KR102626500B1 - Robot cleaning system and contaminant estimation method thereof based on contaminant diffusion map - Google Patents

Abstract

A robot cleaning system is launched. The robot cleaning system of the present invention includes at least one cleaning robot for cleaning a large floor space in a workshop, an MES (Manufacturing Execution System), and production process data collected from a plurality of facilities that make up a production line in the workshop from the MES. Based on the production process data received from the MES and the map information including the location of the equipment in the workplace, the location and amount of pollutants generated according to the operation of each facility are estimated, and the estimated location and amount of pollutants are estimated. A cleaning management server that establishes a cleaning plan for the workplace by a cleaning robot based on the cleaning management server, and the cleaning management server generates a grid map expressing the floor plan of the workplace in a grid based on the map information. A generation unit, a parameter allocation unit that assigns contamination diffusion parameters to each cell constituting the grid map based on the location and type of equipment overlapping on the grid map, and operation data and assigned contamination of each equipment included in the production process data. It includes a pollutant generation amount estimation unit that estimates the amount of pollutants generated in each cell based on the diffusion parameter.

Description

Contaminant estimation method through robot cleaning system and contaminant diffusion map { ROBOT CLEANING SYSTEM AND CONTAMINANT ESTIMATION METHOD THEREOF BASED ON CONTAMINANT DIFFUSION MAP }

The present invention relates to a robot cleaning system and a method for estimating contaminants through a contaminant diffusion map. More specifically, it relates to a robot cleaning system that can efficiently clean a large area and a method for estimating contaminants through a contaminant diffusion map.

Starting in the 1990s, with the opening of agricultural products markets and the entry of large-scale domestic and foreign capital into the distribution industry, APCs (Agricultural Products Processing Centers, APCs) began to be built to enable mass trading of standardized agricultural products from production sites, providing consumers with high-quality agricultural products. We have a supply base and have increased our competitiveness by increasing the size of our producers' shipments.

APC uses cutting-edge technologies such as robots, sensors, and communications to automate APC functions such as storage/selection/packaging of agricultural products and, based on digitized information, establishes cutting-edge production distribution facilities that can be linked with forward and backward industries from farms to consumption sites. it means.

Based on digital data, APC helps with strategic decision-making such as controlling shipment timing, producing customized products for consumers, and diversifying sales channels. It can also utilize automated facilities to reduce manpower and improve the marketability of agricultural products.

In these APCs, many by-products and impurities such as dirt, peel, and roots are generated in the process of processing agricultural products such as sorting, washing, and packaging. Conventionally, large cleaning robots are used for human work or for cleaning large spaces. This APC has been deployed to perform cleaning.

Normally, when cleaning a large space such as a workshop, it is desirable to have a large cleaning robot be deployed to automatically clean the floor as quickly as possible after all work is completed. In particular, when cleaning large spaces such as APC or spaces with many obstacles, it is important to estimate waste generation information to perform cleaning efficiently. Conventional cleaning robots only move as preset or simply avoid obstacles. Therefore, there were limitations in efficiently setting up a cleaning space or allowing multiple cleaning robots to efficiently share duties.

The present invention is intended to solve the above-mentioned problem, and the purpose of the present invention is to provide a robot cleaning system that can efficiently clean a large area such as APC based on process data that can be collected, and a contaminant estimation method through a contaminant diffusion map. It is about providing.

The robot cleaning system according to an embodiment of the present invention for achieving the above-described object consists of at least one cleaning robot for cleaning a large floor space in a workshop, an MES (Manufacturing Execution System), and a production line in the workshop. Production process data collected from a plurality of facilities is received from the MES, and based on map information including the production process data received from the MES and the location of the facility within the workplace, contaminants resulting from the operation of each facility are collected. A cleaning management server that estimates the location and amount of pollutants generated and establishes a cleaning plan for the workplace by the cleaning robot based on the estimated location and amount of pollutants generated,

The cleaning management server is based on the map information, a grid map generator that generates a grid map expressing the floor plan of the workplace in a grid form, and a grid map generator that generates a grid map based on the location and type of the equipment overlapping on the grid map. a parameter allocation unit that allocates a contamination spread parameter to each cell constituting the grid map, and the contaminant in each cell based on the assigned contamination spread parameter and operation data of each facility included in the production process data. It includes a pollutant generation amount estimation unit that estimates the generation amount.

At this time, the grid map includes a first zone including at least one cell where equipment for inputting the raw material overlaps, a second zone including at least one cell where equipment for processing and moving the raw material overlaps, and After processing of the raw material is completed, equipment for discharging the raw material as a product may include a third zone including at least one cell overlapping with the other.

In addition, the pollutant generation amount estimation unit obtains, from the production process data, the total input weight of the raw material in the first zone and the total discharge weight of the product processed with the raw material in the third zone, and the total weight of the raw material in the third zone. Based on the difference between the input weight and the total discharged weight of the product, the total amount of pollutants generated during processing of the raw material is estimated, and the amount of pollutants generated in each cell can be estimated based on the estimated total amount of pollutants. .

In addition, the contaminant generation amount estimation unit calculates the total input weight of the raw material in the first zone, the weight of the material added to the raw material in the second zone, and the weight of the packaging material of the product in the third zone. The weight remaining after subtracting the total discharged weight of the product in the third zone can be estimated as the total weight of contaminants generated during processing of the raw material.

In addition, the cleaning management server groups the grid map into a plurality of square-shaped cleaning zones according to the movement path of the raw material from the first zone to the third zone, and cleans each of the grouped plurality of cleaning zones. It may further include a cleaning plan establishment unit that establishes the cleaning plan by assigning tasks to a plurality of cleaning robots.

