JP2021089569A - Cargo handling system - Google Patents

Abstract

To provide an unmanned work vehicle which can adjust a melody output state in accordance with situations in a facility.SOLUTION: The unmanned work vehicle comprises a vehicle body, a sound output unit 201 which outputs a prescribed melody during travel of the vehicle body, an image data generation unit 203 which captures an image including the front of the vehicle body to generate image data, a learning model unit 204 which is machine-trained to, in response to input of the image data, generate a safety score indicative a level of safety of a travel route of the vehicle body by using a machine learning algorithm having a prescribed parameter, a processing unit 205 which inputs the image data to the learning model unit 204 to acquire the safety score from the learning model unit 204, and a sound control unit 206 which adjusts an output state of the melody in accordance with the safety score.SELECTED DRAWING: Figure 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

The unmanned forklift runs while outputting a melody for the purpose of calling attention to the workers in the warehouse. However, the conventional unmanned forklift has a problem that the output state of the melody cannot be adjusted 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 and a cargo handling system capable of adjusting the output state of a melody according to the situation in a facility (for example, in a warehouse). 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.
With the car body
An audio output unit that outputs a predetermined melody while the vehicle body is running,
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 voice control unit that adjusts the output state of the melody according to the safety score,
It is characterized by having.

The above unmanned work vehicle
An alarm sound output unit that outputs an alarm sound under the control of the voice control unit is provided.
It is preferable that the voice control unit causes the alarm sound output unit to output the alarm sound when the safety score is smaller than a predetermined threshold value.

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
With the car body
An audio output unit that outputs a predetermined melody while the vehicle body is running,
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 voice control unit that adjusts the output state of the melody according to the safety score,
It is characterized by having.

In the above cargo handling system
The unmanned work vehicle includes an alarm sound output unit that outputs an alarm sound under the control of the voice control unit.
It is preferable that the voice control unit causes the alarm sound output unit to output the alarm sound when the safety score is smaller than a predetermined threshold value.

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 and a cargo handling system in which the output state of a melody can be adjusted according to a situation in a facility.

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 a voice output unit 201, an alarm sound output unit 202, an image data generation unit 203, a learning model unit 204, a processing unit 205, and a voice control unit 206. Be prepared.

The voice output unit 201 is composed of, for example, at least one speaker, and outputs a predetermined melody while the vehicle body 210 is traveling under the control of the voice control unit 206. The output melody is stored in the voice control unit 206.

The alarm sound output unit 202 is composed of, for example, at least one speaker, and outputs an alarm sound under the control of the voice control unit 206. Further, when an obstacle sensor is provided in the front part of the vehicle body 210, an alarm is issued when the obstacle sensor detects an obstacle existing in front of the vehicle body 210 (for example, a luggage placed on a traveling route). The sound output unit 202 may output an alarm sound.

The image data generation unit 203 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 204 based on the image.

When the image data generated by the image data generation unit 203 is input, the learning model unit 204 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 205 is configured to acquire a safety score from the learning model unit 204 by inputting the image data acquired from the image data generation unit 203 into the learning model unit 204. The processing unit 205 associates the image data input to the learning model unit 204 with the safety score acquired from the learning model unit 204, generates image data with the safety score, and manages the image data with the safety score. It may be transmitted to the device 100.

The processing unit 205 and the voice control unit 206 include, for example, at least one microcomputer, and various functions of the processing unit 205 and the voice control unit 206 are realized by the CPU of the microcomputer executing a predetermined program or the like.

The voice control unit 206 is configured to adjust the output state of the melody and the alarm sound according to the safety score acquired from the processing unit 205. The voice control unit 206 stores data in which the relationship between the safety score and the output state of the melody and the alarm sound is defined as shown in Table 1.

When the numerical parameter of the safety score is in the range of 5 to 2, the voice control unit 206 outputs the melody so that the volume of the melody becomes louder as the numerical parameter of the safety score becomes smaller (the safety becomes lower). Adjust the condition.

When the numerical parameter of the safety score is 1, the voice control unit 206 causes the voice output unit 201 to stop the output of the melody, and causes the alarm sound output unit 202 to output the alarm sound. Instead of stopping the output of the melody, the voice control unit 206 may output the alarm sound to the alarm sound output unit 202 in a state where the volume of the melody is reduced from 4.

As described above, according to the unmanned forklift truck 200 according to the present embodiment, the output state of the melody can be adjusted according to the safety score, in other words, according to the situation of the traveling route in the warehouse 2. As a result, it becomes possible to call attention to the operator more efficiently, and for example, the operator becomes more likely to notice the existence of the unmanned forklift 200 before the unmanned forklift 200 outputs an alarm sound.

[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 203) 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 weighting parameters of the neural network) included in the learning model unit 204 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 204 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 204 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 includes a vehicle body, an audio output unit that outputs a predetermined melody while the vehicle body is running, an image data generation unit that captures an image including the front of the vehicle body and generates image data, and image data. Is input, a learning model unit machine-learned to generate a safety score indicating the degree of safety of the traveling route of the vehicle body and image data are learned using a machine learning algorithm having a predetermined parameter. If it is provided with a processing unit that acquires a safety score from the learning model unit by inputting to the model unit and a voice control unit that adjusts the output state of the melody according to the safety score, the configuration can be changed as appropriate.

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.

In the first 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. Further, the voice control unit 206 adjusts the output state of the melody so that the volume of the melody increases as the numerical parameter of the safety score decreases. The playback speed and the like may be changed.

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.

1, 11 Cargo handling system 2 Warehouse 3 Shelf 4 Reflector 100, 300 Management device 101 Communication unit 102 General control unit 103 Display unit 200 Unmanned forklift 201 Voice output unit 202 Alarm sound output unit 203 Image data generation unit 204 Learning model unit 205 Processing Unit 206 Voice control unit 210 Body 211 Cargo handling device 212 Laser scanner 301 Collection unit 302 Parameter update unit

Claims (5)

An unmanned work vehicle that travels and handles cargo.
With the car body
An audio output unit that outputs a predetermined melody while the vehicle body is running,
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 voice control unit that adjusts the output state of the melody according to the safety score,
An unmanned work vehicle characterized by being equipped with. An alarm sound output unit that outputs an alarm sound under the control of the voice control unit is provided.
The unmanned work vehicle according to claim 1, wherein the voice control unit causes the alarm sound output unit to output the alarm sound when the safety score is smaller than a predetermined threshold value. 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
With the car body
An audio output unit that outputs a predetermined melody while the vehicle body is running,
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 voice control unit that adjusts the output state of the melody according to the safety score,
A cargo handling system characterized by being equipped with. The unmanned work vehicle includes an alarm sound output unit that outputs an alarm sound under the control of the voice control unit.
The cargo handling system according to claim 3, wherein the voice control unit causes the alarm sound output unit to output the alarm sound when the safety score is smaller than a predetermined threshold value. 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 3 or 4, wherein the learning model unit updates the parameters based on the update data.

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