the hole in the pallet.

Identifying the type of pallet in the unmanned forklift described in Patent Document 2 requires attaching a pattern to all pallets that are subject to loading and unloading operations by the unmanned forklift, which increases costs. Furthermore, when dealing with many different types of pallets with different shapes, a common location for attaching patterns to all of those pallets is required, which limits the types of pallets that can be handled.

On the other hand, when detecting the amount of positional deviation of multiple types of pallets using only a distance detector such as a laser scanner, it is necessary to identify the corresponding type of pallet by comparing the measured shape data acquired by the distance detector with all types of stored position and shape data for the multiple types of pallets.

As a result, due to the characteristics of the distance detector, which measures using only distance data, as the number of target pallet types increases, there is an increased possibility that items other than pallets placed on the floor (such as cardboard boxes) will be mistakenly recognized as a specific type of pallet.

Furthermore, the computational load involved in comparing and matching the shape data of multiple types of pallets is large, which can take a long time to calculate and slow down work speed, and using expensive computers with high processing power can increase costs.

The present invention aims to provide an unmanned forklift that can handle any type of pallet, minimizes a decrease in work speed and an increase in costs, and detects with high accuracy the amount of deviation in the position and posture of a target pallet relative to the pallet's normal position, enabling the required loading and unloading operations to be carried out reliably.

In order to solve the above problems, the unmanned forklift according to the present invention comprises:
An unmanned forklift equipped with a pair of forks that automatically performs loading and unloading work using pallets,
an image acquisition unit that acquires a photographed image from an image acquisition device that photographs a front side of the unmanned forklift;
a pallet type identification unit that has a trained learning model that has undergone machine learning on the combination of each pallet image and the type of pallet for multiple types of pallets, and identifies the type of the target pallet by inputting the photographed image of the target pallet acquired by the image acquisition unit into the learning model;
a pallet position and shape acquisition unit that acquires position and shape data of the target pallet from a distance measurement device that measures the distance to the target pallet;
a pallet deviation amount detection unit that stores position and shape data of each of the plurality of types of pallets in advance, and detects the amount of deviation of the position and posture of the target pallet from its normal position by comparing the stored position and shape data corresponding to the type of target pallet identified by the pallet type identification unit with the position and shape data of the target pallet acquired by the pallet position and shape acquisition unit;
(A) an operation including inserting the forks into the insertion opening of the target pallet using the detected amount of deviation;
(B) a stacking operation in which the pallet and cargo held by the forks inserted into the insertion openings are loaded onto the cargo of the target pallet in accordance with the amount of deviation of the target pallet;
a travel control unit that controls travel to perform at least one of the above tasks;
Equipped with.

With this configuration, even if the objects being transported by the unmanned forklift are multiple types of pallets with different shapes, there is no limit to the types of pallets that can be handled. By inputting the captured image into a trained learning model that has undergone machine learning on the combination of each image of the pallet and the pallet type, the pallet type identification unit can identify the type of the target pallet with high accuracy. The pallet misalignment detection unit can then compare and match the stored position and shape data corresponding to the type of target pallet identified by the pallet type identification unit with the position and shape data of the target pallet obtained by the pallet position and shape acquisition unit.

This allows for highly accurate detection of the deviation in the position and posture of the target pallet relative to the pallet's normal position, and prevents accidents such as items other than pallets placed on the floor (such as cardboard boxes) being mistakenly recognized as a specific type of pallet, resulting in interference between the forks and the pallet or item. Furthermore, the calculation time required to compare and match pallet shape data is shortened, improving work speed, eliminating the need for expensive computers with high processing power.

After detecting the amount of deviation of the target pallet with high accuracy, the unmanned forklift can perform the required loading and unloading operations, such as operation (A) of picking up the pallet and cargo on the floor and transporting it to another location, or operation (B) of picking up the pallet and cargo on the second layer of cargo on the first layer of pallets and transporting it to another location, or operation (B) of stacking.

Here, the position and shape data is data on the height including the insertion opening of the pallet,
In a preferred embodiment, the pallet deviation detection unit superimposes and compares a line segment based on the stored position and shape data corresponding to the type of target pallet with a line segment based on the position and shape data of the target pallet acquired by the pallet position and shape acquisition unit.

With this configuration, the amount of deviation is determined by overlapping and comparing line segments, reducing calculation costs. Furthermore, because the height data can be obtained by simply scanning horizontally, there is no need to move the distance measuring device that measures the distance to the target pallet up and down, thereby shortening the time it takes to detect the amount of deviation in the position and orientation of the target pallet.

In another preferred embodiment, the system further includes a deviation error determination unit that determines an error when the deviation detected by the pallet deviation detection unit exceeds a predetermined threshold.

With this configuration, if the misalignment is too large to insert the forks into the target pallet, an error determination is made before the fork insertion operation, preventing accidents in which the forks interfere with the target pallet. For example, the threshold misalignment amount that serves as the basis for the error determination is set to the maximum value of the range in which the unmanned forklift can self-propel and insert the forks into the target pallet from the predetermined stopping position at which the unmanned forklift stops when the distance measurement device measures the distance to the target pallet and the pallet position and shape acquisition unit acquires the position and shape data of the target pallet.

In a preferred embodiment, the system further includes a pallet shape determination unit that compares the shape data of the target pallet acquired by the pallet position/shape acquisition unit with pre-stored shape data of a pallet of the same type as the target pallet, and if the difference between the two is greater than a predetermined threshold, determines that there is an error in the acquired shape data of the target pallet.

With this configuration, for example, if another item (such as a cardboard box) is placed right next to the target pallet, and the size of the target pallet is incorrectly detected due to the influence of the other item, the error can be determined by comparing it with the stored pallet shape corresponding to the type of target pallet identified by the pallet type identification unit. This prevents accidents in which the forks interfere with the insertion opening of the target pallet, causing work to stop.

Furthermore, in a preferred embodiment, the system further includes a pallet stacking determination unit that determines whether the target pallet is stacked by performing image processing on the photographed image of the target pallet acquired by the image acquisition unit and calculating the number of pallets within a predetermined specific range where the target pallet is present.