In addition, the parameter allocation unit determines that the estimation of the amount of pollutants is not appropriate when, as a result of verification, the amount of pollutants collected by the cleaning robot differs from the estimated amount of pollutants by more than a preset amount, and generates the grid map. Contamination diffusion parameters can be reassigned to at least one constituting cell.

In addition, the cleaning management server determines the type and number of cleaning robots to be deployed at the estimated pollutant generation location based on the estimated pollutant generation location, the estimated pollutant generation amount, and the bin capacity and battery capacity of the cleaning robot to be introduced. , the cleaning plan including the movement route can be established.

Additionally, the parameter allocation unit may change the contamination diffusion parameter assigned to each cell constituting the grid map depending on the type of raw material being processed.

Additionally, the grid map may be divided into cells of different sizes depending on the location and type of the facility within the map information.

Additionally, the contamination diffusion parameter can be calculated by reflecting the contaminant fall rate (%) of the equipment included in each cell.

Additionally, the workplace may be an agricultural products processing center (APC).

Meanwhile, a method for estimating contaminants in an MES-based robot cleaning system including at least one cleaning robot, an MES (Manufacturing Execution System), and a cleaning management server for cleaning a large floor space in a workplace according to an embodiment of the present invention Receiving from the MES production process data collected from a plurality of equipment constituting the production line in the workshop, based on the production process data received from the MES and map information including the location of the equipment in the workshop A step of estimating the location and amount of pollutants generated according to the operation of each facility, and the step of the cleaning management server establishing a cleaning plan for the workplace by the cleaning robot based on the estimated location and amount of pollutants generated. Contains,

The estimating step includes, based on the map information, generating a grid map expressing the floor plan of the workplace in a grid form, and generating the grid map based on the location and type of the equipment overlapping on the grid map. Allocating a contamination diffusion parameter to each cell constituting the cell, and estimating the amount of contamination generated in each cell based on the assigned contamination diffusion parameter and operation data of each facility included in the production process data. Includes.

According to the various embodiments of the present invention as described above, efficient cleaning is possible based on the amount and location of pollutants in a large area such as an agricultural product distribution center (APC).

In order to more fully understand the drawings cited in the detailed description of the present invention, a brief description of each drawing is provided.
1 is a diagram showing the agricultural product processing management process of APC to which the production management system (MES) is applied according to an embodiment of the present invention;
Figure 2 is a diagram showing an MES-based robot cleaning system according to an embodiment of the present invention;
3 is a block diagram briefly illustrating the configuration of a cleaning management server according to an embodiment of the present invention;
Figure 4 is a diagram showing an example of a grid map of APC according to an embodiment of the present invention;
5 and 6 are diagrams for explaining a method of assigning parameters to each cell of a grid map according to an embodiment of the present invention;
7 and 8 are diagrams for explaining a method of estimating the amount of pollutants generated in each zone using parameters assigned to each cell of a grid map according to an embodiment of the present invention;
9 is a diagram illustrating a method of establishing a cleaning plan by grouping cells of a grid map according to an embodiment of the present invention;
10 is a diagram briefly explaining a control method of a robot cleaning system according to an embodiment of the present invention;
Figure 11 is a diagram to explain in more detail a method of estimating the amount of pollutants generated by a robot cleaning system and adjusting it through verification according to an embodiment of the present invention.

First, the terms used in the specification and claims are general terms selected in consideration of their functions in various embodiments of the present invention. However, these terms may vary depending on the intention of technicians working in the field, legal or technical interpretation, and the emergence of new technologies. Additionally, some terms may be terms arbitrarily selected by the applicant. These terms may be interpreted as defined in this specification, and if there is no specific term definition, they may be interpreted based on the overall content of this specification and common technical knowledge in the relevant technical field.

In addition, the same reference numerals or symbols in each drawing attached to this specification indicate parts or components that perform substantially the same function. For convenience of explanation and understanding, different embodiments will be described using the same reference numbers or symbols. That is, even if all components having the same reference number are shown in multiple drawings, the multiple drawings do not represent one embodiment.

Additionally, in this specification and claims, terms including ordinal numbers such as 'first', 'second', etc. may be used to distinguish between components. These ordinal numbers are used to distinguish identical or similar components from each other, and the meaning of the term should not be interpreted limitedly due to the use of these ordinal numbers. For example, components combined with these ordinal numbers should not be interpreted as having a limited order of use or arrangement based on the number. If necessary, each ordinal number may be used interchangeably.

In this specification, singular expressions include plural expressions, unless the context clearly dictates otherwise. In the present application, terms such as 'comprise' or 'constitute' are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are intended to indicate the presence of one or more other It should be understood that this does not exclude in advance the possibility of the presence or addition of features, numbers, steps, operations, components, parts, or combinations thereof.

Additionally, in an embodiment of the present invention, when a part is connected to another part, this includes not only direct connection but also indirect connection through other media. Additionally, the fact that a part includes a certain component does not mean that other components are excluded, but that it may further include other components, unless specifically stated to the contrary.

Hereinafter, the present invention will be described in more detail with reference to the attached drawings.

Figure 1 is a diagram showing the agricultural product processing management process of APC to which the production management system (MES) is applied according to an embodiment of the present invention.

The robot cleaning system 1000 of the present invention provides a cleaning robot for cleaning a large floor space in a workshop, where the workshop performs processes such as production, processing, and assembly to process raw materials or raw materials into finished products. It refers to a large space equipped with various facilities.

