JP6884240B1 - Unmanned work vehicle and cargo handling system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an unmanned work vehicle capable of changing a traveling speed according to a situation in a warehouse. An unmanned work vehicle 200 that performs traveling and cargo handling work, the vehicle body, an image data generation unit 201 that generates image data in front of the vehicle body, and the degree of safety of the traveling route when the image data is input. A learning model unit 202 machine-learned to generate a safety score indicating the above, a processing unit 203 that acquires a safety score from the learning model unit 202 by inputting image data into the learning model unit 202, and a vehicle body. It is characterized by including a traveling control unit 204 that controls traveling of the vehicle body according to a speed command value related to traveling speed and corrects a speed command value according to a safety score. [Selection diagram] Fig. 1

Description

The present invention relates to an unmanned work vehicle and a cargo handling system.

In the warehouse, a plurality of unmanned work vehicles (for example, a laser-guided unmanned forklift) run and handle cargo under the control of a management device. The management device determines the travel route of each unmanned work vehicle, and notifies the unmanned work vehicle of the determined travel route. The unmanned work vehicle that receives the notification travels according to the notified travel route while recognizing its own position.

The laser-guided unmanned forklift is provided with a laser scanner (see, for example, Patent Document 1). The laser scanner projects a laser to the surroundings while rotating the laser light source, and detects the reflected light from a plurality of reflecting plates arranged in the warehouse. The laser-guided unmanned forklift stores the position of the reflector on the map and calculates its own position based on the principle of triangulation. As a result, the laser-guided unmanned forklift truck can travel on the notified travel route while recognizing its own position.

Japanese Unexamined Patent Publication No. 8-161039

Since the conventional unmanned forklift travels on the traveling route at a preset traveling speed, there is a problem that the traveling speed cannot be changed according to the situation in the warehouse.

The present invention has been made in view of the above circumstances, and an object of the present invention is an unmanned work vehicle capable of changing the traveling speed according to the situation in the warehouse and a cargo handling system including the unmanned work vehicle. Is to provide.

In order to solve the above problems, the unmanned work vehicle according to the present invention
An unmanned work vehicle that travels and handles cargo.
A car body equipped with a cargo handling device and
An image data generation unit that captures an image including the front of the vehicle body and generates image data,
When the image data is input, a learning model unit that is machine-learned to generate a safety score indicating the degree of safety of the traveling route of the vehicle body by using a machine learning algorithm having a predetermined parameter, and a learning model unit.
A processing unit that acquires the safety score from the learning model unit by inputting the image data into the learning model unit, and a processing unit.
A traveling control unit that controls traveling of the vehicle body according to a speed command value related to the traveling speed of the vehicle body and corrects the speed command value according to the safety score.
It is characterized by having.

In the above unmanned work vehicle
The travel control unit can be configured to make a first correction to reduce the speed command value when the safety score is smaller than a predetermined first threshold value.

In the above unmanned work vehicle
The travel control unit can be configured to perform a second correction that increases the speed command value when the safety score is larger than a predetermined second threshold value.

In the above unmanned work vehicle
The cargo handling device includes a fork that moves up and down.
The travel control unit permits the ascending / descending motion during traveling when the safety score is larger than a predetermined third threshold value, while the traveling control unit permits the ascending / descending operation during traveling, while the traveling control unit is traveling when the safety score is equal to or less than the third threshold value. It can be configured to prohibit the elevating operation.

In order to solve the above problems, the cargo handling system according to the present invention
A cargo handling system including a management device and a plurality of unmanned work platforms that travel and carry out cargo handling work under the control of the management device.
The unmanned work vehicle
A car body equipped with a cargo handling device and
An image data generation unit that captures an image including the front of the vehicle body and generates image data,
When the image data is input, a learning model unit that is machine-learned to generate a safety score indicating the degree of safety of the traveling route of the vehicle body by using a machine learning algorithm having a predetermined parameter, and a learning model unit.
A processing unit that acquires the safety score from the learning model unit by inputting the image data into the learning model unit, and a processing unit.
A traveling control unit that controls traveling of the vehicle body according to a speed command value related to the traveling speed of the vehicle body and corrects the speed command value according to the safety score.
It is characterized by having.

In the above cargo handling system
The processing unit transmits image data with a safety score in which the image data input to the learning model unit is associated with the safety score acquired from the learning model unit to the management device.
The management device is
A collection unit that collects image data with the safety score and generates learning data,
A parameter update unit that performs machine learning based on the learning data, generates update data for updating the parameters included in the learning model unit, and transmits the update data to the learning model unit.
With
The learning model unit can be configured to update the parameters based on the update data.

According to the present invention, it is possible to provide an unmanned work vehicle whose traveling speed can be changed according to a situation in a warehouse and a cargo handling system including the unmanned work vehicle.