With this configuration, by only calculating the number of pallets within a specific range in front of the unmanned forklift, it is possible to reliably determine whether pallets are stacked or not without erroneously detecting pallets other than the target pallet. For example, there is no erroneous detection that would result in the number of pallets being calculated as two, even though a pallet can be stacked, so there is no erroneous determination that it cannot be stacked.

In a preferred embodiment, the pallet stacking determination unit is configured to perform machine learning in advance on pallet images of multiple pallets stacked vertically, using them as training data.

With this configuration, if the surrounding images of a pallet differ depending on which tier the pallet is on, each of these different surrounding images is learned as training data, thereby improving the accuracy of pallet detection.

Furthermore, a preferred embodiment further includes a fork width change device that changes the spacing of the forks to match the spacing of the insertion openings of the target pallet based on the type of the target pallet obtained from the pallet type identification unit.

With this configuration, there is no need to prepare an unmanned forklift for each target pallet with different insertion opening spacing. By operating the fork width change device to adjust the fork spacing to correspond to the insertion opening spacing, multiple types of pallets can be transported with a single unmanned forklift.

Furthermore, a distance sensor disposed at the tip of the fork for detecting the presence or absence of an object ahead;
a presence sensor that detects whether the forks are holding the pallet;
Furthermore,
Using the result of detecting the presence or absence of the object by the distance sensor and the result of detecting whether the pallet is being held by the inventory sensor,
Stacking operations in which a second pallet and load are loaded onto the load of the first pallet, and/or
In a preferred embodiment, a de-stack operation is performed in which the second tier pallet and its load on the first tier pallet are picked up and transported to another location.

With this configuration, when performing the stacking operation, the distance sensor can detect the height of the load on the first tier of pallets, and the load presence sensor can detect the height at which the forks are withdrawn after the stacking operation. Furthermore, when performing the unstacking operation, the distance sensor can detect the height of the insertion opening of the second tier of pallets, and the load presence sensor can detect the state in which the forks are holding the pallet and load to be held during the unstacking operation. Therefore, the distance sensor and the load presence sensor enable reliable stacking and/or unstacking operations.

In another preferred embodiment, the photographing device photographs the target pallet at a first stop position and the pallet type identification unit identifies the type of the target pallet, and the distance measuring device measures the distance to the target pallet at a second stop position closer to the target pallet than the first stop position, and the pallet position and shape acquisition unit acquires position and shape data of the target pallet.

With this configuration, the distance measuring device measures the distance to the target pallet at the second stop position, which is closer to the target pallet than the first stop position, and the pallet position and shape acquisition unit acquires position and shape data of the target pallet, allowing the pallet deviation amount detection unit to more accurately detect the amount of deviation in the position and posture of the target pallet.

As described above, the unmanned forklift of the present invention is not limited in the types of pallets it can handle, can suppress a decrease in work speed and an increase in costs, and can detect with high accuracy the amount of deviation in the position and posture of the target pallet relative to the pallet's normal position, ensuring the required loading and unloading operations can be carried out reliably.

1 is a perspective view of an unmanned forklift according to an embodiment of the present invention. 1 is a left side view of the unmanned forklift truck holding a pallet and a load, showing a cross section of the pallet with the forks inserted into the insertion openings. FIG. FIG. 2 is a perspective view showing a pallet and a load on a floor surface, and the unmanned forklift truck. FIG. 2 is a block diagram showing an outline of the device configuration. FIG. 10 is an enlarged cross-sectional view of a main portion showing a sensor disposed at the tip of the right fork. FIG. 10 is an enlarged longitudinal cross-sectional view of a main portion showing a sensor disposed at the tip of the right fork. FIG. 10 is an enlarged view of the main part around the inventory sensor as seen from the left. This is an enlarged cross-sectional view of the main part around the inventory sensor. 10 is a diagram showing an example of an image in which the palette type identification unit has identified the type of the target palette. FIG. FIG. 10 is a schematic plan view showing line segments based on stored pallet point cloud data corresponding to the type of target pallet. 8B is a schematic plan view showing the line segment of FIG. 8A superimposed on the line segment based on the point cloud data of the target pallet acquired by the pallet position/shape acquisition unit. FIG. FIG. 2 is a front view of the fork width change device. 10 is a schematic plan view illustrating the operation of narrowing the fork width using the fork width changing device. FIG. FIG. 10 is a schematic plan view illustrating the operation of widening the fork width using the fork width changing device. FIG. 10 is an explanatory diagram of an example of stacking operation, showing a state in which an unmanned forklift approaches a target pallet and stops a predetermined distance before the target pallet. This is an explanatory diagram of an example of stacking operation, showing the state in which the forks of an unmanned forklift are raised and the height of the load on the target pallet is detected by a distance sensor, and then the forks are raised a further predetermined distance and stopped. FIG. 10 is an explanatory diagram of an example of stacking operation, showing a state in which the unmanned forklift has been advanced a predetermined distance to position a pallet and cargo to be stacked above the target pallet and cargo. This is an explanatory diagram of an example of stacking operation, showing the state at the moment when the forks are lowered and the underside of the pallet to be stacked comes into contact with the top surface of the load on the target pallet. FIG. 10 is an explanatory diagram of an example of stacking operation, showing a state in which the forks are further lowered and the load sensor detects that there is no load on the forks.

Embodiments of the present invention will be described below with reference to the drawings.

In the following embodiments, the direction from the base of the forks of an unmanned forklift to the tip is defined as the front, and the opposite direction as the rear, with left and right defined as looking forward.

For unmanned forklifts, the front view is the view looking towards the front where the forks are located. For the target pallet and load, the front view is the view looking towards the side of the target pallet where the fork insertion openings are located.

<Unmanned forklift>
An unmanned forklift 1 according to an embodiment of the present invention, shown in the perspective view of Figure 1, the view from the left in Figure 2, the perspective view of Figure 3, and the block diagram showing an outline of the equipment configuration in Figure 4, has a self-position estimation function and performs loading and unloading work by self-propelling according to destination instructions from a management device that wirelessly communicates with the unmanned forklift 1. The unmanned forklift 1 is equipped with a loading device A that performs loading and unloading work, a moving device B that performs traveling and turning operations, a control device C that controls the loading device A and the moving device B, a fork width changing device D, a processing device 10, etc.

