Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 52–55___________________________________________________________________________________________________________Design System of Cell Type Assembly Machinewith Dual Arms Robot by GA
Hidehiko Yamamoto*1

Department of Mechanical Engineering, Gifu University, Yanagido 1-1
1－1，Yanagido, Gifu 501-1194, Japan
Keita Honda

ADVICS Ltd.
2-1, Showa-cho, Kariya, Aichi 448-8688, Japan
Takayoshi Yamada*2

Department of Mechanical Engineering, Gifu University, Yanagido 1-1
1－1，Yanagido, Gifu 501-1194, Japan
E-mail: *1; yam-h@gifu-u.ac.jp *2; yamat@gifu-u.ac.jp

Abstract
The purpose of this research is to develop a system named DELUGA which automatically decides by genetic algorithm (GA) where toplace a lot of assembled parts, jigs and robot hands in the workstation when designing a cell type assembly machine with a dual arms
robot. In DELUGA, since the left and right robot arms simultaneously perform assembly work, adopt the concept of waiting time toprevent interference between the left and right arms. The cycle time from assembly to completion is used as fitness, GA operation isperformed, and better arrangement is decided.
Keywords: genetic algorithm, Unit arrangement decision, Dual arm robot, Assembly machine
workbench in front of the robot and the units located aroundthe robot.

1. Introduction
In this research, we develop the system called DEciding
Locations of Units by Genetic Algorithm (DELUGA)
for determining the units arrangement of cell typeassembly machines[1] with a dual arms robot by Geneticalgorithm (GA). The unit includes dual arm robot’s
hands, parts trays and jigs. Generally, the units
arrangement which is one of the components of theworkstation was decided by the production engineer.
We aim to improve the design and production efficiency
by automating the units arrangement determination.

Fig. 1. Cell type assembly machine

2.1. Assembly process
The assembly machine of this study uses a dual armrobot, that is, each arm chucks a unit necessary for an
assembly work, carries it to the assembly workbench andassembles parts. In this research, we define threepositions existing on the movement path of the robot

2. Cell type assembly machine
As shown in Fig. 1, the cell type assembly machine of thisresearch consists of an assembly dual arms robot which islocated in the center of the assembly machine, an assembly

Copyright © 2018, the Authors. Published by Atlantis Press.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 52–55___________________________________________________________________________________________________________arm, approach 1, approach 2 and chuck position. Eachdefinitions is as follows.
Chuck position: position to put or grasp the unit
Approach 1: position upward away from the chuckposition
Approach 2: position further upward than Approach 1
The arm passes from approach 2 through approach 1
and arrives at the chuck position. When the arm leaves
the unit, it performs the reverse operation (see Fig.
2).With this definition, the assembly time calculations
can be formulated.

3.1. Condition creation module
The conditions module is as follows.
Step 1: Read work data of parts assembly, placement
possible area, parts arrangement data and create their
database
Step 2: Divide placement possible area into a lattice
shape to create placement possible place
Step 3: Carry out genes coding
Step 4: Define the fitness function
Step 5: Determine various parameters3.2. GA module
The process flowchart of the GA module is shown in
Fig. 4.

Fig. 2. Movement of arm

3. DELUGA
DELUGA is the system that determines the arrangementof each unit with a high efficiency by using GA when acell type part assembly machine is designing. DELUGA
consists of two modules, the conditions module and GA
module (Fig. 3). The conditions module reads theplacement possible area of the unit and work data,
divides the placement possible area, and sets variousparameters required for GA. GA module decides thearrangement place of the unit by GA, determines thearrangement of parts in the parts tray which is one of theunits by a full search method, evaluates the total
working time, and shows the coordinates of thearrangement place of each unit and the output of theacquired arrangement image.

Fig. 4. Flowchart of GA moduleThis detailed process is described below.
Step 1: Generate an initial population
Step 2: Calculate the arrangement coordinates of eachunit
Step 3: Determine part placement inside a parts tray byfull search method
Step 4: Calculate the time taken for each work
Step 5: Calculate the cycle time of each individual inorder not to collide with both arms
Step 6: Calculate fitness
Step 7: Give selection, crossover and mutation
Step 8: Judge whether the termination condition issatisfied or not. If it is satisfied, go to Step 10
Step 9: Give Step 4 to new individuals
Step 10: The unit arrangement of individuals with thesmallest cycle time is adopted as the optimalarrangement and output as a solution

Fig. 3. System construction of DELUGA53

Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 52–55___________________________________________________________________________________________________________4. Application example of DELUGAGroup E are rearranged. The number of each pattern isshown in Table 3.

