SUGISAKAMASANORI

Journal of Advances in Artificial Life Robotics
Vol. 1(3); December (2020), pp. 116–121
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

Research Article

AGVs Task Management with Occupancy Grid Map for FoamManufacturing In-Plant Transport
Amornphun Phunopas1, Wisanu Jitviriya1, Noppadol Pudchuen1, Songklod Tunsiri2, Eiji Hayashi3

Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Road, Bangsue, Bangkok 10800, Thailand
Urban Community Development College, Navamindradhiraj University, Wachira Phayaban, Khao Road, Dusit, Bangkok 10300, Thailand
3
Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-0067, Japan
12ARTICLE INFO
Article History
Received 11 November 2019
Accepted 10 August 2020
Keywords
Automated tasking
AGVs traffic and queue management

ABSTRACTAGVs are increasingly used in the automated warehouse with a high demand for changing
traditional workflow management to industrial 4.0. The heart of the computerized system is thecentral software that can distribute work functions from the queues and manage the AGVs’ traffic.
On the 2D floor plant layout and the occupancy grid map, the RFID girds are initially from marked
points or the place that AGVs have to transit to do an assigned task. This research proposesautonomously generating paths via four nearest grids and path switching scenarios. The results
show the generated paths with sequential tasks concurrently in random conditions. The task
management method can prevent the AGVs’ crash and bottleneck from the operation of ninemachines in the foam manufacturing plant.

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

factors of break-even point and payback period. The
central software is the key to success in optimized
productivity. This paper proposes a scenario that
progresses previous work in using AGVs in foam amanufacturing plant [2]. The central software manages
tasks in the queue and assigns them to two AGVs, thentests this in the real plant. Moreover, the knowledge
about indoor autonomous robots that was gained overmany years in the World RoboCup Rescue League
helped create an occupancy grid map of the plant layout[3]. The map is beneficial for scenario simulations andsoftware development.

1. Introduction
Nowadays, automated warehouses are widely used in
the industry. One example is the IPIN competition. It
challenges the problem of position-tracking in the indoor
environment, which is the initial scenario that industry
needs. The industrial mobile robots or AGVs have to
track the magnetic path in a closed route under thedefined trajectory [1]. The system consists of many
modules of software and hardware, for example, thecentral computing unit, the fixed automation unit, and themovable agent. These AGVs are one part of the systemthat complies with the assigned tasks sequentially. They
can move to the target and do an assigned task; when it
finishes a task, it then moves to the next position
according to the production process. Moreover, this
implemented technology is suitable for a different
industry that has to compromise with the investor on

2. System Overview

Corresponding author’s E-mail: amornphun.p@eng.kmutnb.ac.th, wisanu.j@eng.kmutnb.ac.th URL: https://iraps.eng.kmutnb.ac.th116

The plant layout is created from part of the foam
manufacturing process. The foam product must betransferred from the front machine to the hot temperatureroom to drain the humidity—the AGVs work in zone 1,zone 2, and zone 3. Completing the primary task takes afull cart from zone 2 to zone 3; then, the secondary task
takes an empty cart from zone 1 to replace the taken fullcart in the front machine. The AGVs can travel via allgrids, which are connected as a path. It takes a series ofgrids from the current position to any destination in theplant layout in Fig. 1. The path is generated from thecentral software by traffic management to make the AGV
pause or generate a new path that can avoid the collision
to another AGV.

the queue table meant to complete the cart-out and cartin task.
3. Automated Task
The central software will provide the job that theAGVs have to do. The concept is that an AGV goes,arrives at a target, and performs a job until the last
subtask. The AGV always communicates with the central
software and is then assigned to the next target and do thenext job. After finishing the task, the AGV is free andprompt to receive a command from the central software
in Fig. 3.

Fig. 3 To assign the available AGV to the queue and update the tablequeue.

The subtasks that the AGV can do are a cart’s heading
adjustment, an AGV’s heading adjustment, cart hooking,and cart releasing in Fig. 4. The subtask concept is also
flexible for various applications in other industries.

Fig. 1 Plant layout with four nearest grids and guided lines.

Fig. 4 (Left) Task of taking the full cart to the waiting zone (Zone 3).
(Right) Task of taking the empty cart to the machine zone (Zone 2).

Fig. 2 The worker loads the foam product to the empty cart. When thecart is full, the worker will press the button. The central software pools
the information and manages the tasks.

4. Path Management

The system consists of the central software, pushbuttons,and AGVs. The operation starts from the worker pressing
the button in front of nine machines, randomly depending
on putting the foam to the cart finish in Fig. 2. Then, thecentral software will add the number of the front machine
into the queue table. If there is an AGV available, it will
be assigned to the queue and instantly start doing theassigned task. Thus, there are two sequential queues inThe grids are marked on the layout for significant
tasks that the AGV can move past or stop to do a task.
The grid positions are not symmetrical or balanced inrows and columns in this plant layout. The central
software manages the whole system operation and trafficfor the AGVs.