It is a block diagram of the cargo handling system which concerns on 1st Embodiment. It is a side view of the unmanned work vehicle (unmanned forklift) which concerns on 1st Embodiment. It is a top view of the cargo handling system which concerns on 1st Embodiment. It is a block diagram of the cargo handling system which concerns on 2nd Embodiment.

Hereinafter, embodiments of the unmanned work vehicle and the cargo handling system according to the present invention will be described with reference to the accompanying drawings.

[First Embodiment]
FIG. 1 shows a block diagram of a cargo handling system 1 according to the first embodiment of the present invention. The cargo handling system 1 includes a management device 100 and at least one laser-guided unmanned forklift truck 200 (corresponding to the “unmanned work vehicle” of the present invention).

The management device 100 includes a communication unit 101, a general control unit 102, and a display unit 103. As shown in FIG. 3, the management device 100 may be provided outside the facility (in this embodiment, the warehouse 2 having a plurality of shelves 3) on which the unmanned forklift 200 travels, or may be provided inside the facility. Good.

The communication unit 101 is configured to perform wireless communication with the unmanned forklift 200 registered in advance in the management device 100.

The integrated control unit 102 is configured to manage the traveling and cargo handling work of the unmanned forklift 200. For example, the overall control unit 102 creates a schedule for the cargo handling work of the unmanned forklift 200 and determines a traveling route for smoothly performing the cargo handling work. The integrated control unit 102 notifies the unmanned forklift 200 of the traveling route via the communication unit 101.

The display unit 103 is composed of, for example, a liquid crystal display. The display unit 103 displays running information and cargo handling work information of the unmanned forklift 200.

The unmanned forklift 200 is configured to perform traveling and cargo handling operations under the control of the management device 100. As shown in FIG. 2, the unmanned forklift 200 includes a vehicle body 210, a cargo handling device 211, and a laser scanner 212 provided on the upper portion of the vehicle body 210. The cargo handling device 211 includes a mast provided so as to be horizontally movable in the front and rear of the vehicle body 210, and a fork provided in the mast so as to be able to move up and down.

As shown in FIG. 3, the laser scanner 212 projects the laser L to the surroundings while rotating the laser light source, and detects the reflected light L'from the plurality of reflectors 4 arranged in the warehouse 2. The calculation unit of the laser scanner 212 stores the position of the reflector 4 on a predetermined map, and calculates the current location of the vehicle body 210 based on the principle of triangulation. In this way, the unmanned forklift 200 travels according to the notified travel route while acquiring the current location information regarding the current location of the vehicle body 210.

With reference to FIG. 1 again, the unmanned forklift 200 includes an image data generation unit 201, a learning model unit 202, a processing unit 203, and a traveling control unit 204.

The image data generation unit 201 is configured to include a photographing means and an image processing means. The photographing means captures an image (still image and / or moving image) including the front of the vehicle body 210 and outputs the image to the image processing means. The image processing means generates image data that can be input to the learning model unit 202 based on the image.

When the image data generated by the image data generation unit 201 is input, the learning model unit 202 uses a machine learning algorithm such as a neural network having a predetermined parameter to indicate the degree of safety of the traveling route. A trained model machine-learned to generate scores.

In machine learning of a trained model, a large amount of teacher data is input using a machine learning algorithm such as a neural network as described above. The teacher data includes data in which a predetermined safety score is associated with image data obtained by photographing a traveling route in the warehouse 2. Numerical parameters (numerical parameters 1 to 5 in this embodiment) are used as the safety score. Numerical parameters are set by a person or a computer. For example, when traveling near the shelf 3 and there is a person on the traveling route, it is judged that the safety is low and a small numerical parameter is set. It can be inferred that there is a certain relationship such as a correlation between the condition of the traveling route indicated by the image data and the degree of safety.

The processing unit 203 is configured to acquire a safety score from the learning model unit 202 by inputting the image data acquired from the image data generation unit 201 into the learning model unit 202. The processing unit 203 transmits data associated with the safety score acquired from the learning model unit 202 and the current location information acquired by the laser scanner 212 to the management device 100. Even if the processing unit 203 associates the safety score with the image data input to the learning model unit 202, generates the image data with the safety score, and transmits the image data with the safety score to the management device 100. Good.

The processing unit 203 and the traveling control unit 204 include, for example, at least one microcomputer, and various functions of the processing unit 203 and the traveling control unit 204 are realized by the CPU of the microcomputer executing a predetermined program or the like.

The travel control unit 204 is configured to control the travel of the vehicle body 210 according to a speed command value related to the travel speed of the vehicle body 210. For example, when the management device 100 notifies the travel route, the travel control unit 204 sets a speed command value so that the travel speed of the vehicle body 210 becomes a preset speed. The travel control unit 204 that sets the speed command value determines whether or not to perform correction at a predetermined cycle, and rotates the travel motor provided on the vehicle body 210 based on the speed command value or the corrected speed command value. The traveling control of the vehicle body 210 is performed by calculating the command value of the speed and controlling the traveling motor.