The example cell type assembly machine with dual arm
robot consists of the units, the left parts tray, the rightparts tray, the left parallel hand, the left three nail hand,
the right parallel hand, the right four nail hand, the jig A,
the jig B. The goal of DELUGA is to decide the betterlayout of the units. The layout image of the machine isshown in Fig. 5.

Fig. 7. Arrangement pattern of parts

Fig. 5. Units layout

4.1. System comparison
We compared DELUGA with the following twosystems.
· System that determines unit arrangement only by
reinforcement learning·System that determines unit arrangement only by GA
The above two systems cannot find the parts layout inthe tray. On the contrary, DELUGA can determines thearrangement of parts in the tray.
Fig. 6 is the left and right parts tray. Each numbermeans each part. DELUGA decides the part
arrangement in the tray by full search as follows.

Fig. 8. Arrangement of group A
Table 1. Number of patterns
Group AGroup B

6Sorting A and B

2Group C5Group D

6Group E

6Sorting C, D and E

6All patterns

Left parts tray

677,760

Table 1 shows 6 × 6 × 2 = 72 patterns in the left tray
and 5 × 6 × 6 × 6 = 1080 patterns in the right tray.
That means 72 × 1,080 = 77,760 patterns exist.

Right parts tray

Fig. 6. Parts trays

4.2. Arrangement determination method

As shown in Fig. 7, we divide the parts in left andright trays into two groups and three groups. Left tray
has Group A and Group B, and right tray has Group C,
Group D and Group E. Group A has 6 arrangementpatterns. Group B to E also have arrangement patterns.
Also, we consider arrangements in which Group A andGroup B are rearranged, and Group C, Group D andDELUGA calculated 77,760 pattern cycle times of parts
arrangement in the tray for each individual during GA
operations and the parts arrangement in the tray with thesmallest cycle time among them is adopted.

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Journal of Robotics, Networking and Artificial Life, Vol. 5, No. 1 (June 2018) 52–55___________________________________________________________________________________________________________4.3. Details of placement possible area
Table 2 shows the detailed data for the possible
placement areas of each unit.
Table 2. Placement area of each unit
Unit

x1

y1

z1

x2

y2z2

1,811.5

1,760

740

1,918.5

1,804.5

740

2,522

1,806.5

740

2,628

1,807.5

740

1,280

1,480629

2,320

1,480

629

1,280

1,480629

2,320

1,480

629

2,100

1,480629

3,140

1,480

629

2,100

1,480629

3,140

1,480

629

Jig A

3,034

83

740

3,656

1,117740

Jig B

3,034

83

740

3,656

1,117

740

Left
tray
Right
tray
Left
parallelhand
Leftthreeclaw
hand
Right
parallelhand
Rightfourclaw
handFig. 9. Layout of DELUGA

Fig.10. Layout of GA

Table 3. Coordinates of dual arm robot
xyz
Dual arm Robot
2,210915
740

Table 3 shows the coordinates of the dual arm robot.
All units of the label values are mm.
5. Simulation results

Fig. 11. Layout of Reinforcement learning

We acquired the simulation results as follows.
・Minimum working time: Smallest working time ineach simulation
・ Average working time: average of 100 hours
simulation work time・ Plane drawing of unit arrangement: plan view ofarrangement of each unit whose working time is thesmallest
The minimum cycle time and the average cycle timeof 100 simulations are summarized in Table 4. Acquired
unit layouts are shown in Fig. 9, Fig. 10 and Fig. 11.
From the Table 4, DELUGA has the shortest cycle timecompared with other two systems[2].

6. Conclusions
DELUGA is the system that aims the improvement ofdesign and production efficiency by automating the unit
arrangement determinations of cell type assembly
machine by GA. Compared DELUGA with a dual arm
robot system with using reinforcement learning and thesystem using only GA, we were able to obtain the unit
arrangement more efficient than the two systems.
References
1.

Table. 4 Simulation results comparison (seconds)
ReinforcementGA
DELUGA
learning
Minimum time
237.41
237.25234.38Average time
237.68237.26234.892.

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