In particular, in a preferred embodiment of the present invention, the workplace may be an agricultural products processing center (APC).

APC is a distribution facility equipped with complex functions such as collection/sorting/packaging/processing/storage/shipment necessary to standardize and commercialize agricultural products from the producing area to meet the needs of consumers. The production of agricultural products is scaled and modernized to suit the characteristics of each product by main producing location. It is being developed as a base for distribution integration and operates various facilities to perform the above-mentioned complex functions.

Hereinafter, for convenience, the description will be made assuming that the workplace to which the robot cleaning system 1000 is applied is APC.

As shown in Figure 1, when the raw materials (3a, 3b) transported by vehicle from the farms (2a, 2b) that harvested various agricultural products are received into the APC (1), a certain unit is added to each raw material (3a, 3b). A production history management number may be assigned for each product.

Assuming that the raw material (3a) harvested from farm A (2a) has been received at the APC (1), a quality inspection can be performed on the received raw material (3a). Afterwards, the producer, variety, arrival date, and quantity for each pallet can be listed and loaded onto the pallet, and some can be stored in cold storage.

Thereafter, the raw material 3a may be sorted by a sorter. At this time, quality inspection can be performed again on the raw material 3a, and the raw material 3a can be selected according to producer, variety, grade, and sugar content by fruit tree. At this time, some of the selected raw materials 3a may be loaded on pallets according to variety, grade, and sugar content and then stored in cold storage.

The selected raw material 3a may be packaged by packaging unit. At this time, quality inspection and weight inspection can be performed on the raw material 3a, and it can be automatically packaged in boxes and bags. Some may be placed in cold storage.

The packaged finished product 4 can be shipped, shipping information can be recorded, and delivery can be made to customers such as wholesalers 3a and 3b by vehicle.

Meanwhile, the entire process from when the raw material 3a received in this way becomes the finished product 4 and is delivered can be monitored and recorded by the Manufacturing Execution System (MES) 200.

MES (200) is a system for managing all production activities from receiving orders at the production site to quality inspection of finished products. It collects and aggregates/analyzes production performance, worker activity, facility operation, and product quality information at the production site in real time. It can refer to an integrated production management system for monitoring and controlling the production process.

However, in the present invention, the MES (200) automatically collects, manages, and collects at least one data to manage each process regardless of the scale not only at the production site but also at the work site where various processes such as processing and inspection are performed. It can be defined as including all management systems for control.

The MES (200) integrated into the APC (1) can aggregate production process data in real time across APC's raw material receipt and finished product shipment.

Specifically, MES 200 can manage the receipt, inventory, and shipment of raw materials for each farm.

In addition, the MES (200) can manage production management information, production performance information, and process facility information by facility or zone within the APC (1).

Production management information may include order information, production plan information, work order information, finished product inventory information, and loading information.

Production performance information may include process defect information, input raw materials and process inventory, work history, and performance information compared to plan.

Process equipment information may include LOT, equipment operation/non-operation information, prevention/preservation history, equipment abnormalities, and process change information.

Additionally, the MES 200 can manage standard information, monitoring information, and management statistics information.

Standard information may include business partner information, equipment information, item information, defect information, and inspection standard information.

Monitoring information may include monitoring information on production status, production environment, work progress details, equipment status, quality inspection status, yield, and performance status. For example, production environment monitoring information may include real-time information on temperature, humidity, etc. obtained by a plurality of sensors.

Management statistical information may include statistical information on production plan/performance, production status by product, yield status, process inspection, shipment inspection, and inventory status.

On the other hand, APC may have differences in the process process and facilities installed on site depending on the item or size.

For example, if the raw material is fruit, 1) Arrival of fruit container, 2) Pallet loading, 3) Pallet dismantling, container separation, 4) Fruit input, 5) Foreign matter removal, 6) Quality (sweetness, color, etc.) selection, The process can be performed through the following processes: 7) weight sorting, 8) box packaging, 9) box taping/banding, and 10) cold storage and shipping.

In addition, if the raw material is vegetables, 1) raw material receipt, 2) vacuum pre-cooling and low-temperature storage, 3) raw material input, 4) sorting, rounding and cutting, 5) washing and sterilization, 6) rinsing and dehydration, 7) weighing and packaging, 8) metal detection, 9) weight check, 10) box packaging, and 11) cold storage and shipping.

Therefore, there may be differences in the processing process and operating facilities depending on the type of raw material received. For example, if the raw material is fruits, a washing machine is not provided or is not operated, but in the case of vegetables, a washing machine may be required to be operated in some cases.

In addition, even if the type of raw material received is the same, the process process and operating equipment may vary depending on the type of finished product and the orderer's needs.

Figure 2 is a diagram showing an MES-based robot cleaning system according to an embodiment of the present invention.

The robot cleaning system 1000 of the present invention includes a cleaning robot 100, an MES 200, and a cleaning management server 300.

The cleaning robot 100 is configured to clean a large floor space within the APC.

The cleaning robot 100 is located in a designated waiting area, is assigned a task by the cleaning management server 300, moves to a designated cleaning area, and collects contaminants such as dust and trash to clean the cleaning area.

The cleaning robot 100 includes a contaminant removal unit that collects contaminants on the floor by sweeping or suctioning, a bin that stores the collected contaminants, a power unit that supplies driving power to drive the cleaning robot 100, and a cleaning management server ( 300) and may include a communication unit and a driving wheel to perform communication.

The power supply unit may include a battery that is electrically connected to each driving device for driving various parts mounted on the cleaning robot 100 and supplies driving power. The battery is provided as a rechargeable secondary battery, and when the cleaning robot 100 completes the cleaning process and is coupled to the docking station, it can be charged by receiving power from the docking station.

