This document summarizes a research paper that designed and implemented an RFID line-follower robot system with color detection capabilities using fuzzy logic. The system allows a robot to follow multiple colored tracks by using LED lights to detect color with an LDR sensor. RFID tags provide input to determine which color track to take. A microcontroller integrates the sensor readings and uses a fuzzy logic inference system and rule base to generate motor control signals. The system was tested for color track detection, response to brightness and reflectiveness, and for selecting tracks based on RFID input. Limitations and suggestions for improvement are also discussed.
Water Industry Process Automation & Control Monthly - April 2024
Line follower robot with color detection capability
1. Design and Implementation of
RFID Line-Follower Robot System
with Color Detection Capability
using Fuzzy Logic
Presented by
Akhil K J
10-06-2018 1
2. About the paper
•Title
Design and Implementation of RFID Line-
Follower Robot System with Color Detection
Capability using Fuzzy Logic
•Authors
M B. Nugraha [Telkom University Bandung, Indonesia]
Rizki Ardianto P [Telkom University Bandung, Indonesia]
Denny Darlis [Telkom University Bandung, Indonesia]
Year: 2015
10-06-2018 2
3. Introduction:-
•Industrial mobilization and
transportation system (forklift)
•AGV (Automated Guided Vehicle)
•Line- follower robot.
• LED and LDR- based color sensor (Fuzzy
Logic)
• RFID-based identification/authorization
system
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5. AGV (Automated
Guided Vehicle)
• Automatically guided from one point to another
in industries
• Used for the mobilization of raw materials and
products.
• Only moves through pre defined paths.
E.g. Line follower robot
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7. Line follower robot
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• Flows a track of dark line.
• Light sensor is used to detect the path.
• Used in industry as AGV
• It can only pass through the single line.
• Simple in construction and accurate
tracking can obtain.
8. Line follower with multiple track
• The line-follower robot system
that is able to recognize some
kind of color, in this case the
basic colors Red, Green and
Blue (RGB) as the system
guidance.
• RFID as inputs to determine
which track to be selected.
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9. LED produces colored light
colored objects illuminated
with light in same color, will
reflect more light intensity than
if it is illuminated with other
color.
LDR sense the reflected light
from the track
RGB Color Detection And
Color Sensor Design
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10. • Used as the initial input system
• Each card can only activate the system for
only one route.
• This is done to limit the authority of the user.
• RFID tags will send its ID information when it
gets electromagnetic signals from a
compatible device, RFID reader.
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RFID Identification
System
11. Fuzzy Logic Simulation
• Process the LDR-based sensor output value
(voltage),
• Certain input system will produce output as
expected from the formation of fuzzy rule.
• The system uses inputs from sensors and generates
motor control that makes the robot can move
according to the guide lines
Table : Sensor Response for Each Color
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13. Test on system
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• RFID System
• Color Sensor System
• Robot Drive System
• System Response of Color Track
Brightness and Reflectiveness
17. System Response of Color
Track Brightness and Reflectiveness
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18. Limitations in this concept
1. It only discuss with tracks of three coloures.
2. Branching of the tracks are not explained
3. Description and testing of only prototype is
given.
4. The program or the logic loaded in the
microcontroller is not given.
5. Obstacle avoiding facility is not included.
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19. Suggestions to improve this idea
• Include obstacle avoiding facility
• Use track with secondary colours
• Include object detection capability
• Include ANN technology
• Branching
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20. CONCLUSION
The system is designed for the mobilization in
industries.(AGV)
It increase the utilization by increasing the number of
tracks.
The tracks are selected by an external input (RFID)
Tracks are detected by coloured light and LDR
assembly
The system consist of sensors, motor driver, RFID
reader and microcontroller.
Tests on the system for controlling parameter and
physical conditions
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21. FUZZY SETS
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Three fuzzy sets for input and one fuzzy set for
output using triangular membership function
are created
22. 10-06-2018 22
Sugeno-type Fuzzy Interference was chosen for the
process of formulating the mapping from the given
inputs to the output.
For defuzzification process, all consequent
membership functions are represented by singleton
spikes.
The weighted average (WA) of these singletons is
used to get the crisp output. The equation
23. RULES
RULE
IF RED
IS
AND GREEN
IS
AND BLUE
IS
THEN OUTPUT IS
1 Low Low Medium Blue
2 Low Low High Blue
3 Low Medium High Blue
4 Medium Low High Blue
5 Low Medium Low Green
6 Low High Low Green
7 Low High Medium Green
8 Medium High Low Green
9 Medium Low Low Red
10 High Low Low Red
11 High Medium Low Red
12 High Low Medium Red
13 Medium Medium High Blue
14 Medium High Medium Green
15 High Medium Medium Red
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24. 10-06-2018 24
Figure 5.1: A Fuzzy Logic System.
PC with MATLAB
software
Microcontroller Colour Sensor
readingoutput
Serial
Comm
Figure 5.1.1. Colour Sensor-MATLAB setup.
A fuzzy logic system (FLS) can be defined as the nonlinear
mapping of an input data set to a scalar output data. A FLS
consists of four main parts: fuzzifier, rules, inference engine,
and defuzzifier. These components and the general architecture
of a FLS is shown
Mamdani's fuzzy inference method is the most commonly seen fuzzy methodology. Mamdani's method was among the first control systems built using fuzzy set theory. It was proposed in 1975 by Ebrahim Mamdani [1] as an attempt to control a steam engine and boiler combination by synthesizing a set of linguistic control rules obtained from experienced human operators.
using fuzzy logic because it changes depend on light conditions and have a volatile measurement value.
From the MATLAB simulation results, data sensors test and implementation of fuzzy rules are used to obtain the employment patterns in a system
system recognizes which path to follow based on the brightest color.
The initial input is received from the RFID reader and then the system will activate which color LED to be used.
Color sensor detection results in Table 4 show that the system recognizes which path to follow based on the brightest color reflection received by the sensor.
Table 5 shows the results of testing when the system moves from the starting point to the destination point. The system is tested using different motor drive PWM values and
sensor reading rates to obtain the optimal condition of the system. From the test results can be seen that the optimal conditions for the system is on the PWM value of 100% and a detection rate of 70ms which will result in speed of the robot movement from the starting point to the destination point at 0,083m/s.
This study also testing and analyze which track condition is applicable for the system operation. Color brightness were separated into 3 type of brightness; high, normal and low. And for the reflectiveness, there are 3 type of material used for testing; normal white paper, glossy photo paper and grey newsprint paper.
Result from table 7 shows for white paper, normal and high color brightness still applicable for the system and highbrightness level has the optimum value for system operation
The system also have optimum result of the color line detection and successfully move from start to stop with 70ms sensor refresh rate and 100% PWM value resulting to 0.08m/s movement speed.