International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 6- June 2013
Intelligent Controling System for Path Finder
Tanvi Patel#1
#
Department of Biomedical Engineering, Govt. Engineering College, Gandhinagar, India
Abstract— Nowadays in the world of globalisation and drastically
increasing technologies and changing environment, problems of
the visually impaired person are increasing day by day. This
paper shows how the intelligent controlling stimulation system
and algorithm developed and verified in MATLAB (.fis file) for
the path finder. The scheme proposed below will allow the path
finder to navigate in an unknown environment with auto
guidance facility based on fuzzy logic (IF-THEN rules) approach.
The inputs considered here signals from the sensors after
obstacle detection and output we get desired angle rotation for
the wheels. The proposed scheme has been presented below for
the path finder for navigating through an unknown environment
and known environment their merits and demerits.
Keywords— Auto guidance, Fuzzy logic, Motor rotation,
Obstacle avoidance.
I. INTRODUCTION
Looking towards the scientific development and rapid
increasing demands of the society. The problems of the
visually impaired persons have been a major consideration
regarding their navigation in their day to day life. The
international health society stated that 80% of these persons
(600 Millions around the world) are living in underdeveloped
countries and suffering many social and psychological
problems due to poverty and insufficient education. These
countries succeeded to overcome this problem by increasing
the health care and applying special training programs [1].
Path finder with auto guidance facility is perfect solution
for the problems of the visually impaired persons. It based on
the intelligent controlling systems which have the ability to
navigate through an known and unknown environment based
on Fuzzy logic (IF-THEN rules). Rule-based fuzzy logic
provides a scientific formalism for reasoning and decision
making with uncertain and imprecise information. The main
advantages of a fuzzy navigation strategy lie in the ability to
extract heuristic rules from human experience, and to obviate
the need for an analytical model of the process [2].
In this approach a framework is developed to be
implemented in the path finder for obstacle avoidance and
controlling. It based on fuzzy logic (IF-THEN rules) based on
linguistic terms instead of mathematical equations. For
example, considering a temperature control system one can
write“IF room temperature is WARM,
THEN set fan speed to MEDIUM” [3]
Here is a algorithm for the obstacle avoidance and desired
steering angle prepared fuzzy inference systems with Fuzzy
Logic Toolbox software in MATLAB. Stimulation can also
prepared by other software’s like LABVIEW.
ISSN: 2231-5381
II. FUZZY LOGIC APPROACH
Fuzzy logic uses the entire interval between one and zero,
and can therefore be used to closely mimic human reasoning.
The design process of a fuzzy logic system can generally be
separated into three stages [4]:

Fuzzification

Rule Base

Defuzzification
The fuzzification module transforms numerical variables in
fuzzy sets, which can be manipulated by the controller [5].The
input are fuzzified by the membership function. This
membership function (triangular, trapezoidal, bell shaped etc.)
maps the crisp inputs to a degree of values of membership for
each function. Then the rules are developed after the
membership functions are developed. After the rules
evaluation, in the defuzzification stage the output fuzzy values
are again mapped to provide the crisp output. This can be
done by various defuzzification method like centre of gravity,
centre of sums etc.
III. FUZZY CONTROLLER
The fuzzy logic controllers (FLC) make a non-linear
mapping between the input and the output using membership
functions and linguistic rules (normally in the form if-then).
In this paper we have developed a system in which we have
taken inputs from 3pairs of sensors (left, front and right) and
output is the desired steering angle of rotation of motor, two
wheels(left wheel and right wheel) are turned. Specific
membership function is chosen for mapping of crisp input to
the fuzzy output in range [0, 1]. The ultrasound sensors, IR
sensors etc can be used for obstacle detection. According to
proper reasoning provided intelligent controlling system is
used for navigating the path finder in unknown and known
environment.
On the bases of input and output parameters membership
function are defined and rules are set A fuzzy controller is
choosen on the information from the sensors. In this paper we
used Mamdani fuzzy controller.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 6- June 2013
A. Structure
This controller consists of three main parts: fuzzification,
rules evaluation and defuzzification.
The inputs have to be crisp values in order to allow the
fuzzification using membership functions, and the outputs of
this controller are also crisp values. The membership function
of the Left sensor is shown in Fig. 1[4]. The input to the
controller from the sensor is considered as Low, Medium and
High signal.
Fig3.Membership function for right wheel
Fig 1: Mamdani Fuzzy Controller Block Diagram
B. Fuzzification Process
For mapping the crisp values to fuzzy ones, you have to
evaluate their membership degree using membership
functions. With this you get one fuzzy value for each crisp
input. The input from the sensors(left, right and front) are
taken with signals obtained after detection of obstacles
according to inputs membership function is selected and
mapping is done accordingly in range [0 1].The membership
function considered over here is Triangular function Fig 2.
D. Defuzzification Process
To defuzzify the outputs we use the center of sums
method. In this method we take the output from each
contributing rule, and then we add them.
The center of sums is one of the most popular methods for
defuzzification because it is very easy to implement and gives
good results. The equation (1) is shown below [6]:
∗=
∑
∑
.∑
∑
(
(
)
)
........ (1)
C. Rule Evaluation
The controllers define rules for controlling the motor
and obtain a desired steering angle for wheels. If the
signal is S1-low and S2- low and S3- low then the angle
for Left wheel is NR and Right wheel will be NR. The
following 27 rules are defined as follows:
Fig 2: Membership function of Left Sensor
ISSN: 2231-5381
IV. RESULT AND CONCLUSION
In this paper we developed a algorithm in Matlab(.fis file)
based on Fuzzy Logic(If –Then rules) for path finder to
navigate safely from an unknown and known environment by
providing auto guidance facility. We get the desired steering
angle of motor , it has two wheels left and right wheels.
Intelligent controlling system based on Fuzzy logic is
considered for decision making and controlling the motor
rotation. It can be further carried in future with more inputs
and implemented in many systems by developing more rules.
The major problem regarding this algorithm creation is
during its implementation and complexity increases as a
person develop many rules as per his/her knowledge.
With help of past experiences and knowledge gathered we
can develop human-reasoning based operating system by
defining certain rules based on their gained knowledge and get
the desired output.
The rule viewer of the membership function is shown
below in Fig 4.
When at distances obstacle is considered left sensor1 = 5,
S2-front = 2.5 and S3 = 1 then Left-wheel = -40 degree and
Right-wheel = - 20 degree.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 6- June 2013
TABLE I
Where,
SML –SMALL,
NS –NEGATIVELY SMALL,
MS –MEDIUM SMALL,
VS –VERY SMALL,
NR –NO ROTATION OR SLIGHT ROTATION,
BG –BIG,
PB –POSITIVELY BIG,
MB –MEDIUM BIG,
TB – TOO BIG
Input From The
Sensors
S1_
S2_
S3_
Left
Front
Right
Output The Steering
Angle
Left _
Right _
Wheel
Wheel
RULE - 1
HIGH
HIGH
HIGH
NR
NR
RULE -2
HIGH
HIGH
MEDIUM
BG
BG
RULE -3
HIGH
HIGH
LOW
PB
PB
RULE -4
HIGH
MEDIUM
HIGH
NR
NR
RULE -5
HIGH
MEDIUM
MEDIUM
BG
BG
RULE -6
HIGH
MEDIUM
LOW
MB
MB
RULE -7
HIGH
LOW
HIGH
NR
NR
RULE -8
HIGH
LOW
MEDIUM
TB
TB
RULE -9
HIGH
LOW
LOW
BG
BG
RULE -10
MEDIUM
HIGH
HIGH
NS
NS
RULE -11
MEDIUM
HIGH
MEDIUM
VS
NS
RULE -12
MEDIUM
HIGH
LOW
TB
PB
RULE -13
MEDIUM
MEDIUM
HIGH
SML
SML
RULE -14
MEDIUM
MEDIUM
MEDIUM
NR
NR
RULE -15
MEDIUM
MEDIUM
LOW
MB
MB
RULE -16
MEDIUM
LOW
HIGH
NR
NR
RULE -17
MEDIUM
LOW
MEDIUM
NR
NR
RULE -18
MEDIUM
LOW
LOW
BG
BG
RULE -19
LOW
HIGH
HIGH
MS
MS
RULE -20
LOW
HIGH
MEDIUM
SML
SML
RULE -21
LOW
HIGH
LOW
MS
MS
RULE -22
LOW
MEDIUM
HIGH
MS
VS
RULE -23
LOW
MEDIUM
MEDIUM
MS
VS
RULE -24
LOW
MEDIUM
LOW
BG
BG
RULE -25
LOW
LOW
HIGH
NR
NR
RULE -26
LOW
LOW
MEDIUM
VS
VS
RULE -27
LOW
LOW
LOW
NR
NR
Fig 5: Rule viewer for the membership functions
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[4.]
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ISSN: 2231-5381
Pedro Ponce-Cruz • Fernando D. Ramirez-Figueroa “Intelligent
Control Systems with Lab VIEW™”
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