The cleaning robot 100 may be configured to include a proximity sensor and/or a vision sensor. The cleaning robot 100 may travel in the cleaning area using a proximity sensor, or may generate a map of the recognized surrounding environment using a vision sensor.

Meanwhile, the cleaning robot 100 may be composed of a single cleaning robot or a plurality of cleaning robots (100-1 to 100-N). When implemented with a plurality of cleaning robots (100-1 to 100-N), It may include cleaning robots of the same or different types.

Heterogeneous cleaning robots include large cleaning robots and small (low-floor) cleaning robots classified according to the size of the cleaning robot, ride-on cleaning robots and fully automatic cleaning robots classified according to whether they can be driven manually, and classified according to the cleaning method. It may include a vacuum-type cleaning robot, a sweeping-type cleaning robot, and a wiping-type cleaning robot.

Depending on whether the cleaning robot 100 is composed of a single cleaning robot or a plurality of cleaning robots (100-1 to 100-N), the cleaning plan established may vary, and details about this will be described later.

The MES (200) is configured to record and manage production process data collected from a plurality of facilities that make up the production (processing) line of the APC (1). At this time, a separate server may be built to operate the MES 200.

The cleaning management server 300 establishes a cleaning plan within the APC 1, assigns tasks according to the cleaning plan to the cleaning robot 100, and performs the overall operation of verifying the cleaning results after the task is completed. .

The cleaning management server 300 may receive production process data from the MES 200 and obtain information related to the operation status of each facility within the APC 1.

Based on map information including the location of each facility within the APC 1 and information on the operation status of each facility obtained, the cleaning management server 300 determines the location of contaminants generated due to the operation of each facility and /Or the amount of pollutants generated can be estimated.

The cleaning management server 300 may establish a cleaning plan for the APC by the cleaning robot 100 based on the estimated location of contaminant generation and/or amount of contaminant generation.

For example, the cleaning management server 300 may obtain information about the equipment operated during the period between certain cleaning cycles and/or its operation time from information related to the operation status of each equipment within the APC 1. .

The cleaning management server 300 moves the cleaning robot 100 to an area of a certain size centered on the location of the operated facility, using information about the operated facility and information about the location of the facility included in the map information. A cleaning plan can be developed to clean the area.

In addition, when the cleaning robot 100 is composed of different types of cleaning robots, a cleaning plan is established by determining what type of cleaning robot to deploy in the area based on information about the type or location of the operated equipment. You may.

For example, if the area of a certain size centered on the location of the operated facility is a narrow place where it is difficult for a large cleaning robot to enter, or is at the bottom of the facility so that only a low-floor robot can enter or clean, the low-floor robot is used in that area. It can be.

In addition, the cleaning management server 300 further uses information about the operation time of the operated equipment to estimate the amount of contaminants generated in an area of a certain size centered on the location of the operated equipment and injects it into the area. You can also establish a cleaning plan by determining the number of cleaning robots to be installed.

At this time, data measuring the actual amount of contaminants generated according to a specific operation time can be used to estimate the amount of contaminants.

For example, if the equipment was operated for 6 hours, and the measured data that a total of 0.1 kg of contaminants was actually generated when the equipment was operated for 3 hours is stored in the database, the same amount is calculated according to the operating time of the equipment. Since it can be assumed that contaminants were generated proportionally, it can be estimated that a total of 0.2 kg of contaminants were generated from the facility over 6 hours.

In addition, it is also possible to continuously learn the actual amount of pollutants generated compared to the operation time of the facility through machine learning and predict the amount of pollutants generated by using the operation time of the facility as input data. At this time, the type of raw material being processed can be additionally learned.

Thereafter, the cleaning management server 300 may control the cleaning robot 100 and assign a task to clean the contaminated area within the APC 1 according to the established cleaning plan.

After the mission is completed, the cleaning management server 300 may compare the amount of contaminants collected by the cleaning robot 100 with the estimated amount of contaminants generated to verify the adequacy of the estimate of the amount of contaminants generated.

Specifically, the cleaning robot 100 may measure the amount of contaminants collected in the collection bin through a weight sensor and transmit information about the amount of collected contaminants to the cleaning management server 300.

If the information on the amount of collected contaminants received from the cleaning robot 100 and the pre-estimated contaminant generation amount differ by more than a preset amount, the cleaning management server 300 may determine that the estimation of the contaminant generation amount is not appropriate. there is.

Here, the amount of collected contaminants and the estimated amount of contaminants generated may mean the weight of the collected contaminants (kg) and the estimated weight of contaminants generated (kg), respectively.

At this time, the cleaning management server 300 may correct the estimated amount of pollutants generated based on the value corresponding to the difference in the above-mentioned amount when estimating the future amount of pollutants generated for the cleaning area for which it is determined that the estimation of the amount of pollutants is not appropriate. there is.

Meanwhile, the cleaning robot 100, MES 200, and cleaning management server 300 may be connected to each other through the network 2000. At this time, the network 2000 may be wired or wireless. The communication method of the network 2000 is not limited, and any communication method utilizing a communication network that the network 2000 may include (for example, a mobile communication network, wireless online, or broadcasting network) may be used.