(Loading equipment)
The cargo handling device A has a mast M that moves up and down and tilts back and forth, forks 2 that carry a pallet P and cargo W, and a lift bracket 3 that moves up and down along the mast M and supports the forks 2. The forks 2 consist of a right fork 2R and a left fork 2L.

The fork 2 and lift bracket 3 are moved up and down along the inner mast 4 by a lift chain 7. The mast M consists of the inner mast 4, which supports the lift bracket 3 and moves up and down, and the outer mast 5, which guides the inner mast 4 so that it can be raised and lowered. The outer mast 5 tilts forward and backward by a tilt cylinder 6.

(Mobile device)
The transportation device B has a pair of left and right front wheels, rear wheels which are both drive wheels and steerable wheels, and a drive unit for the rear wheels.

(Control device)
The control device C controls the lifting drive device of the cargo handling device A, the drive device of the moving device B, and the drive device of the tilt cylinder 6, and also has a communication device for communicating with a management device on the ground. The control device C also has an interface with the processing device 10 shown in Figure 4, as well as interfaces with the distance sensor S3 and the cargo presence sensor S4.

(Fork width change device)
The fork width change device D changes the distance F between the right fork 2R and the left fork 2L.

(Processing device)
4 has an interface with the image capture device S1 and issues image capture instructions to the image capture device S1. The processing device 10 also has an interface with the distance measurement device S2 and issues distance measurement instructions to the distance measurement device S2.

<Palette>
Pallets P, P1 shown in the left view of Figure 2 and the perspective view of Figure 3 are, for example, flat pallets and have insertion openings Q, Q for left and right forks 2L, 2R. A load W is placed on the pallets P, P1.

<Sensor group>
As shown in the perspective view of Fig. 1 and the block diagram showing an outline of the equipment configuration of Fig. 4, the unmanned forklift 1 is equipped with an image capture device S1, a distance measurement device S2, a distance sensor S3, a load presence sensor S4, a self-position recognition sensor S5, and an obstacle detection sensor S6. The sensors S1 to S6 are arranged at positions described below. The control device C and processing device 10 shown in Fig. 4 are arranged within the main body 1A of the unmanned forklift 1 as shown in Fig. 1.

(photography equipment, distance measuring equipment)
As shown in Fig. 1, the camera device S1 and the distance measurement device S2 are held by a bracket J attached to a support plate I that protrudes forward from the center in the left-right direction of the front lower part of the main body 1A of the unmanned forklift 1. In other words, the camera device S1 and the distance measurement device S2 are not mounted on movable members relative to the main body 1A, such as the lift bracket 3 or the forks 2, but on immovable members relative to the main body 1A. This holding structure for the camera device S1 and the distance measurement device S2 is also applied to the structure shown in other drawings, such as Fig. 9.

The image capturing device S1 is, for example, a monocular camera, and captures an image of the area in front of the unmanned forklift 1 at a predetermined stopping position where the unmanned forklift 1 will be stopped. The distance measuring device S2 is, for example, a 2D-LiDAR (Light Detection And Ranging) or TOF (Time Of Flight) camera, and measures the distance to a target pallet P1 (e.g., Figure 2) on the floor N at a predetermined stopping position where the unmanned forklift 1 will be stopped. If the distance measuring device S2 is a 2D-LiDAR, it emits pulsed laser light while changing direction horizontally, detects the scattered light that is reflected and returned, and measures the distance and direction to the target from the time it takes for the light to be reflected by the object and return.

As shown in Figure 4, the image captured by the imaging device S1 is sent to the processing device 10, and the data measured by the distance measuring device S2 is sent to the processing device 10.

(distance sensor)
As shown in the perspective view of Fig. 1, the enlarged horizontal cross-sectional view of a main portion of Fig. 5A, and the enlarged vertical cross-sectional view of a main portion of Fig. 5B, the distance sensor S3 is disposed at the tip of the right fork 2R and fixed by a mounting plate 19. The distance sensor S3 may also be disposed at the tip of the left fork 2L. The distance sensor S3 is, for example, a distance setting type photoelectric sensor, and detects the presence or absence of an object ahead.

As shown in Figure 4, a signal indicating the presence or absence of an object detected by distance sensor S3 (e.g., an ON/OFF signal) is sent to control device C.

(obstacle detection sensor)
As shown in the perspective view of Fig. 1 and the enlarged cross-sectional view of the main part of Fig. 5A, the obstacle detection sensors S6 are mounted on the left and right ends of the tips of the left and right forks 2L, 2R by mounting plates 20. The obstacle detection sensors S6 are, for example, photoelectric sensors, and detect obstacles approaching the tips of the forks 2.

A signal (e.g., an ON/OFF signal) indicating the presence or absence of an obstacle detected by the obstacle detection sensor S6 is sent to the control device C. If the obstacle detection sensor S6 detects an obstacle, the control device C will, for example, bring the unmanned forklift 1 to an emergency stop.

(Cargo sensor)
As shown in the view from the left in Fig. 2, the enlarged view of the main part from the left in Fig. 6A, and the enlarged cross-sectional view of the main part in Fig. 6B, the load presence sensor S4 is disposed at the base of the left and right forks 2L, 2R while being supported by the support plate 21. The load presence sensor S4 is, for example, a proximity switch, and detects whether the forks 2 are holding a pallet P.

As shown in Figure 4, a signal (e.g., an ON/OFF signal) indicating whether the fork 2 is holding a pallet P detected by the load sensor S4 is sent to the control device C.

The structure around the load sensor S4 at the base of the left fork 2L will be described with reference to Figures 6A and 6B. The seesaw plate 22, supported by the left-right support shaft R, is stopped by the stop plate 23 and is elastically biased by the tension coil spring 24. When the fork 2 is not holding a pallet P, the seesaw plate 22 remains stationary in the position shown by the solid line in Figure 6A. When the fork 2 is holding a pallet P as in Figure 2, the seesaw plate 22 swings so that the operating piece 22A descends and the detection piece 22B ascends, as shown by the imaginary line in Figure 6A. As a result, the load sensor S4 detects the detection piece 22B, making it possible to detect that the fork 2 is holding a pallet P.