117

4.1. Grids Connection to Guided LineThe guided lines are designed to cover the work areain the plant layout that the grids can be connected to,creating a path for AGVs. The AGV has tasks assigned
differently, but it has the same design and capability. The
guided line uses magnetic track that has to be constructedunder the plant’s floor because of durability and longterm operation. The AGV uses the magnetic sensor forline detecting and tracking. The grids of the main lines
are mapped to the plant layout, as seen in Table 1. The
grid can be detected by the cross junction of magnetic
track and RFID.

Fig. 5 Main lines in zone 1, zone 2, and zone 3, including switchinglines in each zone.

4.3. Traffic Manager
In Fig. 6, the central software has perceived theintersection points of the AVGs’ paths. It monitors thecritical distances by extending each AGV’s path.
However, it takes time to do the job at a target. The
central software also has to calculate the time that
corresponds to doing a job and traveling. Besides thecollision problem, bottleneck conditions can occur when
AGVs have to wait for too long for the current path toclear. This might result in a traffic problem that isinefficient for productivity, even if the AGVs can get tothe destination.

Table 1. Main line for AGVs
Line

Grid sets of main lines

L0{0,1,2,3,4,5,6,7,8}

L1{9,10,11,12,13,14,15,16,17,18}

L2{9,10,11,19,20,21,22,23,24,25,26,16,17,18}

L3{6,15,25,48,51,54,57,60,63,66,70,73,75,77,79}

L4{6,15,16,26,49,52,55,58,61,64,67,71,73,75,77,79}

L5{7,17,16,26,49,50,53,56,59,62,65,68,69,72,74,76,78,80}

L6{4,13,22,81,84,87,90,93,96,99,102,105,108,111,114}

L7{4,13,22,81,82,85,88,91,94,97,100,103,106,109,112,115}

L8{4,13,22,81,82,83,86,89,92,95,98,101,104,107,110,113,116}

4.2. Path Switching
The AGV travels along the defined main lines. If theline is not available or is occupied by others, the AGV
has to pause or switch being in the current mainline toprevent collision damage, as shown in Table 2. When thesystem has many AGVs, the central software has all of
the information to analyze and assign a proper path foreach AGV.
Table 2. Switching lines across the main linesZone
123

Fig. 6 Collision monitoring using all paths of AGVs in the central
software

Grid sets of switching lines

5. Results{{48,49,50},{51,52,53},{54,55,56},{57,58,59},{60,61,62},{63,64,65},{66,67,68,69},{70,71,72},{73,74},{75,76},{77,78},{79,80}}{{0,9},{1,10},{2,11,19,20},{3,12,21,22},{4,13,23},{5,14,24},{6,15,25},{16,26},{7,17},{8,18}}{{81,82,83},{84,85,86},{87,88,89},{90,91,92},{93,94,95},{96,97,98},{99,100,101},{102,103,104},{105,106,107},{108,109,110},{111,112,113},{114,115,116}}

The experiment took one hour and 32.54 minutes(from 3:24:34 p.m. to 4:57:28 p.m.) to complete 24
instances of transportation by cart. There are work areas
7 m2, 57 m2, and 72 m2 for zone 1, zone 2, and zone 3,respectively. The priority task started from zone 2 to zone
3 in order from grid 116 to 82. The pattern to transfer theempty carts from zone 1 to zone 2 had six grids, from grid
48 to 55, which ran in a loop repeatedly, as in Fig. 7. Thebuttons were independently pressed by the workers andthen appended to the queue, as in Fig. 8.

The path-switching method helps to manage the traffic of
the multiple AGVs’ operation under the limited guidedlines. It is similar to railroad switching, but Fig. 5
indicates that it has many possibilities for switching themain lines via the cross lines.

118

Fig. 7 The travel pattern via grids in the plant layout for carts’transportation

Fig. 9 All complete tasks to place 24 full carts in zone 3 and 24 empty
carts in zone 2.

time (mins)

It took every 3.40 minutes to press the button once of
totally one hour and 28.15 minutes (or 88.15 minutes).

(24 queues)

Fig. 10 The AGV waited to start working and take the full cart out until
after the worker pressed a button on the front of the machine 24 times.

Fig, 8 The nine buttons in front of the machine are pressedindependently 24 times

6. Occupancy Grid Map

There were two AGVs separately operating in theexperiment. One AGV had the full-cart-out task, andanother AGV had the empty-cart-in task. It took 3.52
minutes, on average, for one cart to finish taking out thefull cart and taking in the empty cart to the front machine.
The AGVs had the same design, traveled by the samespeed (0.5 meters per second), and had the same setting
and behaviors. They worked overlap in zone 2, which
made it possible for the AGVs to crash and bottleneck.
As long as there is a task in the queue, the AGVs do notstop working but continue running to the next queue. The
completion time of the full-cart-out task and the emptycart-in task were approximately in the linear. Meanwhile,the time to pause and do a job were the interferences, asin Fig. 9. The workers take time to load the foam productto the cart, about 30–60 minutes, depending on the sizeof the foam product. The workers then call the AGV totake the full cart out and replace it with the new empty
cart. Fig. 10 shows the time that the worker at the front
machine has to wait for the AGV to start taking the fullcart out. The problem that can occur is when the workers
call the AGV at the same time. Then the AGV has tocomplete the tasks in the queue one by one. The tasks thatare not assigned to the AGVs have to wait up to 20
minutes, which is a very long time.