For example, the network 2000 may be comprised of one or more of a wide area network (WAN), a metropolitan area network (MAN), and a local area network (LAN), or a combination thereof. In addition, the network 2000 is a wireless Internet technology such as Wireless LAN (WLAN) (WiFi), Wireless Broadband (Wibro), and/or World Interoperability for Microwave Access (Wimax), Code Division Multiple Access (CDMA), and Global System (GSM). for Mobile communication), LTE (Long Tern Evolution), and/or LTE-Advanced, and/or V2X (Vehicle to Everything) communication technologies such as V2I (Vehicle to Infra).

The cleaning management server 300 according to this embodiment has the same hardware configuration as a typical web server, and is implemented in software through various types of languages such as C, C++, Java, Visual Basic, and Visual C. It can contain program modules that perform various functions. In addition, general server hardware is provided in a variety of operating systems such as DOS, Windows, Linux, Unix, Macintosh, Android, and iOS. It can be implemented using an existing web server program.

The cleaning management server 300 may be implemented including a database or may be implemented independently from the database. When implemented independently from the database, the cleaning management server 200 can connect to the database wired or wirelessly to exchange files.

Figure 3 is a block diagram briefly illustrating the configuration of a cleaning management server according to an embodiment of the present invention.

The cleaning management server 300 of the present invention includes a grid map generator 310, a parameter allocation unit 320, a pollutant generation amount estimation unit 330, a cleaning plan establishment unit 340, a cleaning robot control unit 350, and a verification unit. It may include (360).

The grid map generator 310 is a component that generates a grid map expressing the floor plan of the APC 1 in a grid form based on map information.

The grid map generator 310 may receive map information including the location of each facility within the APC 1 from the MES 200, the cleaning robot 100, or an external terminal, or may generate it on its own based on user input. .

FIG. 4 shows an example of a grid map in which a floor plan including the location of each facility of the APC 1 is divided into a plurality of cells 40-1, 40-2??.

As shown in FIG. 4, the grid map generator 310 may generate a grid map expressing the floor plan of the APC in a grid form based on map information.

A grid map can be a map that expresses the internal space of the APC as a grid of the same size and indicates the presence or absence of facilities that make up the production line in each grid.

Here, the size of each cell constituting the grid map may be set differently depending on the location and type of equipment in the map information.

Specifically, the size of each cell (40-1, 40-2??) constituting the grid map may vary depending on the area of the APC (1) and the number, size, location, and type of facilities.

Additionally, each cell (40-1, 40-2??) may be configured to have the same size, but may be configured to have different sizes depending on the embodiment.

For example, the size of the cells (40-1, 40-2??) is based on the location and size of each facility recognized in the map information, so that each facility enters a cell less than a preset number or is connected to the cell. The size or shape may be adjusted collectively or individually so as not to overlap the boundaries between cells.

In addition, as an embodiment, the grid map generator 310 is configured to create a first zone (input space, 51) including at least one cell in which equipment for inputting raw materials overlaps the grid map, as shown in FIG. 5. , a second zone (moving space, 52) including at least one cell where equipment for moving the raw material while processing it overlaps, and at least equipment for discharging the raw material as a finished product (commodity) after the processing is completed. A third zone (discharge space, 53) containing one cell can be set.

Accordingly, the cleaning plan establishment unit 340, which will be described later, establishes a cleaning plan by dividing the first zone 51, the second zone 52, and the third zone 53 set as described above, and the cleaning robot 100 can be controlled to perform cleaning in a manner or order suitable for each area (51 to 53).

The parameter allocation unit 320 is configured to allocate contamination diffusion parameters to each cell (40-1, 40-2??) constituting the grid map based on the characteristics of the location and type of facilities overlapping on the grid map. am.

Here, the contamination diffusion parameter may be a value calculated by reflecting the contaminant falling rate (%) or the contaminant falling rate (%) according to the characteristics of the facilities overlapping or close to each cell (40-1, 40-2??), The pollutant drop rate (%) may be a value initially estimated and assigned according to empirical rules.

For example, the contamination diffusion parameter may be a value representing the ratio (%) of the average amount of contaminants falling or generated from the raw material compared to the amount of raw material processed in the facility when operating a specific facility.

Additionally, the contamination diffusion parameter can generally be defined as a value between 0 and 1. The contamination diffusion parameter may be assigned 0 to cells that do not have overlapping facilities or do not have facilities within a certain radius.

In addition, since the characteristics of contaminant generation in each facility may differ depending on the type of raw material input for processing, the parameter allocation unit 320 spreads the contamination assigned to each cell constituting the grid map according to the type of raw material being processed. You can also change parameters.

Figure 6 shows that a preset contamination diffusion parameter is assigned to each cell on a grid map, according to an embodiment of the present invention.

As shown in FIG. 6, the cells included in the first zone 51 and the third zone 53 are basically assigned the same parameters regardless of the cell location, and the cells included in the second zone 52 Different parameters may be assigned depending on the characteristics of the equipment overlapping in the cell and/or the type of raw material being input.

The pollutant generation amount estimation unit 330 is configured to estimate the pollutant generation amount in each cell (40-1, 40-2??) based on the operation data of each facility included in the production process data and the assigned pollution diffusion parameters. am.

The pollutant generation amount estimation unit 330 calculates the operation time of the equipment overlapping in each cell (40-1, 40-2??), the amount of input/processed raw material, and/or the production process data received from the MES 200. Alternatively, the production performance data can be extracted, the raw material processing volume at the facility can be estimated, and the amount of pollutants generated in each cell (40-1, 40-2??) can be estimated based on the estimated raw material processing volume and contamination diffusion parameters. there is.

In particular, as shown in FIG. 7, the pollutant generation amount estimation unit 330 determines the production process data received from the MES 200 until the raw material inputted into the first zone 51 is moved to the third zone 53. Cells overlapping with equipment operated on the production line and their movement directions can be extracted.