(Self-position recognition sensor)
As shown in the perspective view of FIG. 1 , the self-location recognition sensor S5 is disposed facing upward on the upper surface of a frame K that protrudes upward from the main body 1A. That is, the self-location recognition sensor S5 is installed on a member that is immovable relative to the main body 1A. The self-location recognition sensor S5 performs, for example, 3D imaging in all horizontal directions and with a vertical field of view of approximately 30°. The self-location recognition sensor S5 is, for example, a 3D-LiDAR, and is used for a laser SLAM (Simultaneously Localization And Mapping) type self-location estimation method. The self-location estimation of the unmanned forklift 1 may also be performed using an image SLAM type self-location estimation method, etc.

In the unmanned forklift 1 controlled using the above group of sensors S1 to S6, the signal cables and power supply cables of sensors S3, S4, and S6, which are installed on movable members relative to the main body 1A, are connected to the control device C and power supply within the main body 1A via the cable bear 8 shown in Figure 1.

<Image acquisition section>
As shown in Fig. 4, the processing device 10 has an image acquisition unit 11. The image acquisition unit 11 acquires a photographed image from a photographing device S1. The photographing device S1 photographs, for example, a target pallet P1 shown in Fig. 2 or 3. The image acquisition unit 11 acquires the photographed image of the target pallet P1 from the photographing device S1.

<Pallet type identification unit>
4, the processing device 10 has a pallet type identification unit 12. The pallet type identification unit 12 has a trained learning model that has undergone machine learning on combinations of images of each pallet P and the type of pallet P for multiple types of pallets P. The pallet type identification unit 12 identifies the type of the target pallet P1 by inputting the photographed image of the target pallet P1 acquired by the image acquisition unit 11 into the learning model.

Figure 7 shows an example of an image in which the pallet type identification unit 12 has identified the type of the target pallet. In this example, the pallet type identification unit 12 has identified the type of target pallet P1 on floor N as "small" and the type of target pallet P2 on the cargo of target pallet P1 as "small." The pallet type identification unit 12 can identify pallet types such as "small," "medium," and "large," and can also display the certainty (probability) of the identified pallet type on the image, such as (0.XX) and (0.△△) in Figure 7.

<Pallet position and shape acquisition unit>
As shown in Fig. 4, the processing device 10 has a pallet position/shape acquisition unit 13. The pallet position/shape acquisition unit 13 acquires position/shape data of the target pallet P1 from a distance measurement device S2 that measures the distance to the target pallet P1 when the unmanned forklift 1 shown in Fig. 2 or 3 is at a predetermined stop position that is a predetermined distance E away from the target pallet P1. The position/shape data of the target pallet P1 is, for example, point cloud data of the height including the insertion opening Q of the target pallet P1.

<Pallet deviation amount detection unit>
As shown in Fig. 4, the processing device 10 has a pallet misalignment detection unit 14. The pallet misalignment detection unit 14 stores in advance position and shape data for each of multiple types of pallets P. The position and shape data for each pallet P stored in advance by the pallet misalignment detection unit 14 is the position and shape data for each pallet P acquired by the pallet position and shape acquisition unit 13 when the distance measurement device S2 measures the distance to each pallet P at the predetermined stopping position of the unmanned forklift 1. The position and shape data for each pallet P is, for example, point cloud data of the height including the insertion opening Q of the pallet P.

The pallet deviation amount detection unit 14 detects the amount of deviation in the position and orientation of the target pallet P1 from its normal position by comparing the stored position and shape data corresponding to the type of target pallet P1 identified by the pallet type identification unit 12, i.e., the position and shape data of the pallet P corresponding to the type of target pallet P1 among the pallets P previously stored as described above, with the position and shape data of the target pallet P1 acquired by the pallet position and shape acquisition unit 13.

L0 in the schematic plan view of Figure 8A is a line segment based on point cloud data of the height including the insertion port Q of the stored pallet P corresponding to the target pallet P1 in Figure 2 or 3, for example, for a pallet on floor N. As described above, the pallet misalignment detection unit 14 stores data for the line segment L0 shown in Figure 8A for each of multiple types of pallets P.

As shown in the schematic plan view of Figure 8B, the pallet deviation amount detection unit 14 overlays and compares line segment L0 with line segment L1, which is based on point cloud data of the height including the insertion port Q of the target pallet P1 acquired by the pallet position and shape acquisition unit 13.

This type of pallet misalignment detection unit 14 determines the amount of misalignment by superimposing and comparing line segments L0 and L1, thereby reducing calculation costs. Furthermore, because the height point cloud data can be obtained by simply scanning horizontally, there is no need to move the distance measurement device S2, which measures the distance to the target pallet P1, up and down, thereby reducing the time required to detect the amount of misalignment in the position and orientation of the target pallet P1.

<Photographing position of the photographing device, distance measurement position of the distance measuring device>
The position where the unmanned forklift 1 stops when the photographing device S1 photographs the target pallet P1 and the pallet type identification unit 12 identifies the type of the target pallet P1 is the first stopping position, and the position where the unmanned forklift 1 stops when the distance measuring device S2 measures the distance to the target pallet P1 and the pallet position/shape acquisition unit 13 acquires the position/shape data of the target pallet P1 is the second stopping position.

For example, the first and second stop positions of the unmanned forklift 1 shown in Figures 2 and 3 may be set to positions a predetermined distance E away from the target pallet P1, and the first and second stop positions may be the same, or the second stop position may be closer to the target pallet P1 than the first stop position. By setting the second stop position closer to the target pallet P1 than the first stop position, the pallet deviation amount detection unit 14 can more accurately detect the deviation amount of the position and posture of the target pallet P1.

<Error determination unit>
4, the processing device 10 has a deviation amount error determination unit 15. The deviation amount error determination unit 15 determines that an error has occurred when the deviation amount of the position and posture of the target pallet P1 from the correct position detected by the pallet deviation amount detection unit 14 exceeds a predetermined threshold value.