The travel control unit 204 is configured to correct the speed command value according to the safety score acquired from the processing unit 203. The travel control unit 204 stores data in which the relationship between the safety score, the speed command value, and the ascending / descending motion of the fork is defined, as shown in Table 1, for example. In Table 1, the speed command value when the speed command value is not corrected (when there is no correction) is V.

When the numerical parameter of the safety score is 5, the traveling control unit 204 makes a correction (second correction) for increasing the speed command value V to the speed command value V1 (> V). The speed command value V1 is, for example, a speed command value V multiplied by a predetermined coefficient K1 and a gain G1. The coefficient K1 is a fixed value determined by the dimensions of the vehicle body 210 and the like, and the gain G1 is a parameter that can be changed according to the environment and the like. The same applies to the coefficients K2 and K3 and the gains G2 and G3 described later.

Further, when the numerical parameter of the safety score is 5, the traveling control unit 204 permits the fork to move up and down during traveling. As a result, the unmanned forklift 200 can move to the cargo handling work position while raising and lowering the fork, so that the fork lifting time at the cargo handling work position can be shortened and the cargo handling work can be speeded up.

When the numerical parameter of the safety score is 4, the travel control unit 204 permits the fork to move up and down during travel without correcting the speed command value V.

When the numerical parameter of the safety score is 3, the travel control unit 204 prohibits the fork from moving up and down during travel without correcting the speed command value V. As a result, the unmanned forklift 200 can travel more safely.

When the numerical parameter of the safety score is 2, the traveling control unit 204 makes a correction (first correction) for reducing the speed command value V to the speed command value V2 (<V), and raises and lowers the fork during traveling. Is prohibited. The speed command value V2 is, for example, a speed command value V multiplied by a predetermined coefficient K2 and a gain G2.

When the numerical parameter of the safety score is 1, the traveling control unit 204 makes a correction (first correction) for reducing the speed command value V to the speed command value V3 (<V2), and raises and lowers the fork during traveling. Is prohibited. The speed command value V3 is, for example, a speed command value V multiplied by a predetermined coefficient K3 and a gain G3.

As described above, in the unmanned forklift truck 200 according to the present embodiment, the traveling control unit 204 makes a correction to reduce the speed command value V when the safety score is smaller than 3 (that is, in the case of 2 or 1). , If the safety score is greater than 4 (ie, 5), a correction is made to increase the speed command value. Therefore, according to the unmanned forklift truck 200 according to the present embodiment, flexible traveling in which the traveling speed is changed according to the condition of the traveling route in the warehouse 2 becomes possible.

[Second Embodiment]
FIG. 4 shows a block diagram of the cargo handling system 11 according to the second embodiment of the present invention. The cargo handling system 11 includes a management device 300 and a plurality of laser-guided unmanned forklifts 200. Since the configuration of the unmanned forklift 200 is the same as that of the first embodiment, the description thereof will be omitted here.

The management device 300 manages the traveling and cargo handling operations of a plurality of unmanned forklifts 200. The management device 300 is common to the management device 100 of the first embodiment except that the collection unit 301 and the parameter update unit 302 are provided.

The collecting unit 301 is configured to collect image data with a safety score from the unmanned forklift 200 and generate learning data that can be input to the parameter updating unit 302 based on the image data with the safety score. ..

Further, the collecting unit 301 collects image data (image data without a safety score generated by the image data generation unit 201) from the unmanned forklift 200, and associates the image data with the safety score for learning. Data may be generated. In this case, the learning data can be generated in the same manner as the teacher data of the first embodiment. That is, the collecting unit 301 uses numerical parameters (numerical parameters 1 to 5 in this embodiment) as the safety score, and sets the safety score for each image data. For example, when traveling near the shelf 3 and there is a person on the traveling route, it is judged that the safety is low and a small numerical parameter is set. It can be inferred that there is a certain relationship such as a correlation between the condition of the traveling route indicated by the image data and the degree of safety.

The parameter update unit 302 generates update data for updating the parameters of the machine learning algorithm (for example, the parameters of the weighting of the neural network) included in the learning model unit 202 of the unmanned forklift 200. The parameter update unit 302 performs machine learning based on the learning data by using a machine learning algorithm such as a neural network in order to generate the update data. The parameter update unit 302 transmits the generated update data to a plurality of unmanned forklifts 200 via the communication unit 101.

The learning model unit 202 of the unmanned forklift 200 updates the parameters of the machine learning algorithm based on the received update data. This allows the learning model unit 202 to generate a safety score based on a machine learning algorithm with updated parameters.

[Modification example]
Although the embodiment of the unmanned work vehicle and the cargo handling system according to the present invention has been described above, the present invention is not limited to the above embodiment.