In this case, the pollutant generation amount estimation unit 330 may estimate that pollutants are generated only in cells where equipment operated on the production line overlaps.

At this time, the amount of pollutants generated can be estimated as the product of the estimated raw material treatment amount and the pollution diffusion parameter as shown in Equation 1 below.

P _con (Cell _n ): Estimated amount of pollutants generated in the nth cell

raw(Cell _n ): Estimated raw material throughput in the nth cell

parameter(Cell _n ): Contamination diffusion parameter of the nth cell

Here, the contamination diffusion parameter may be defined as a contaminant falling rate (%) or a value calculated by reflecting the contaminant falling rate (%).

Meanwhile, the pollutant generation amount estimation unit 330 obtains the total input weight of the raw material in the first zone 51 and the total discharge weight of the product processed from the raw material in the third zone 53 from the production process data, Based on the difference between the total input weight of the raw material and the total discharged weight of the product, the total amount of pollutants generated in the APC (1) due to processing of the raw materials is estimated, and based on the estimated total amount of pollutants generated in each cell (40-1, 40-2??), the amount of pollutants generated can be estimated.

At this time, the amount of pollutants generated can be estimated as the product of the total remaining amount of pollutants in the nth cell among the estimated total amount of pollutants multiplied by the pollution diffusion parameter, as shown in Equation 2 below.

P _con (Cell _n ): Estimated amount of pollutants generated in the nth cell

res(Cell _n ): Total remaining amount of pollutants in the nth cell among the total amount of pollutants generated

parameter(Cell _n ): Contamination diffusion parameter of the nth cell

Here, the contamination diffusion parameter can be defined as a value calculated by reflecting the movement order on the production line and facility characteristics.

Referring to FIG. 8, assuming that the weight of raw materials input into the first zone 51 within the daily production cycle is 101 kg and the total discharge weight of products processed and discharged from the third zone 53 is 100 kg, the daily production cycle During this process, it can be estimated that the total amount of pollutants generated from the processing of raw milk is 1kg.

At this time, the total remaining amount of pollutants estimated in the first cell is 1kg, which is the same as the total amount of pollutants generated, and the estimated amount of pollutants generated in the first cell, P _con (Cell ₁ ), may be (101-100)*0.3=0.3kg.

In addition, the total remaining amount of pollutants estimated in the 2nd cell is 0.7kg, which is the total amount of pollutants generated in the 1st cell minus 0.3kg, which is P _con (Cell ₁ ), from the total amount of pollutants generated in the 1st cell, P con (Cell 1). _con (Cell ₂ ) can be (101-100-0.3)*0.3=0.14kg.

In addition, the total amount of residual pollutants estimated in the 3rd cell is 0.56kg, which is obtained by subtracting 0.14kg, which is the estimated amount of pollutants generated in the 2nd cell, P _con (Cell ₂ ), from the total residual amount of pollutants in the 1st cell, 0.7kg. The estimated amount of pollutants generated in the cell, P _con (Cell ₃ ), may be (101-100-0.3-0.14)*0.2=0.112 kg.

In this way, the amount of pollutants distributed within the 1st to 23rd cells can be predicted from the total amount of pollutants estimated within one cycle and the parameters assigned to each cell.

In addition, according to an embodiment of the present invention, the pollutant generation amount estimation unit 330 calculates the total input weight of raw materials in the first zone 51 and the second zone 52 in order to more accurately estimate the total pollutant generation amount. The weight of the material added to the raw material plus the weight of the packaging material of the product in the third zone (53) is subtracted from the total weight of the product discharged from the third zone (53), and the remaining weight is the total weight generated by the processing of the raw material. It can also be estimated by the weight of contaminants.

Meanwhile, the parameter allocation unit 320 is configured to provide at least one of the types of raw materials input into the first zone 51, the type of equipment included in the second zone 52, and the type of finished product discharged from the third zone 53. Based on this, the parameters assigned to the grid map can be adjusted.

Specifically, the parameter allocation unit 320 can uniformly adjust parameters assigned to the first zone 51, the second zone 52, and the third zone 53 within a preset range for each zone.

For example, when the parameters assigned to one of the cells included in the first zone 51 are changed, the parameters of other cells included in the first zone 51 may also be changed in the same way.

Additionally, depending on the type of raw material input into the first zone 51, if there are unused facilities among the facilities included in the second zone 52, the parameters can be readjusted only in the second zone 52. For example, the parameter may be adjusted to 0 in a cell in the second area 52 where unused equipment overlaps.

Meanwhile, the cleaning plan establishment unit 340 may establish a cleaning plan for the workplace of the cleaning robot 100 based on the estimated location of contaminant generation and amount of contaminant generation.

Specifically, the cleaning plan establishment unit 340 groups the grid map into a plurality of square-shaped cleaning areas according to the movement path of the raw material from the first area 51 to the third area 53, and groups the plurality of grouped cleaning areas. A task can be assigned to a plurality of cleaning robots 100 to clean each cleaning area.

For example, referring to FIG. 9, the cleaning plan establishment unit 340 divides the grid map into a plurality of different sizes so that cleaning performed in each cell by each cleaning robot 100 can be completed at the same time. Can be grouped into areas (91, 92).

At this time, the cleaning plan establishment unit 340 determines which cleaning robot 100 is the best based on the pollutant generation location, the total amount of pollutants estimated in each cell, and the bin capacity and battery capacity of the cleaning robot 100 to be input. A plurality of areas 91 and 92 that can be cleaned efficiently can be grouped.