With this type of deviation error determination unit 15, if the deviation is too large to insert the forks 2 into the target pallet P1, an error determination is made before the fork insertion operation, preventing accidents in which the target pallet P1 and the forks 2 interfere with each other. For example, the deviation threshold that serves as the basis for the error determination is the maximum value within the range in which the unmanned forklift 1 can insert the forks 2 into the target pallet P1 while self-propelled from the predetermined stopping position at which the unmanned forklift 1 is stopped when the distance measurement device S2 measures the distance to the target pallet P1 and the pallet position and shape acquisition unit 13 acquires the position and shape data of the target pallet P1.

<Pallet shape determination unit>
4, the processing device 10 has a pallet shape determination unit 16. The pallet shape determination unit 16 compares the shape data of the target pallet P1 acquired by the pallet position/shape acquisition unit 13 with pre-stored shape data of a pallet of the same type as the target pallet P1, and if the difference between the two is greater than a predetermined threshold, determines that there is an error in the acquired shape data of the target pallet P1.

With this type of pallet shape determination unit 16, even if the size of the target pallet P1 is incorrectly detected due to the influence of another item (such as a cardboard box) placed right next to the target pallet P1, the error can be determined by comparing it with the stored pallet shape corresponding to the type of target pallet P1 identified by the pallet type identification unit 12. This prevents accidents in which the forks 2 interfere with the insertion opening Q of the target pallet P1, causing work to stop.

<Pallet stacking determination unit>
As shown in Fig. 4, the processing device 10 has a pallet stacking determination unit 17. The pallet stacking determination unit 17 processes the photographed image of the target pallet (e.g., P1 and P2 in Fig. 7) acquired by the image acquisition unit 11 as shown in Fig. 7, and determines whether the target pallets P1 and P2 are stacked by calculating the number of pallets within a predetermined specific range.

For example, if the number of pallets within the specified range is two, as shown in Figure 7, it can be determined that stacking work, in which a second layer of pallets and cargo transported from another location is placed on top of the first layer of pallets and cargo, is not possible; if the number of pallets within the specified range is one, it can be determined that stacking work is possible. Also, if the number of pallets within the specified range is one, it can be determined that unstacking work, in which the second layer of pallets and cargo are picked up and transported to another location, is not possible; if the number of pallets within the specified range is two, it can be determined that unstacking work is possible. Also, if it is preset that stacking work is not possible when the number of pallets within the specified range is two or more, it can be determined that stacking work is not possible when the number of pallets within the specified range is two or more.

By calculating only the number of pallets within a specific range in front of the unmanned forklift 1, this pallet stacking determination unit 17 can reliably determine whether pallets are stacked or not without erroneously detecting pallets other than the target pallet. For example, there is no erroneous detection that would result in the detection of a non-target pallet placed to the side of the target pallet on floor N and the calculation that the number of pallets is two, eliminating the erroneous determination that stacking is not possible when stacking is possible.

For example, in the example image of Figure 7, the image surrounding pallet P1 on the floor is different from the image surrounding pallet P2 on the upper level. Therefore, if a large number of images including pallets on the floor are prepared and a trained learning model is used that has undergone machine learning using the pallets on the floor as training data, it may not be able to identify pallet P2 on the upper level.

Therefore, in a preferred embodiment, the pallet stacking discrimination unit 17 is one that has previously undergone machine learning for multiple tiers of pallets, using each pallet image as training data. With this type of pallet stacking discrimination unit 17, if the surrounding images of a pallet differ depending on which tier the pallet is on, each of these different surrounding images is learned as training data, thereby improving the accuracy of pallet detection.

<Travel control unit>
4, the control device C has a travel control unit 18. The travel control unit 18 controls the travel of the unmanned forklift 1 to perform at least one of the following tasks (A) and (B). The travel control unit 18 may be provided in a management device on the ground.

(A) A task that includes inserting the fork 2 into the insertion opening of the target pallet using the amount of deviation detected by the pallet deviation amount detection unit 14.
(B) A stacking operation in which the pallet P and cargo W held by the forks 2 inserted into the insertion openings Q are loaded onto the cargo of the target pallet in accordance with the amount of deviation of the target pallet.

For example, as shown in Figure 3, the unmanned forklift 1 picks up a target pallet P1 and a load W on floor N and transports them to another location. Also, as shown in Figure 2, the unmanned forklift 1 stacks a pallet P brought from another location on top of the load W on the target pallet P1 on floor N. Furthermore, as shown in Figure 7, the unmanned forklift 1 picks up a target pallet P2 and a load W on the load W on the target pallet P1 on floor N and transports them to another location.

<Example of fork width change device structure>
As shown in the front view of Figure 9, the right fork 2R and the left fork 2L are guided by linear guides 25, 26 so that they can move left and right. The actuators of the fork width changing device D, which changes the gap F between the right fork 2R and the left fork 2L, are, for example, electric cylinders 27, 28 with position sensors. That is, by driving the electric cylinder 27 to advance and retract the piston 27A, the right fork 2R moves left and right, and by driving the electric cylinder 28 to advance and retract the piston 28A, the left fork 2L moves left and right. The electric cylinders 27, 28 with position sensors may also be hydraulic cylinders with position sensors, etc.

As shown in the schematic plan view of Figure 10, if the fork spacing F is larger than the width G between the insertion openings Q, Q of the target pallet P1, the fork width change device D operates to narrow the fork spacing F, as indicated by the arrows on the tips of the forks. As shown in the schematic plan view of Figure 11, if the fork spacing F is smaller than the width G between the insertion openings Q, Q of the target pallet P1, the fork width change device D operates to widen the fork spacing F, as indicated by the arrows on the tips of the forks.

Depending on the type of target pallet P1 obtained from the pallet type identification unit 12, the fork width change device D can change the spacing F between the left and right forks 2L, 2R to match the spacing G between the insertion openings Q, Q of that target pallet P1. Therefore, there is no need to prepare an unmanned forklift 1 for each target pallet with a different spacing G between the insertion openings Q, Q. By operating the fork width change device D to set the spacing F between the forks 2L, 2R to correspond to the spacing G between the insertion openings Q, Q, a single unmanned forklift 1 can transport multiple types of pallets.