The unmanned work vehicle of the present invention has a vehicle body provided with a cargo handling device, an image data generation unit that captures an image including the front of the vehicle body and generates image data, and a predetermined parameter when the image data is input. A learning model unit that has been machine-learned to generate a safety score that indicates the degree of safety of the vehicle's travel route using its own machine learning algorithm, and a learning model unit that inputs image data to the learning model unit. It is provided with a processing unit that acquires a safety score from the vehicle, and a traveling control unit that controls the traveling of the vehicle body according to the speed command value related to the traveling speed of the vehicle body and corrects the speed command value according to the safety score. If, the configuration can be changed as appropriate.

For example, the driving control unit makes a correction to reduce the speed command value when the safety score is smaller than the predetermined first threshold value, and / or the safety score is from the predetermined second threshold value (> first threshold value). Can be configured to make a correction to increase the speed command value when is also large.

In the above embodiment, the numerical parameters of the safety score are set in the range of 1 to 5, but the range can be changed as appropriate.

The collecting unit 301 and the parameter updating unit 302 according to the second embodiment may be provided in the unmanned forklift 200 instead of the management device 300. However, from the viewpoint that a large amount of data can be acquired, it is preferable that the management device 300 includes a collection unit 301 and a parameter update unit 302.

The unmanned work vehicle of the present invention is not limited to the laser-guided unmanned forklift, and may be another type of unmanned forklift or automatic guided vehicle.

1,11 Cargo handling system 2 Warehouse 3 Shelf 4 Reflector 100 Management device 101 Communication unit 102 General control unit 103 Display unit 200 Unmanned forklift 201 Image data generation unit 202 Learning model unit 203 Processing unit 204 Travel control unit 210 Body 211 Cargo handling device 212 Laser scanner 300 Management device 301 Collection unit 302 Parameter update unit

Claims (3)

An unmanned work vehicle that travels and handles cargo.
A car body equipped with a cargo handling device and
An image data generation unit that captures an image including the front of the vehicle body and generates image data,
When the image data is input, a learning model unit that is machine-learned to generate a safety score indicating the degree of safety of the traveling route of the vehicle body by using a machine learning algorithm having a predetermined parameter, and a learning model unit.
A processing unit that acquires the safety score from the learning model unit by inputting the image data into the learning model unit, and a processing unit.
A traveling control unit that controls traveling of the vehicle body according to a speed command value related to the traveling speed of the vehicle body and corrects the speed command value according to the safety score.
Equipped with a,
When the safety score is smaller than a predetermined first threshold value, the travel control unit makes a first correction for reducing the speed command value, while the safety score is larger than a predetermined second threshold value. The second correction to increase the speed command value is performed.
The cargo handling device includes a fork that moves up and down.
The travel control unit permits the ascending / descending motion during traveling when the safety score is larger than a predetermined third threshold value, while the traveling control unit permits the ascending / descending operation during traveling, while the traveling control unit is traveling when the safety score is equal to or less than the third threshold value. An unmanned work vehicle characterized by prohibiting the ascending / descending operation. A cargo handling system including a management device and a plurality of unmanned work platforms that travel and carry out cargo handling work under the control of the management device.
The unmanned work vehicle
A car body equipped with a cargo handling device and
An image data generation unit that captures an image including the front of the vehicle body and generates image data,
When the image data is input, a learning model unit that is machine-learned to generate a safety score indicating the degree of safety of the traveling route of the vehicle body by using a machine learning algorithm having a predetermined parameter, and a learning model unit.
A processing unit that acquires the safety score from the learning model unit by inputting the image data into the learning model unit, and a processing unit.
A traveling control unit that controls traveling of the vehicle body according to a speed command value related to the traveling speed of the vehicle body and corrects the speed command value according to the safety score.
Equipped with a,
When the safety score is smaller than a predetermined first threshold value, the traveling control unit makes a first correction for reducing the speed command value, while the safety score is larger than a predetermined second threshold value. The second correction to increase the speed command value is performed.
The cargo handling device includes a fork that moves up and down.
The travel control unit permits the ascending / descending motion during traveling when the safety score is larger than a predetermined third threshold value, while the traveling control unit permits the ascending / descending operation during traveling, while the traveling control unit is traveling when the safety score is equal to or less than the third threshold value. A cargo handling system characterized in that the elevating operation is prohibited. The processing unit transmits image data with a safety score in which the image data input to the learning model unit is associated with the safety score acquired from the learning model unit to the management device.
The management device is
A collection unit that collects image data with the safety score and generates learning data,
A parameter update unit that performs machine learning based on the learning data, generates update data for updating the parameters included in the learning model unit, and transmits the update data to the learning model unit.
With
The cargo handling system according to claim 2 , wherein the learning model unit updates the parameters based on the update data.

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