JP6653033B1 - Cargo handling system and control method - Google Patents

Abstract

When a laser type manned unmanned forklift is switched from an unmanned operation mode to a manned operation mode, an operator can safely switch. A collection unit (40) for collecting teacher data (5) based on environmental information when switching from an unmanned driving mode to a manned driving mode, machine learning is performed from the teacher data (5) collected by the collection unit (40), and machine learning is performed. A learning model generation unit 41 that generates and stores a learning model according to the above, an acquisition unit 45 that acquires the current environment information 60 at predetermined time intervals, and applies the learning model to the current environment information 60 to switch from the unmanned driving mode. A prediction unit for predicting whether or not the switching is performed to switch to the manned operation mode; and performing the predetermined control such that the operator can safely switch from the unmanned operation mode to the manned operation mode at the time of the switching predicted by the prediction unit. And a control unit 44. [Selection] Figure 2

Description

  The present invention relates to a cargo handling system having a laser type manned unmanned forklift and a control method.

  A manned unmanned forklift can switch between a manned operation mode operated by an operator's manual operation and an unmanned operation mode operated automatically without an operator's manual operation (for example, see Patent Document 1). The operator switches between the manned operation mode and the unmanned operation mode as needed, and uses the manned unmanned forklift.

  Further, the laser type unmanned forklift includes a laser scanner (for example, see Patent Documents 2 and 3). The laser scanner transmits and receives a laser beam while rotating the laser horizontally 360 degrees. A laser unmanned forklift recognizes a plurality of reflectors arranged along a traveling route in a warehouse with a laser scanner.

  The reflector is fixed in the warehouse, and its position is stored on the map. A laser unmanned forklift recognizes a plurality of reflectors with a laser scanner and calculates a current position based on the principle of triangulation. The laser type unmanned forklift travels on a preset route based on the calculated current position.

  Further, as disclosed in Patent Document 4, there is a shelf for storing luggage. A plurality of shelves are installed in the warehouse and store a plurality of packages. In the warehouse, a laser type unmanned forklift and a plurality of shelves are installed, and the laser type unmanned forklift takes in and out of luggage from the shelves.

  By the way, when switching from the unmanned operation mode to the manned operation mode, the operator approaches the manned unmanned forklift traveling in the unmanned operation mode and presses the switching button provided on the manned unmanned forklift, but the manned unmanned forklift travels. And the environment surrounding the manned and unmanned forklifts has put operators at risk.

JP 2010-222108 A JP-A-8-161039 JP-A-8-166821 JP 2003-20102 A

  Therefore, an object to be solved by the present invention is a cargo handling system and a control method for enabling an operator to safely switch when a laser type manned unmanned forklift is switched from an unmanned operation mode to a manned operation mode. .

In order to solve the above-mentioned problems, a cargo handling system according to the present invention includes:
A cargo handling system including a facility and a laser type manned unmanned forklift that performs traveling and cargo handling work in the facility,
Laser type manned unmanned forklift can be switched between manned operation mode and unmanned operation mode, operates by manual operation of operator during manned operation mode, automatically operates by laser guidance during unmanned operation mode,
The cargo handling system
A collection unit that collects teacher data based on environmental information when switching from the unmanned driving mode to the manned driving mode;
A learning model generation unit that performs machine learning from the teacher data collected by the collection unit, generates and stores a learning model by machine learning,
An acquisition unit that acquires current environmental information at predetermined time intervals;
By applying the learning model generated by the learning model generation unit to the current environment information acquired from the acquisition unit, a prediction unit that predicts whether or not a switch is performed when switching from the unmanned operation mode to the manned operation mode,
A control unit that executes predetermined control so that the operator can safely switch from the unmanned operation mode to the manned operation mode at the time of the switching predicted by the prediction unit.

Preferably,
The predetermined control transmits the position information of the laser type manned unmanned forklift to the terminal of the operator.

Preferably,
In the predetermined control, the laser type manned / unmanned forklift automatically travels to a preset safety area.

Preferably,
The environmental information is a surrounding image of a laser type manned unmanned forklift.

Preferably,
The environment information is layout information of shelves and luggage in the facility.

Preferably,
The environment information is an image of the operator.

Preferably,
When the mode is switched from the unmanned driving mode to the manned driving mode, the environment information acquired by the acquisition unit is collected by the collection unit as teacher data.