<Work using the detection results of distance sensors and inventory sensors>
The unmanned forklift 1 performs the following operations, for example, using the detection results of the distance sensor S3, which detects whether or not an object is present ahead, and the detection results of the load sensor S4, which detects whether or not the forks 2 are holding a pallet P.

That is, as shown in Figure 2, the unmanned forklift 1 performs stacking work by loading the second-tier pallet P and cargo W onto the cargo W of the first-tier target pallet P1. The unmanned forklift 1 also performs unstacking work by picking up the second-tier pallet P2 and cargo from the cargo of the first-tier pallet P1 shown in Figure 7 and transporting them to another location.

When performing the stacking operation, the distance sensor S3 can detect the height of the load W on the first pallet P1, and the load presence sensor S4 can detect the height at which the forks 2 are removed after the stacking operation. Furthermore, when performing the unstacking operation, the distance sensor S3 can detect the height of the insertion opening of the second pallet P2, and the load presence sensor S4 can detect the state in which the forks 2 are holding the pallet P2 and load to be held during the unstacking operation. Therefore, the distance sensor S3 and load presence sensor S4 enable reliable stacking and/or unstacking operations.

(Example of stacking operation)
The unmanned forklift 1 is positioned a predetermined distance E away from the target pallet P1 shown in Figure 2, with the imaging device S1 (Figure 1) capturing an image ahead, and the image acquisition unit 11 acquiring the captured image from the imaging device S1. The captured image of the target pallet P1 acquired by the image acquisition unit 11 is input to the learning model of the pallet type identification unit 12, and the pallet type identification unit 12 identifies the type of the target pallet P1.

The distance measuring device S2 (Figure 1) measures the distance to the target pallet P1, and the pallet position and shape acquisition unit 13 acquires the position and shape data of the target pallet P1. The pallet deviation amount detection unit 14 detects the amount of deviation in the position and orientation of the target pallet P1 from its correct position by comparing the stored position and shape data corresponding to the type of target pallet P1 identified by the pallet type identification unit 12 with the position and shape data of the target pallet P1 acquired by the pallet position and shape acquisition unit 13.

The pallet stacking determination unit 17 also processes the photographed image of the target pallet P1 acquired by the image acquisition unit 11, and determines whether the target pallet P1 is present by calculating the number of pallets within a predetermined specific range. In other words, as shown in Figure 2, since the number of pallets within the specific range is one, the pallet stacking determination unit 17 determines that stacking is possible.

The height of the forks 2 is lowered and the unmanned forklift 1 is moved forward to approach the target pallet P1, and the unmanned forklift 1 is stopped at a position where the target pallet P1 and pallet P are separated by, for example, a predetermined distance U, as shown in Figure 12A. At this position, the distance sensor S3 (Figure 1), which detects the presence or absence of an object, detects the target pallet P1, as indicated by arrow V in Figure 12A.

Next, the forks 2 are raised as shown by arrow T1 in Figure 12A, and the position at which the distance sensor S3 at the tip of the forks 2 no longer detects the load W on the target pallet P1 is the height H of the load W on the target pallet P1 shown in Figure 12B. From the position at which the distance sensor S3 no longer detects the load W, the forks 2 are raised a further predetermined distance as shown in Figure 12B.

Next, the unmanned forklift 1 is moved forward as indicated by arrow T2 in Figure 12B, and is stopped at the position shown in Figure 12C where pallet P is superimposed directly on the target pallet P1, in accordance with the amount of deviation in the position and posture of the target pallet P1 from the correct position detected by the pallet deviation amount detection unit 14.

Next, the fork 2 is lowered as indicated by arrow T3 in Figure 12C, and the pallet P and load W are stacked on top of the load W on the target pallet P1 as indicated by arrow T3 in Figure 12D. When the fork 2 is further lowered as indicated by arrow T4 in Figure 12D to the position shown in Figure 12E, the fork 2 is no longer holding the pallet P; in other words, the load sensor S4 detects that the pallet P is not being held, and the lowering of the fork 2 is stopped.

Next, the unmanned forklift 1 is moved backward from the state shown in Figure 12E as indicated by arrow T5 in Figure 12E, and the forks 2 are pulled out from the pallet P, completing the stacking operation.

(Example of unstacking work)
For example, an example of unpacking work will be described in which a second pallet P2 and its cargo are picked up from the cargo on the first pallet P1 shown in FIG. 7 and transported to another location.

When the second pallet P2 is stacked in accordance with the first pallet P1, the second pallet P2 can be picked up based on the position and posture of the first pallet P1.

In this case, the unmanned forklift 1 is stopped in front of the stacked pallets P1, P2 and the load shown in Figure 7, and then the forks 2 are raised. The lifting of the forks 2 is stopped when the distance sensor S3 at the tip of the forks 2 changes from detecting an object in front to no longer detecting the object in front. In this state, the left and right forks 2L, 2R are positioned in front of the insertion openings Q, Q of the second pallet P2, so the unmanned forklift 1 is moved forward a predetermined distance, and then the forks 2 are raised a predetermined distance. When the load presence sensor S4 detects the pallet P2, the forks 2 are holding the load, so the lifting of the forks 2 is stopped after the predetermined distance, and the unmanned forklift 1 is moved backward.

When performing the above-described unstacking operation, for example, in the example image of Figure 7, if the load on pallet P1 is not covered with stretch film, the left-right position of the gap between the left and right cardboard boxes that make up the load may be the same as the left-right positions of the insertion ports Q, Q of pallets P1, P2. In such a case, there is a risk that the distance sensor S3 at the tip of the fork 2 will no longer detect an object in front of the gap between the left and right cardboard boxes. When the distance sensor S3 at the tip of the fork 2 no longer detects an object in front, the fork 2 will stop rising, and it will be erroneously determined that the left and right forks 2L, 2R are positioned in front of the insertion ports Q, Q of the second tier of pallet P2. This will cause a problem in which unstacking operation cannot be performed properly.