The occupancy grid map of the plant layout has merged
the information of RFID grids in the red dots in Fig. 11.
The map floor plan has two purposes: The first is for theautomated task and path management simulation. The
second is for path-planning navigation strategies withouta magnetic track–guided line. The AGVs can update theircurrent positions on the occupancy grid map using
SLAM and fuse the information from magnetic trackingand the RFID grid to the correct position. Therefore, theAGVs can move freely outside the magnetic track andreturn to the track again. When an AGV moves on themagnetic track, it can park quickly and precisely and save
space to accommodate as many carts as possible. There
are many optimizing solutions that can be tested in
scenario software simulation using an occupancy gridmap. For safety purposes, the AGVs can recognize thehazmat labels in the factory [4]. Other labels can be usedas visual commands for the AGVs.

119

Marking Chart for Rescue Robot. Journal of Robotics,
Networking and Artificial Life, 5(4), 2019. pp.223-227.
==========================================Authors Introduction

Dr. Amornphun Phunopas
He is an Assistant Professor ofFaculty of Engineering at King
Mongkut’s
UniversityofTechnology North Bangkok inThailand. He graduated from
Institute of Field Robotics (FIBO)
in 2009. He received his D. Eng.
degree in Mechanical Information
System from Kyushu Institute ofTechnology in 2012.

Fig. 11 The occupancy grid map of the factory plant layout built using
Lidar SLAM for scenario simulationDr. Wisanu Jitviriya

7. Conclusion

He is an Assistant Professor ofFaculty of Engineering at King
Mongkut’s
Universityof
Technology North Bangkok inThailand. He graduated from theDepartmentofElectricalEngineering, King Mongkut’s
University of Technology North
Bangkok, in 2011. He received hisD. Eng. degree in Mechanical
Information System from Kyushu Institute ofTechnology in 2016.

On average, the transportation of one cart takes 3.52
minutes to finally replace a new empty cart and ready fora worker to load the foam product to the cart. However,the system needs to add more AGVs to decrease thewaiting time in the queue. To increase work efficiency,
the speed of the AGVs should be adjustable when they
do not hook the cart; this can speed up as well as slow
down the subtasks. Furthermore, the simulation softwaredevelopment can save costs by finding the best solution
without necessitating tests at the factory.

Mr. Noppadol Pudchuen
He received his B.E. degree from
DepartmentofMechanicalEngineering (honors) in 2015 andreceived his M.Eng. from
Department of Instrumentationand Electronics Engineering in2017, King Mongkut’s Universityof Technology North Bangkok inThailand.

Acknowledgements
This work is supported by the Thai Research Fund (TRF)
and Polyfoam Manufacturing Co., LTD under thecontract no. RDG6050047.
References
1. Pérez-Rubio, M.C., Losada-Gutiérrez, C., Espinosa, F.,
Macias-Guarasa, J., Tiemann, J., Eckermann, F., Wietfeld,
C., Katkov, M., Huba, S., Ureña, J. and Villadangos, J.M.,
A realistic evaluation of indoor robot position tracking
systems: The IPIN 2016 competition experience.
Measurement, 135, 2019, pp. 151-162.
2. Amornphun Phunopas, Wisanu Jitviriya, Noppadol
Pudchuen, Sutee Kumjaikong, Songklod Tunsiri and, EijiHayashi. Automated Guided Vehicle System Analysis in
Foam Manufacturing Plant Using Petri-net. Proceedingsof International Conference on Artificial Life and Robotics.
24. 2019, pp. 622-625.
3. Phunopas, A., Blattler, A. and Pudchuen, N., iRAP Robot:
World RoboCup Rescue Championship 2016 and Best inClass Mobility Award. In Robot World Cup, Springer,Cham. 2016, June. pp. 554-564.
4. Jitviriya, W., Chaicherdkiat, P., Pudchuen, N. and Hayashi,E., Development of Automation Recognition of Hazmat

Mr. Songklod Tunsiri

120

He received his B.Eng. degreefrom Department of MechanicalEngineering Technology in 2011
and received his M.Eng. fromDepartment of Instrumentationand Electronics Engineering inKing
Mongkut’s
2015,
University of Technology North
Bangkok in Thailand.

Dr. Eiji Hayashi

He received his PhD from
Graduate School, Division ofScienceandEngineering,
Waseda University, Japan in1994. He is a Professor in
Faculty of Computer Science
andSystemsEngineering,
Kyushu Institute of Technology,
Japan. His main researchinterests
arePerception
information processing and Intelligent robotics system==========================================

121