Journal of Robotics, Networking and Artificial Life
Vol. 7(2); September (2020), pp. 77–80
DOI: https://doi.org/10.2991/jrnal.k.200528.001; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Selection System of Robot Type for Cell Assembly
Production (Production Efficiency Comparison ofSingle and Double Arm Robot)
Hidehiko Yamamoto1,*, Takahiro Watanabe2, Takayoshi Yamada1
Department of Mechanical Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan
Mitsubishi Automotive Engineering Co., Ltd., 1 Hashimecho, Okazaki, Aichi 444-8501, Japan12ARTICLE INFOABSTRACT

Article History

The purpose of this research is to compare the production efficiency of single and double arm robot. This research determines
which robot is suitable for an assembly cell production, a single arm robot or a double arm robot. First, we develop the system touse a double arm robot to determine the best parts location by Genetic Algorithm (GA). The system consists of two modules, theconditions module and GA module. The conditions module reads work data for robot and sets up various parameters requiredfor GA. The GA module decides the efficient arrangement place of parts, jigs and robot hands by GA and outputs the acquired
arrangement visual images. Next, we use a single arm robot at the same parts location and compare the working time with adouble arm robot. Finally, we conclude which robot is suitable for an assembly cell judging from the results of a total robot
working time.

Received 06 November 2019
Accepted 30 March 2020
Keywords
Genetic algorithm
unit arrangement decisiondual arm robot
assembly machine© 2020 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
In recent years, it has been demanded to produce a large quantityof products in a short time at the production site. Automations of
the assembling works by robots has been also carried out. Whendeveloping an automatic assembly machine including robots, theproduction efficiency depends on the arrangement of each unit of
the assembly machine. However, in many factories, the placement ofunits is based on the experienced engineers. It remains questionable
whether the determined placement by the experienced engineers isreally efficient. In this research, the layout decision system that assists
unit layout determinations of an efficient cell type assembly machineis developed by using Genetic Algorithm (GA) [1]. The system compares the production efficiency of a single and a double arm robot
and determines which robot is suitable for the assembly job.

2. CELL TYPE ASSEMBLY MACHINEAs shown in Figures 1 and 2, the cell type assembly machine to
be used in this research has a configuration in which an assemblymachine is arranged at the center, a work stand at the front andassembly units on the periphery. After each arm chucks the neededunit, the arm carries it to the workbench to assemble the parts. In
the research, approach 1, approach 2 and the chuck position of therobot arm movement route are defined as the three points that exist
on the arm moving route as follows:
Corresponding author. Email: yam-h@gifu-u.ac.jp*• Chuck position: position for chucking and unchucking parts.
• Approach 1: position upward away from the chuck position.
• Approach 2: position further upward than approach 1.
As shown in Figure 3, the arm passes from approach 2 to approach 1
and arrives at the chuck position.

3. LAYOUT DETERMINATION SYSTEM
The layout decision system we develop decides the arrangementof each unit with the best operation efficiency during designing a cell type part assembly machine by using GA. The system
consists of two modules, the condition creation module and theGA module. The condition creation module performs to readthe arrangement possible area of the unit and work contents, todivide the arrangement possible area and to decide various necessary GA parameters. The GA module determines the arrangeable place of the units, evaluates the working time and outputs
coordinates of the arrangement place of the unit and the layout
image of the unit.

3.1. Condition Creation Module
The procedure of the condition creation module is as follows:
Step 1: Read work data of parts assembly, placement possible area,parts arrangement data and create their database.

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Figure 1 | Cell type assembly machine of double arm robot.

Figure 4 | Flowchart of GA module.
Table 1 | Each arm speed
Figure 2 | Cell type assembly machine of single arm robot.
Approach 2–2
Approach 2–1
Approach 1–chuck

Do not have parts(mm/s)Have parts(mm/s)

7704002538025050

Step 7: The unit arrangement of individuals with the smallest operation time is adopted as the optimum arrangement and the output
as a solution.
A single arm robot comparison procedure.
Figure 3 | Movement of arm.

Step 1: Calculate the unit optimal placement coordinates for tentimes and each work time with a double arm robot using GA.

Step 2: Divide possible placement area into a lattice shape to create
possible placement place.

Step 2: The same work is performed on the industrial robot withthe optimal placement coordinates for ten times obtained with thedouble arm robot, and the work time for each ten times is obtained.

Step 3: Carry out genes coding.
Step 4: Define the fitness function.
Step 5: Determine various parameters.

3.2. GA Module
The process flowchart of the GA module is shown in Figure 4. The
process procedure is shown below.
Step 1: Generate an initial population.
Step 2: Calculate the placement coordinates of each unit.
Step 3: Calculate the time taken for each work.
Step 4: Calculate fitness.
Step 5: Apply genetic operations (selection, crossover, mutation).
Step 6: Judge whether the termination condition is satisfied. If it is,
go to Step 7. If not, go to step 3.

Step 3: Compare work times obtained by both robots to find work
efficiency.
Step 4: Determine which robot is suitable for cell-type automatic
assembly production from the comparison of work time andefficiency.
Using the results of the double arm robot above mentioned, thesingle arm robot performs the same work above. The single arm
robot is set with the same conditions such as the arrangement coordinates and the work process and arm speeds. As shown in Figure 2,
the robot arm approach speeds are changed. Table 1 shows the bothrobot speed.