One way to resolve this issue is to estimate the approximate height of the underside of the second-tier pallet P2 based on image data when the pallet type identification unit 12 identifies the types of the target pallets P1 and P2 at the first stop position. For example, in an image (e.g., Figure 7) in which the pallet type identification unit 12 has identified the types of the target pallets P1 and P2, the ratio of the number of pixels in the width of the first-tier pallet P1 to the number of pixels in the vertical space between the first-tier pallet P1 and the second-tier pallet P2 is calculated. Furthermore, the actual width and height dimensions of pallet P1 are obtained based on pre-stored shape data for pallets of the same type as the target pallet P1. The approximate height of the underside of the second-tier pallet P2 can be estimated from this ratio and these actual dimensions.

Then, the unpacking work is performed after recognizing that the insertion openings Q, Q of the second tier of pallet P2 are located above the estimated approximate height of the underside of the second tier of pallet P2. That is, after stopping the unmanned forklift 1 in front of the stacked pallets P1, P2 and the cargo, the forks 2 are raised, and when the distance sensor S3 no longer detects an object ahead, the position at which the forks 2 are stopped from raising is set to a position above the estimated approximate height of the underside of the second tier of pallet P2. By operating in this manner, the above-mentioned problem can be resolved.

(Another example of unstacking work)
A 2D-LiDAR distance measurement device S2 is installed on the lift bracket 3, which rises and falls together with the forks 2, and detects the position, shape data, and height of the second-stage pallet P2 while the forks 2 are rising and falling. The 2D-LiDAR is installed on the lift bracket 3 at a location midway between the bases of the forks 2L and 2R. Alternatively, as shown in Figure 1, the distance measurement device S2 installed on the main body 1A of the unmanned forklift 1 can be changed to a 3D-LiDAR, so that the position, shape data, and height of the second-stage pallet P2 can also be detected. In these cases, the distance sensor S3 at the tip of the forks 2 is used to check for interference with pallets P1 and P2.

For example, for stacked pallets P1 and P2 as shown in Figure 7, the unmanned forklift 1 detects the position, shape data, and height of the second pallet P2, and then, with the forks 2 at the height of the insertion opening Q of pallet P2, the unmanned forklift 1 moves forward a predetermined distance so that the left and right forks 2L and 2R are aligned with the insertion openings Q and Q of the second pallet P2. Next, the forks 2 are raised, and since the load presence sensor S4 detects the pallet P2 and the forks 2 are holding a load, the forks 2 are stopped from raising after being raised a predetermined distance, and the unmanned forklift 1 moves backward.

In the above embodiment of the present invention, we have described a case where the target pallet is on floor N, but the target pallet may also be on the bed of a truck. An example of work when the target pallet is on the bed of a truck is shown below.

(1) Devanning of the first layer of pallets and cargo on the truck bed Devanning of the first layer of pallets and cargo on the truck bed is performed using the same operation as the devanning of the second layer of pallet P2 and its cargo in the "Another example of devanning operation" above.

(2) Devanning the upper pallet and cargo of the two-tiered pallets and cargo on the truck bed (tiered disassembly)
The upper pallet and cargo of the two upper and lower tiers of pallets and cargo on the truck bed are devanned (devanned) using the same operation as the devanning of the second tier pallet P2 and its cargo in the "Another example of devanning operation" above.

If the upper pallet is stacked to fit the lower pallet on the truck bed, the upper pallet and cargo on the truck bed may be unloaded as follows:

That is, the position, shape data, and height of the lower pallet on the truck bed are detected using the same operations as for unloading the second tier of pallet P2 and its cargo in the "Another Operational Example of Unloading Work" described above. Then, after stopping the unmanned forklift 1 in front of the two tiers of pallets and cargo, the forks 2 are raised, and the lifting of the forks 2 is stopped when the distance sensor S3 at the tip of the forks 2 changes from detecting an object in front to no longer detecting an object in front. The unmanned forklift 1 moves forward a predetermined distance, the forks 2 rise a predetermined distance, the load sensor S4 stops the lifting of the forks 2, and the unmanned forklift 1 moves backward, thereby unloading the upper pallet and cargo from the truck bed.

(3) Vanning (stacking) pallets and cargo onto a single layer of pallets and cargo on the truck bed.
A 2D-LiDAR distance measurement device S2 is installed on the lift bracket 3, which rises and falls together with the forks 2, and detects the position, shape data, and height of a single layer of pallets on the truck bed while the forks 2 are rising and falling. Alternatively, as shown in Figure 1, by changing the distance measurement device S2 installed on the main body 1A of the unmanned forklift 1 to a 3D-LiDAR, it becomes possible to detect the position, shape data, and height of a single layer of pallets on the truck bed.

Then, in the "Stacking Operation Example" above, the target pallet P1 is replaced with a single layer of pallets on the truck bed, and the pallet and cargo are vanned (stacking) onto the single layer of pallets and cargo on the truck bed.

In the work examples (1) to (3) above, the learning model used to identify the type of pallet is a trained model that has undergone machine learning using images of the first pallet (lower pallet) or second pallet (upper pallet) on the truck bed as training data.

<Operation and effect of the unmanned forklift according to the embodiment of the present invention>
Even if the objects to be transported by the unmanned forklift 1 are multiple types of pallets with different shapes, there is no limit to the types of pallets that can be handled, and the photographed image of the target pallet acquired by the image acquisition unit 11 is input to the learning model of the pallet type identification unit 12, which has been trained by machine learning on combinations of images of each pallet and the type of pallet, and the pallet type identification unit 12 identifies the type of the target pallet. By inputting the photographed image, the pallet type identification unit 12 identifies the type of the target pallet with high accuracy. The pallet misalignment detection unit 14 can then compare and collate the stored position and shape data corresponding to the type of target pallet identified by the pallet type identification unit 12 with the position and shape data of the target pallet acquired by the pallet position and shape acquisition unit 13.