Further, the control method of the cargo handling system according to the present invention includes:
A facility, a method of controlling a cargo handling system including a laser type manned unmanned forklift that performs traveling and cargo handling in the facility,
Laser type manned unmanned forklift can be switched between manned operation mode and unmanned operation mode, operates by manual operation of operator during manned operation mode, automatically operates by laser guidance during unmanned operation mode,
The control method is
A collecting step of collecting teacher data based on environmental information when the mode is switched from the unmanned driving mode to the manned driving mode,
A learning model generating step of performing machine learning from the teacher data collected in the collecting step, generating and storing a learning model by machine learning,
An acquisition step of acquiring current environmental information at predetermined time intervals;
A prediction step of predicting whether or not a switching time when switching from the unmanned driving mode to the manned driving mode is performed by applying the learning model generated in the learning model generation step to the current environmental information obtained in the obtaining step,
And performing a predetermined control so that the operator can safely switch from the unmanned operation mode to the manned operation mode at the time of the switching predicted by the prediction step.

  The cargo handling system and control method according to the present invention having the above-described configuration enables the operator to safely switch when the laser type manned unmanned forklift is switched from the unmanned operation mode to the manned operation mode.

The top view which shows a cargo handling system. The block diagram which shows a cargo handling system. The figure explaining environmental information. The figure which shows the terminal of an operator. The flowchart figure which shows the control method of a cargo handling system.

  Hereinafter, an embodiment of a cargo handling system and a control method according to the present invention will be described with reference to the drawings.

  As shown in FIG. 1, the cargo handling system includes a facility 3 and a laser type manned unmanned forklift (hereinafter, referred to as “forklift”) 2 that performs traveling and cargo handling work in the facility 3. In this embodiment, the facility 3 is a warehouse, but may be a factory or the like. In the facility 3, a plurality of shelves 1 are installed. The forklift 2 performs traveling and cargo handling work in the facility 3.

  The forklift 2 is switchable between a manned operation mode and an unmanned operation mode. In the manned operation mode, the forklift 2 is operated by a manual operation of an operator boarding the forklift 2. In the unmanned operation mode, the forklift 2 automatically operates by laser guidance.

  The forklift 2 includes a laser scanner 20. A plurality of reflectors 21 are installed in the facility 3. The laser scanner 20 transmits and receives the laser L to and from the reflector 21 while rotating the laser L horizontally by 360 degrees.

  The forklift 2 recognizes the plurality of reflectors 21 arranged along the traveling route in the facility 3 with the laser scanner 20. The reflection plate 21 is fixed to a wall in the facility 3, and its position information is stored on a map. The forklift 2 recognizes the plurality of reflectors 21 with the laser scanner 20, and calculates the current position based on the principle of triangulation. The forklift 2 travels on a preset route based on the calculated current position.

  The laser scanner 20 detects the laser beam L reflected from the reflector 21 and calculates the angle (azimuth) or distance between the laser scanner 20 and the reflector 21. When the reflection plate 21 is recognized by the laser scanner 20, a triangle specified by the reflection plate 21 is calculated based on the angle or the distance between the laser scanner 20 and the reflection plate 21.

  The current position of the forklift 2 is calculated by comparing the angle or distance information recognized by the forklift 2 with the position information of the reflector 21 stored in advance.

  As shown in FIG. 2, the cargo handling system includes a collection unit 40 that collects the teacher data 5 based on the environment information 60 when the operation mode is switched from the unmanned operation mode to the manned operation mode. As shown in FIG. 3, the environment information 60 includes the surrounding image (FIG. 3A) acquired by the imaging means provided in the forklift 2, the layout information of the shelves 1 and the luggage in the facility 3 (FIG. 3B), and the environment information 60 provided in the facility 3. A face image and / or a clothing image (FIG. 3C) of the operator O acquired by the imaging means. The layout information is detected by, for example, the number of packages stored in the shelf 1 detected by a weight sensor or a sensing sensor installed in the shelf 1 or an image captured by an imaging unit installed in the facility 3. This is the arrangement of the shelves 1.

  The cargo handling system includes a learning model generation unit 41 that performs machine learning from the teacher data 5 collected by the collection unit 40, and generates and stores a learning model by machine learning. The learning model generation unit 41 of the present embodiment performs supervised learning. In the supervised learning, a large amount of teacher data 5, that is, a set of input and output data, is input to the learning model generation unit 41. The input is environment information 60, and the output is a switchability score. In the present embodiment, the environment information 60 is evaluated as a switchability score indicating the possibility of switching from the unmanned operation mode to the manned operation mode, and numerical parameters from 0 to 10 are set.

