Southern Taiwan University
PSO-based Fuzzy Controller
Design for Robot Soccer
Department of Electrical Engineering, Southern Taiwan University,
Tainan, R.O.C
Juing-Shian Chiou, Chi-Jo Wang, Shih-Wen Cheng,
Kuo-Yang Wang, and Yu-Chia Hu
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Southern Taiwan University
Outline
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
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Abstract
Introduction
Motion Fuzzy Controller Structure
Particle Swarm Optimization algorithm
Simulation Results
Conclusion
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Abstract

This paper will use the algorithm based on fuzzy to combine with
particle swarm algorithm, applying to the mobile robot’s obstacle
avoidance, determine the fuzzy algorithm and Particle Swarm
Optimization (PSO) to design the optimal route and speed.

In this paper, we will use this algorithm in five-versus-five
simulation platform, it knows whether the combination of these
algorithms can be quickly and accurately to achieve our objectives.
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Introduction(1/3)

we propose the application of this algorithm - fuzzy particle swarm
algorithm, the advantage of PSO, convergence time is quicker than
others and easy to modify, the above features are very special in the
algorithm.

In this experimental platform, we choose the robot Football
Association 5-vs-5 simulation platform in Figure 1
Figure 1. The Five-versus-Five simulation platform
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Introduction(2/3)

Using to cluster features for the particle swarm, to find out how to
avoid obstacles in the move, at the same time moving towards the
destination path planning, and this focus on how to quickly take the
lead particles are individual optimal solution, also obtained group
optimal solution, show in Figure 2.
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Introduction(3/3)
Set of fuzzy rule.
Fuzzy controller of robot wheels
Speed.
Evaluate each particle's fitness
function.
Records of individual particles and groups
of the best memories.
Update the particle
position and velocity.
No
Terminating condition.
Figure 2. System structure.
Yes
END
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Motion Fuzzy Controller
Structure(1/7)

In this part, we start design the fuzzy logic controller aimed at
producing the velocities of the robot right and left wheel. We set
two input parameters of the fuzzy logic controller are distance d
and angle  .

The former d is the distance between the robot and the goal. The
latter  is the direction of with on the straight line path to the
goal. Both are shown in Figure 3.
Figure 3. the relation of d and 
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Motion Fuzzy Controller
Structure(2/7)
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We set the values of variable e1 , e2 , e3 , e4 , v1 , v2 , y1 , y2 and design two
fuzzy controllers to control the velocity of the right and left wheels to
move the robot.

The fuzzy rules on which were based these fuzzy controllers are
described in tables 1 and 2, and can be described according to the
following equations:
Ry1  j1 , j2  : IF
e1
is
A1, j1 
And
e2
is
A 2, j2 
Then
j1 , j2 3, 2, 1,0,1,2,3
A
A
Ry2  j3 , j4  : IF e3 is 3, j1  And e4 is  4, j2 
y1 is
y1 j1 , j2 
Then
y2 is y2 j3 , j4 
j3 , j4 3, 2, 1,0,1,2,3
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(1)
(2)
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Motion Fuzzy Controller
Structure(3/7)
Table 1. Fuzzy rule base of the leftwheel velocity fuzzy controller
Table 2. Fuzzy rule base of the rightwheel velocity controller
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Motion Fuzzy Controller
Structure(4/7)

The following term sets were used to describe the fuzzy sets of each
input and output fuzzy variables:
T  ei   NB, NM , NS , Z , PS , PM , PB , i  1,2,3,4


(3)
 Ai ,3 , Ai ,2 , Ai ,1 , Ai ,0 , Ai ,1 , Ai ,2 , Ai ,3 ,
T  ym   NB, NM , NS , Z , PS , PM , PB , m  1,2


 y m,3 , y m,2 , y m,1 , y m,0 , y m,1 , y m,2 , y m,3 ,
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(4)
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Motion Fuzzy Controller
Structure(5/7)

As show in figure 4, the triangle membership function and the
singleton membership function are used to describe the fuzzy sets
of input variables and output variables.

NB
0
NM
NS
Z
PS
PM
PB
10
20
30
40
50
60
(inch)
(a)
NB
NM
NS
90
60
30
Z
0
PS
PM
PB
30
60
90
(b)
Figure 4. Membership function: (a) the fuzzy sets for ei ;
(b) the fuzzy sets for ym .
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
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Motion Fuzzy Controller
Structure(6/7)
Based on the weighted average method, the final output of these
fuzzy controllers can be described by means of equation (5) and (6)

y1 
3
3
  w
j1 3 j2 3

w j1 , j2  
y1 j1 , j2 
j1 , j2 
(5)
y2 
3
3
  w
j3 3 j4 3
j3 , j4 
y1 j3 , j4 
(6)
Where w j1 , j2  and w j3 , j4  were determined according to Equations
(7) and (8).

min u A1, j   e1  , u A2, j   e2 
1
2
  min  u  e  , u
3
3
j1 3 j2 3
A1, j 
1
1
A 2, j 
2

 e2  
(7)
w j3 , j4  

min u A3, j   e3  , u A 4, j   e4 
  min  u
3
3
j1 3 j2 3
12
4
3
A 3, j 
3

 e3  , u A   e4  
4, j4
(8)
Southern Taiwan University
Motion Fuzzy Controller
Structure(7/7)

When the input data of e1 , e2 , e3 and e4 are given, y1 and y2 can be
determined by using Equations (5) and (6) Thus, the left-wheel velocity
vl and the right-wheel velocity vr can be obtained.
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Particle Swarm Optimization
algorithm (1/3)

Initially the group is based on the flock-based mobile way, there are
based on particles, the first, particles will be randomly distributed in
space, each particle has own optimal solution, also to the group's
information to determine the optimal location of the next movement
and speed, the optimal by different individuals, repeat implementation
to find the overall optimization, after iterative calculation method, to
achieve the optimization goal.

In the particle swarm system in the simultaneous existence of
individual optimal value pbesti and the group optimal value gbesti ，
it will use the robot to avoid obstacles function, the schematic diagram
of pbesti and gbesti below.
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Particle Swarm Optimization
algorithm (2/3)
v  wv  c  rand ()  ( gbest  s ) 
k 1
k
i
i
1
k*
k
i
i
(9)
c  rand ()  ( pbest  s )
k
2
#
k
i
s  s v
k 1
k
k 1
i
i
i
i
(10)
According to the above function, determining the velocity and
position, the maximum speed limit vmax for each particle, and
the maximum distance limit smax ，When the speed limit and
greater limit than distance, The speed and distance will be
defined as vmax or smax 。
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Particle Swarm Optimization
algorithm (3/3)
Figure 5. Particle velocity and position graph
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Simulation Results(1/2)

We use the particle swarm algorithm to simulate the path and the
avoidance function.
(a)
(b)
Figure 6(a)(b). Use the PSO to modify the fuzzy rule, the robot to
achieve faster
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Simulation Results(2/2)
(a)
(b)
Figure 7(a)(b). While planning a path ahead to avoid obstacles in
the movement
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Conclusion

In this experiment, we use particle swarm algorithm to avoid
obstacles, at the same time toward the destination, and through the
particle swarm faster convergence to obtain the optimal solution, can
achieve the path planning objects quickly, at the same time as change
with the environment, and immediately change its pre-determined
parameters, PSO is easy to change, the platform is also very easy to
operate.
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Thanks for your attention !
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