This allows for highly accurate detection of the deviation in the position and posture of the target pallet relative to the pallet's normal position, and prevents accidents such as items other than pallets placed on the floor (such as cardboard boxes) being mistakenly recognized as a specific type of pallet, resulting in interference between the fork 2 and the pallet or item. Furthermore, the calculation time required to compare and collate pallet shape data is shortened, improving work speed, eliminating the need for expensive computers with high processing power.

After detecting the amount of misalignment of the target pallet with high accuracy, the unmanned forklift 1 can, for example, use the amount of misalignment detected by the pallet misalignment detection unit 14 to perform tasks including inserting the forks 2 into the insertion openings of the target pallet, such as picking up a pallet and cargo from the floor and transporting it to another location, or picking up a pallet and cargo from a second level on top of the load on the first level pallet and transporting it to another location. The unmanned forklift 1 can also perform stacking tasks, such as loading a pallet and cargo held by inserting the forks 2 into the insertion openings onto the load on the target pallet in accordance with the amount of misalignment of the target pallet.

The above description of the embodiments is by way of example only and is not intended to be limiting. Various improvements and modifications may be made without departing from the scope of the present invention.

DESCRIPTION OF SYMBOLS 1 Unmanned forklift 1A Main body 2 Fork 2L Left fork 2R Right fork 3 Lift bracket 4 Inner mast 5 Outer mast 6 Tilt cylinder 7 Lift chain 8 Cableveyor 10 Processing device 11 Image acquisition unit 12 Pallet type identification unit 13 Pallet position/shape acquisition unit 14 Pallet deviation amount detection unit 15 Deviation amount error determination unit 16 Pallet shape determination unit 17 Pallet stacking determination unit 18 Travel control unit 19, 20 Mounting plate 21 Support plate 22 Seesaw plate 22A Operation piece 22B Detection piece 23 Stop plate 24 Tension coil spring 25, 26 Linear guide 27, 28 Electric cylinder with position sensor 27A, 28A Piston A Load handling device B Moving device C Control device D Fork width change device E Predetermined distance F Fork spacing G Width between insertion openings H Load height I Support plate J Bracket K Frame L0 Line segment L1 based on stored point cloud data corresponding to the type of target pallet Line segment M based on point cloud data of the target pallet acquired by the pallet position and shape acquisition unit Mast N Floor P Pallet P1, P2 Target pallet Q Insertion port R Support shaft S1 Photography device S2 Distance measurement device S3 Distance sensor S4 Load sensor S5 Self-position recognition sensor S6 Obstacle detection sensors T1 to T5 Operation U Predetermined distance V Object detection by distance sensor W Load

Claims (9)

An unmanned forklift equipped with a pair of forks that automatically performs loading and unloading work using pallets,
an image acquisition unit that acquires a photographed image from an image acquisition device that photographs a front side of the unmanned forklift;
a pallet type identification unit that has a trained learning model that has undergone machine learning on the combination of each pallet image and the type of pallet for multiple types of pallets, and identifies the type of the target pallet by inputting the photographed image of the target pallet acquired by the image acquisition unit into the learning model;
a pallet position and shape acquisition unit that acquires position and shape data of the target pallet from a distance measurement device that measures the distance to the target pallet;
a pallet deviation amount detection unit that stores position and shape data of each of the plurality of types of pallets in advance, and detects the amount of deviation of the position and posture of the target pallet from its normal position by comparing the stored position and shape data corresponding to the type of target pallet identified by the pallet type identification unit with the position and shape data of the target pallet acquired by the pallet position and shape acquisition unit;
(A) an operation including inserting the forks into the insertion opening of the target pallet using the detected amount of deviation;
(B) a stacking operation in which the pallet and cargo held by the forks inserted into the insertion openings are loaded onto the cargo of the target pallet in accordance with the amount of deviation of the target pallet;
a travel control unit that controls travel to perform at least one of the above tasks;
Equipped with
Unmanned forklift. The position and shape data is data on the height of the pallet including its insertion opening,
The pallet deviation amount detection unit superimposes and compares a line segment based on the stored position and shape data corresponding to the type of the target pallet with a line segment based on the position and shape data of the target pallet acquired by the pallet position and shape acquisition unit.
The unmanned forklift according to claim 1. a deviation error determination unit that determines an error when the deviation detected by the pallet deviation detection unit exceeds a predetermined threshold value;
3. The unmanned forklift according to claim 1 or 2. The system further includes a pallet shape determination unit that compares the shape data of the target pallet acquired by the pallet position/shape acquisition unit with pre-stored shape data of a pallet of the same type as the target pallet, and determines that there is an error in the acquired shape data of the target pallet if the difference between the shape data and the pre-stored shape data is greater than a predetermined threshold value.
The unmanned forklift according to any one of claims 1 to 3. The system further includes a pallet stacking determination unit that performs image processing on the photographed image of the target pallet acquired by the image acquisition unit to calculate the number of pallets within a specific range that is set in advance to include the target pallet, thereby determining whether or not the target pallet is stacked.
The unmanned forklift according to any one of claims 1 to 4. The pallet stacking determination unit has previously performed machine learning on pallet images of multiple tiers of pallets, using training data.
The unmanned forklift according to claim 5. The system further includes a fork width changer that changes the spacing between the forks to match the width between the insertion openings of the target pallet based on the type of the target pallet obtained from the pallet type identification unit.
The unmanned forklift according to any one of claims 1 to 6. a distance sensor disposed at the tip of the fork to detect the presence or absence of an object ahead;
a presence sensor that detects whether the forks are holding the pallet;
Furthermore,
Using the result of detecting the presence or absence of the object by the distance sensor and the result of detecting whether the pallet is being held by the inventory sensor,
Stacking operations in which a second pallet and load are loaded onto the load of the first pallet, and/or
A pallet unpacking operation is performed to pick up the second pallet and cargo on the first pallet and transport it to another location.
The unmanned forklift according to any one of claims 1 to 7. At a first stop position, the photographing device photographs the target pallet and the pallet type identification unit identifies the type of the target pallet, and at a second stop position closer to the target pallet than the first stop position, the distance measuring device measures the distance to the target pallet and the pallet position/shape acquisition unit acquires position/shape data of the target pallet.
The unmanned forklift according to any one of claims 1 to 8.