For example, when the numerical parameter of the switchability score is high, that is, when it is determined that the possibility of switching from the unmanned operation mode to the manned operation mode is high, the surrounding image acquired by the imaging unit provided in the forklift 2 may be used. When the package is photographed, when there is a large number of packages stored in the shelf 1 as the layout information of the shelf 1 and the package in the facility 3 (the number is more than a predetermined number), or when the image is acquired by the imaging means provided in the facility 3 The operator O's face image is facing upward, or the operator's O clothing image is in a predetermined color (for example, red for forklift work clothes).
On the other hand, when it is determined that the numerical parameter of the switchability score is low, that is, it is determined that the possibility of switching from the unmanned operation mode to the manned operation mode is low, the surrounding image acquired by the imaging unit provided in the forklift 2 If the luggage is not shown, or if the number of shelves stored in the shelves 1 as the layout information of the shelves 1 and luggage in the facility 3 is small (a predetermined number or less), or acquired by the imaging means provided in the facility 3 The operator O's face image is facing down, or the operator O's clothing image is in another predetermined color (for example, blue which is not forklift work clothes).
The switchability score may be set with any numerical parameter of the surrounding image, layout information, face image, and / or clothing image, or may be set with a numerical parameter weighted and averaged by a weighting coefficient.

  Further, in another embodiment, the input may be the environment information 60 only when switching from the unmanned driving mode to the manned driving mode, and the output may be the switchability score 10. In another embodiment, the environment information 60 when the mode is switched from the unmanned driving mode to the manned driving mode and the environment information 60 when the mode is not switched are output. You may.

  The learning model generation unit 41 uses a machine learning algorithm such as a general neural network. When the learning model generation unit 41 inputs a model (learning model) for estimating an output from an input by performing machine learning using environment information 60 having a correlation and a switchability score as teacher data, that is, when the environment information 60 is input. , A model that outputs the switchability score is generated.

  The cargo handling system includes an acquisition unit 45 that acquires the current environment information 60 at predetermined time intervals. As shown in FIG. 3, similarly to the above, the environment information 60 includes the surrounding image (FIG. 3A) acquired by the imaging means provided on the forklift 2, the layout information of the shelf 1 and the luggage in the facility 3 (FIG. 3B), The image is a face image and / or a clothing image (FIG. 3C) of the operator O acquired by the imaging unit provided in the facility 3. The environment information 60 is acquired every predetermined time (for example, one minute).

  The cargo handling system applies the learning model generated by the learning model generation unit 41 to the current environment information 60 acquired from the acquisition unit 45, and determines whether or not the switching is performed when switching from the unmanned operation mode to the manned operation mode. Is provided. When the current environment information 60 is input to the prediction unit 42, the surrounding image (FIG. 3A), the layout information (FIG. 3B), the face image and / or the clothing image (FIG. 3C) are analyzed, and the switchability score is obtained. Is done. If the acquired switchability score satisfies the condition that it is equal to or greater than a predetermined numerical parameter, the possibility of switching is high, and it is predicted that switching is performed.

  Here, even when the environment information 60 acquired by the acquisition unit 45 is automatically collected by the collection unit 40 as the teacher data 5 when the operator O switches from the unmanned operation mode to the manned operation mode. Good. Thereby, the learning model generation unit 41 automatically updates the learning model.

When switching from the unmanned operation mode to the manned operation mode, the operator O approaches the forklift 2 traveling in the unmanned operation mode and presses a switching button (not shown) provided on the forklift 2, but the forklift 2 travels. In addition, the operator O is at risk due to the surrounding environment of the forklift 2.
Therefore, the cargo handling system includes a control unit 44 that executes predetermined control so that the operator O can safely switch from the unmanned operation mode to the manned operation mode at the time of the switching predicted by the prediction unit 42.

  As shown in FIG. 4, the predetermined control transmits the position information of the forklift 2 to the terminal 70 of the operator O. The terminal 70 displays a model diagram M of the facility 3 and a position Ma of the forklift 2 in the model diagram M. Thereby, the operator O can determine whether the surrounding environment of the forklift 2 is safe, and when it is determined to be safe, can approach the forklift 2 and press the switching button.

  In addition, the predetermined control may be such that the forklift 2 automatically travels to a preset safety area 30 (an area surrounded by a dashed line in FIG. 1). Thereby, the operator O can press the switching button while approaching the forklift 2 in a place (safety area) where the environment around the forklift 2 is safe. The safety region may be defined in advance by actually measuring and / or experimenting, or may be defined in advance by measuring and / or experimenting by simulation or the like.