For example, an area to be grouped can be set and assigned to the cleaning robot 100 so that the cleaning robot 100 to be introduced can minimize the number of times it empties trash or performs battery charging, and a plurality of cleaning robots 100 ) is input, based on the estimated amount of pollutants generated in each cell, an area to be grouped is set and assigned to the cleaning robot 100 so that cleaning by a plurality of cleaning robots 100 can be completed at the same time. You can.

Furthermore, the cleaning plan establishment unit 340 may establish a cleaning plan including the type, number, and movement path of the cleaning robots 100 to be introduced into each cleaning zone, based on the plurality of grouped cleaning zones.

The cleaning robot control unit 350 generates a control signal to control the operation of each cleaning robot 100 and controls each cleaning robot 100 to clean each cleaning area according to an established cleaning plan.

The cleaning robot control unit 350 may transmit a control signal to each cleaning robot 100 through the network 2000, receive status information including a response signal from each cleaning robot 100, and control each cleaning robot 100. The cleaning operation can be monitored.

The verification unit 360 is configured to compare the amount of contaminants collected by the cleaning robot 100 with the estimated amount of contaminants generated to verify whether the parameters assigned to each cell are appropriate.

If the comparison results show a difference of more than a preset amount, the verification unit 360 may determine that the estimation of the amount of pollutants generated is not appropriate, and may reallocate the pollution diffusion parameter to at least one cell constituting the grid map.

To this end, after the cleaning of the cleaning robot 100 is completed, the verification unit 360 receives the weight data of the collected contaminants detected from the weight detection sensor provided in the cleaning robot 100 and calculates the amount of collected contaminants. can do.

Alternatively, the verification unit 360 may continuously receive and monitor weight data of contaminants collected by the cleaning robot 100 while the cleaning robot 100 is being cleaned. In this case, changes in the weight data of contaminants can be tracked according to the moving direction of the cleaning robot 100, and the amount of contaminants collected in each cell can be calculated.

For example, if the amount of contaminants actually collected is greater than the amount of contaminants estimated in one cell, the verification unit 360 may adjust the value of the contamination diffusion parameter assigned to that cell to be higher.

The database 370 is a configuration that stores various data and modules for driving the cleaning management server 300.

Specifically, the database 370 may store a base module that processes signals transmitted from each hardware included in the cleaning management server 300, a storage module that manages the registry, a security module, a communication module, etc.

In addition, the database 370 stores production process data received from the MES 200, map information including the location of equipment in the workplace, and estimated information on the location and amount of pollutants generated according to the operation of each facility. .

The database 370 is a flash memory type, hard disk type, solid state disk type, SDD type (Silicon Disk Drive type), and multimedia card micro type. ), card-type memory (e.g. SD or XD memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), EEPROM (electrically erasable programmable read) -only memory), PROM (programmable read-only memory), magnetic memory, magnetic disk, and optical disk may include at least one type of storage medium.

Additionally, the database 370 may be operated in relation to web storage that performs a storage function of the database 370 on the Internet.

Figure 10 is a diagram briefly explaining a control method of a robot cleaning system according to an embodiment of the present invention.

First, the cleaning management server estimates the location and amount of pollutants generated during the operation of each facility based on the production process data and workplace map information received from the MES 200 (S1010).

Thereafter, the cleaning management server establishes a cleaning plan for the cleaning robot based on the estimated location and amount of contaminants (S1020).

Afterwards, the cleaning management server controls the cleaning robot and assigns it a task to clean the workplace according to the cleaning plan (S1030).

Then, after the mission is completed, the adequacy of the estimate of the amount of pollutants generated is verified by comparing the amount of pollutants collected by the cleaning robot and the estimated amount of pollutants generated (S1040).

Figure 11 is a diagram to explain in more detail a method of estimating the amount of pollutants generated by a robot cleaning system and adjusting it through verification according to an embodiment of the present invention.

First, based on the map information, a grid map of the floor plan of the workplace is created (S1110).

Thereafter, contamination diffusion parameters are assigned to each cell based on the location and type of overlapping facilities on the grid map (S1120).

Thereafter, the amount of pollutants generated in each cell is estimated based on the operation data and pollution diffusion parameters of each facility (S1130).

Thereafter, the adequacy of the estimate of the amount of pollutants generated is verified by comparing the amount of pollutants collected by the cleaning robot after the mission is completed and the estimated amount of pollutants generated (S1140).

Afterwards, if the estimation of the amount of pollutants generated is not appropriate, the pollution diffusion parameters are reallocated (S1150).

According to the various embodiments of the present invention as described above, efficient cleaning is possible based on the amount and location of pollutants in a large area such as an agricultural product distribution center (APC).

Meanwhile, the contaminant estimation method through the contaminant diffusion map of the robot cleaning system according to the various embodiments described above may be implemented as a program and stored in various recording media. That is, a computer program that is processed by various processors and can execute the contaminant estimation method through the contaminant diffusion map of the robot cleaning system described above may be used while stored in a recording medium.

As an example, i) receiving production process data collected from a plurality of facilities that make up the production line in the workshop from the MES. ii) a step of estimating the location and amount of pollutants generated according to the operation of each facility based on production process data received from the MES and map information including the location of facilities in the workplace, and iii) the cleaning management server estimates the pollutants The step of establishing a cleaning plan for the workplace by a cleaning robot is performed based on the location of occurrence and the amount of contaminants generated, and the estimating step of ii) is a) a grid map expressing the floor plan of the workplace in a grid form based on map information ( Steps to create a grid map. b) assigning contamination spread parameters to each cell constituting the grid map based on the location and type of overlapping equipment on the grid map, and c) operating data and assigned contamination spread for each equipment included in the production process data. A non-transitory computer readable medium storing a program that sequentially performs steps for estimating the amount of pollutants generated in each cell based on parameters may be provided.