4. APPLICATION EXAMPLES ANDPERFORMANCE COMPARISONS
The layout decision system was applied to the assembly machine
and the production simulations were carried out. As a comparison,
simulations of the following two systems were carried out.

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5. SIMULATION RESULTS• The system that determines unit layout by GA.
• The same work with a double and a single arm robot.
The units that needs to determine the placements have eight lefttray, right part tray, left parallel hand, left three nail hand, rightparallel hand, right four nail hand, jig A, jig B and also the assemblywork. The layout map is shown in Figure 5. Figure 6 is the left andright part trays with 52 parts. Each number represents each part.
Next, Tables 2 and 3 show the position data and placeable area dataof both robot.

Simulations were carried out ten times in each of the three systems.
The results of the simulations are described below.
• Minimum cycle time:
The smallest work time in each simulation.
• Average cycle time:
Average work time of simulation for ten times.
• Average simulation time:
Average time taken for ten simulations.
Figure 7 shows the output drawing of the unit arrangement withthe highest efficiency. Table 4 shows the best position of units.
Table 5 shows both the simulation and the average times.

Figure 5 | Units layout.

Figure 7 | Best layout of a double arm robot.
Table 4 | Best position of unit
Unit

Figure 6 | Parts trays.
Table 2 | Coordinates of double arm robot

Robotx (mm)y (mm)z (mm)

2210

915

740

Left tray
Right tray
Left parallel hand
Left three claw hand
Right parallel hand
Right four claw hand
Jig A
Jig B

x1 (mm)y1 (mm)z1 (mm)

1915
2527
2055
2205
2365
2565
3379
3039

1775
1807
1480
1480
1480
1480
708
1115

740
740
629
629
629
629
740
740

Table 5 | Both robot simulation times
Table 3 | Placement area of each unit
y1(mm)z1(mm)x2(mm)y2(mm)z2(mm)Left tray
1811.5 1760
Right tray
2522 1806.5
Left parallel hand
1280
1480
Left three claw hand 1280
1480
Right parallel hand
2100
1480
Right four claw hand 2100
1480
Jig A
3034
83
Jig B
3034
83

740
740
629
629
629
629
740
740

1918.5 1804.5
2628 1807.5
2320
1480
2320
1480
3140
1480
3140
1480
3656
1117
3656
1117

740
740
629
629
629
629
740
740

Unit

x1(mm)

One time
Two times
Three times
Four times
Five times
Six timesSeven times
Eight times
Nine times
Ten times
Average times

A double arm robot

A single arm robot

237.253
237.274
237.265
237.265
237.252
237.251237.267
237.300
237.256
237.251237.257

291.591291.602291.585291.595
291.595
291.586291.590291.593291.590291.586291.585

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6. TESTING
The average difference of the average work time of ten times
obtained by each robot was tested. In the alternative hypothesis,
the difference between the average value of the working time of thedouble arm robot was tested and the average difference was tested.
In both cases, the null hypothesis was rejected and the alternative
hypothesis results that there was a difference in the working timeof two robots.

The system compares the production efficiency of a single and adouble arm robot and determines which robot is suitable for assembly.
Comparing the working time of a double and a single arm robot, it
was found that a double arm robot is more efficient in this assembly job. It is better to use a double arm robot because of its work
efficiency.
It is ascertained that the developed system is more useful to use adouble arm robot with this equipment.

7. CONCLUSIONREFERENCE

The layout decision system developed in the research is the system
to improve design and production efficiency by automating the unit
arrangement determinations of a cell type assembly machine by GA.

[1] H. Yamamoto, K. Honda, T. Yamada, Design system of cell typeassembly machine with dual arms robot by GA, J. Robot. Netw.
Artif. Life 5 (2018), 52–55.

AUTHORS INTRODUCTION
Dr. Hidehiko Yamamoto

Dr. Takayoshi Yamada

He received his B.S., M.A. and PhD fromNagoya Institute of Technology. After
13 years of developing production linesfor automotive parts at Toyota Industrial
Corporation, he moved to Wakayama
University in 1991 and Gifu University
in 2000. He is currently a Professor in
Department of Mechanical Engineering. His fields of interest
are production systems, intelligent systems, knowledge learningand cyber physical factory. He is the fellow of the Japan Societyof Mechanical Engineer.

He received his BE, ME, and PhD in
Mechanical Engineering from Nagoya
Institute of Technology in 1991, 1993 and1995. He is a Professor in Department ofMechanical Engineering at Gifu University.
He joined Nagoya Institute of Technologyas a Research Associate of MechanicalEngineering in 1995. He moved to Gifu University as Associate
Professor in 2008. His research interests include grasping,manipulations, sensing and automation systems. He is themember of IEEE, IEEE, the Japan Society of MechanicalEngineers, the Robotics Society of Japan, the Society ofInstrument and Control Engineers and the Japan Society for
Precision Engineering.

Mr. Takahiro Watanabe
He received his B.S. in MechanicalEnginering and M.S. in Intelligent
Mechanichal Engineering from Gifu
University. He joined Mitsubishi Automotive
Engineering Co., Ltd. in 2020.