As shown in FIG. 5, the above cargo handling system executes the following control method.
The collection unit 40 collects the teacher data 5 based on the environment information 60 at the time of switching from the unmanned driving mode to the manned driving mode (collection step: S1). Then, the learning model generation unit 41 performs machine learning from the teacher data 5 collected in the collection unit 40 in the collection step S1, and generates and stores a learning model by machine learning (learning model generation step: S2). The acquisition unit 45 acquires the current environment information 60 at predetermined time intervals (acquisition step: S3).

  The prediction unit 42 applies the learning model generated in the learning model generation step S2 to the current environment information 60 acquired in the acquisition step S3, thereby predicting the time of switching from the unmanned operation mode to the manned operation mode. (Prediction step: S4). At the time of the switching predicted in the prediction step S4, the control unit 44 executes predetermined control so that the operator O can safely switch from the unmanned operation mode to the manned operation mode (control step: S5).

  The preferred embodiments of the present invention have been described above, but the configuration of the present invention is not limited to these embodiments.

  In the cargo handling system according to the present invention, when the operator operating the laser type manned unmanned forklift 2 switches from the unmanned operation mode to the manned operation mode, the environment around the forklift 2 approaches the forklift 2 in a safe place (safety area). You can press the toggle button.

2 Laser type manned unmanned forklift 3 Facility 30 Safety area 40 Collection unit 41 Learning model generation unit 42 Prediction unit 44 Control unit 45 Acquisition unit 5 Teacher data 60 Environment information 70 Terminal

Claims (8)

A cargo handling system including a facility and a laser type manned unmanned forklift that performs traveling and cargo handling work in the facility,
The laser type manned unmanned forklift can be switched between a manned operation mode and an unmanned operation mode, operates by a manual operation of an operator in the manned operation mode, and is automatically operated by laser guidance in the unmanned operation mode. Work,
The cargo handling system comprises:
A collection unit that collects teacher data based on environmental information when the mode is switched from the unmanned driving mode to the manned driving mode,
A learning model generation unit that performs machine learning from the teacher data collected by the collection unit, generates and stores a learning model by the machine learning,
An acquisition unit that acquires the environmental information at a current time every predetermined time;
By applying the learning model generated by the learning model generation unit to the current environment information acquired from the acquisition unit, it is determined whether or not the switching is performed from the unmanned operation mode to the manned operation mode. A prediction unit for predicting,
A cargo handling system, comprising: a control unit that executes predetermined control so that the operator can safely switch from the unmanned operation mode to the manned operation mode at the time of the switching predicted by the prediction unit. The cargo handling system according to claim 1, wherein the predetermined control transmits position information of the laser type manned unmanned forklift to a terminal of the operator. 2. The cargo handling system according to claim 1, wherein in the predetermined control, the laser manned unmanned forklift automatically travels to a preset safety area. 3. The cargo handling system according to any one of claims 1 to 3, wherein the environmental information is an image around the laser type manned unmanned forklift. The cargo handling system according to any one of claims 1 to 4, wherein the environment information is layout information of shelves and packages in the facility. The cargo handling system according to any one of claims 1 to 5, wherein the environment information is an image of an operator. The environment information acquired by the acquisition unit is collected by the collection unit as the teacher data when the mode is switched from the unmanned operation mode to the manned operation mode. The cargo handling system described in Crab. A facility, a method of controlling a cargo handling system including a laser type manned unmanned forklift that performs traveling and cargo handling in the facility,
The laser type manned unmanned forklift can be switched between a manned operation mode and an unmanned operation mode, operates by a manual operation of an operator in the manned operation mode, and is automatically operated by laser guidance in the unmanned operation mode. Work,
The control method includes:
A collection step of collecting teacher data based on environmental information when the mode is switched from the unmanned driving mode to the manned driving mode,
A learning model generating step of performing machine learning from the teacher data collected in the collecting step, and generating and storing a learning model by the machine learning;
An obtaining step of obtaining the environmental information at a current time every predetermined time;
By applying the learning model generated in the learning model generating step to the current environment information acquired in the acquiring step, whether or not it is a switching time when switching from the unmanned operation mode to the manned operation mode is performed. A prediction step to predict,
A control step of executing predetermined control so that the operator can safely switch from the unmanned operation mode to the manned operation mode at the time of the switching predicted by the prediction step. Method.

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