A non-transitory readable medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as registers, caches, and memories. Specifically, the various applications or programs described above may be stored and provided on non-transitory readable media such as CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM, etc.

In the above, preferred embodiments of the present invention have been shown and described, but the present invention is not limited to the specific embodiments described above, and may be used in the technical field to which the invention pertains without departing from the gist of the invention as claimed in the claims. Of course, various modifications can be made by those skilled in the art, and these modifications should not be understood individually from the technical idea or perspective of the present invention.

100: cleaning robot 200: MES
300: Cleaning management server 1000: Robot cleaning system
2000: Network

Claims (13)

In the robot cleaning system,
at least one cleaning robot for cleaning large floor areas of the workplace;
Manufacturing Execution System (MES); and
Production process data collected from a plurality of equipment constituting the production line in the workshop is received from the MES, and based on map information including the production process data received from the MES and the location of the equipment in the workshop, A cleaning management server that estimates the location and amount of pollutants generated according to the operation of each facility, and establishes a cleaning plan for the workplace by the cleaning robot based on the estimated location and amount of pollutants generated,
The cleaning management server,
A grid map generator that generates a grid map representing the floor plan of the workplace in a grid form, based on the map information;
a parameter allocation unit that allocates a contamination spread parameter to each cell constituting the grid map based on the location and type of the equipment overlapping on the grid map; and
A robot cleaning system comprising a pollutant generation amount estimator that estimates the amount of pollutants generated in each cell based on the operation data of each facility included in the production process data and the assigned pollution diffusion parameter. According to paragraph 1,
The grid map is,
A first zone including at least one cell where equipment for inputting the raw material overlaps, a second zone including at least one cell where equipment for moving the raw material while processing it overlaps, and processing of the raw material. A robotic cleaning system comprising a third zone comprising at least one cell overlapping with facilities for discharging finished products as goods. According to paragraph 2,
The pollutant generation amount estimation unit,
From the production process data, the total input weight of the raw materials in the first zone and the total discharge weight of the products processed by the raw materials in the third zone are obtained, and the total input weight of the raw materials and the total weight of the products are obtained. A robot cleaning system characterized by estimating the total amount of pollutants generated during processing of the raw material based on the difference in discharge weight, and estimating the amount of pollutants generated in each cell based on the estimated total amount of pollutants. According to paragraph 3,
The pollutant generation amount estimation unit,
The product in the third zone in the weight of the total input weight of the raw material in the first zone, the weight of materials added to the raw material in the second zone, and the weight of the packaging material of the product in the third zone. A robot cleaning system characterized in that the remaining weight minus the total discharged weight is estimated as the total weight of contaminants generated during processing of the raw material. According to paragraph 2,
The cleaning management server,
The grid map is grouped into a plurality of square-shaped cleaning zones according to the movement path of the raw material from the first zone to the third zone, and a plurality of cleaning robots are tasked to clean each of the grouped plurality of cleaning zones. A robot cleaning system further comprising a cleaning plan establishment unit that establishes the cleaning plan by allocating . According to paragraph 1,
The parameter allocation unit,
As a result of verification, if the amount of pollutants collected by the cleaning robot differs from the estimated amount of pollutants by more than a preset amount, it is determined that the estimate of the amount of pollutants is not appropriate, and at least one cell constituting the grid map A robotic cleaning system characterized by reassigning contamination diffusion parameters to . According to paragraph 1,
The cleaning management server,
Based on the estimated pollutant generation location, the estimated pollutant generation amount, the bin capacity and battery capacity of the cleaning robot to be introduced, including the type and number of the cleaning robots to be introduced to the estimated pollutant generation location, and the movement path. A robot cleaning system characterized by establishing the cleaning plan. According to paragraph 1,
The parameter allocation unit,
A robot cleaning system characterized in that the contamination diffusion parameter assigned to each cell constituting the grid map is changed depending on the type of raw material being processed. According to paragraph 1,
The grid map is,
A robot cleaning system characterized in that it is divided into cells of different sizes depending on the location and type of the equipment in the map information. According to paragraph 1,
The contamination diffusion parameter is,
A robot cleaning system characterized in that it is calculated by reflecting the contaminant falling rate (%) of the equipment included in each cell. According to paragraph 1,
The workshop is,
A robot cleaning system characterized by being an agricultural products processing center (APC). In the method of estimating contaminants in an MES-based robot cleaning system including at least one cleaning robot, a Manufacturing Execution System (MES), and a cleaning management server for cleaning a large floor space in a workplace,
Receiving production process data collected from a plurality of facilities constituting the production line in the workshop from the MES;
estimating the location and amount of pollutants generated according to the operation of each facility based on production process data received from the MES and map information including the location of the facility within the workplace; and
Comprising: the cleaning management server establishing a cleaning plan for the workplace by the cleaning robot based on the estimated pollutant generation location and pollutant generation amount,
The estimation step is,
Based on the map information, generating a grid map expressing the floor plan of the workplace in a grid form;
assigning a contamination spread parameter to each cell constituting the grid map based on the location and type of the facility overlapping on the grid map; and
A pollutant estimation method comprising: estimating the amount of pollutants generated in each cell based on the operation data of each facility included in the production process data and the assigned pollutant diffusion parameter. A computer program stored in a recording medium that executes the contaminant estimation method of claim 12.

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