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org © 2024 IJCRT | Volume 12, Issue 6 June 2024 | ISSN: 2320-2882

Line Following Robot Using Arduino

1
Abhishek Sahu, 2Apoorva Nema, 3Ayushi Negi, 4Anmol Jaiswal, 5 Uma Shankar Kurmi
1
Student, 2Student, 3Student, 4Student, 5Associate Professor
Lakshmi Narayan College of Technology
Kalchuri Nagar, Bhopal 462022, INDIA

Abstract: This paper details the design, development, and evaluation of an autonomous line-following
robot using an Arduino Uno microcontroller. The robot navigates a predefined path marked by a line,
employing infrared (IR) sensors for detection and a Proportional-Integral-Derivative (PID) control
algorithm for motor control. The hardware setup includes an Arduino Uno, IR sensors, DC motors, and
an L298N motor driver mounted on a compact chassis. The software, developed using the Arduino IDE,
processes sensor data to compute the error between the desired path and the robot's actual position, which
is minimized using a PID control algorithm.

Extensive experiments on various track configurations demonstrated the robot's high precision and
stability in following lines. The PID controller's dynamic motor speed adjustments allowed the robot to
efficiently navigate complex paths, outperforming traditional threshold-based methods. This research
highlights the robot's potential applications in automated guided vehicles (AGVs), industrial automation,
and educational tools. The findings underscore the efficacy of PID control in enhancing line-following
robot performance, providing a foundation for future advancements in this field.

Index Terms - Autonomous Navigation, Embedded System, Sensor Data Processing, Path Tracking.
1. INTRODUCTION

Line-following robots are pivotal in robotics, finding applications in industrial automation, warehouse
logistics, and educational environments. These robots navigate autonomously by following a path marked
by a visible or invisible line on the ground. Precision in line following is essential for tasks like automated
guided vehicles (AGVs) in manufacturing and service robots in structured environments.
The objective of this project is to design and develop a highly efficient line-following robot using the Arduino
Uno microcontroller. Chosen for its versatility and strong community support, the Arduino Uno, paired with
infrared (IR) sensors and a motor driver, forms the core of the robot's hardware setup.
In this project, IR sensors detect the line, while a Proportional-Integral-Derivative (PID) control algorithm
adjusts motor speed for accurate path tracking. The PID controller balances simplicity and effectiveness,
ensuring smooth navigation. This paper outlines the complete development process, from hardware assembly
to software programming, and presents experimental results demonstrating the robot's performance across
various track configurations. This research aims to enhance robotics by showcasing a reliable line-following
robot design, setting the stage for future innovations in autonomous navigation For this study secondary data
has been collected. From the website of KSE the monthly stock prices for the sample firms are obtained from
Jan 2010 to Dec 2014. And from the website of SBP the data for the macroeconomic variables are collected
for the period of five years. The time series monthly data is collected on stock prices for sample firm sand

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relative macroeconomic variables for the period of 5 years. The data collection period is ranging from January
2010 to Dec 2014. Monthly prices of KSE -100 Index is taken from yahoo finance.

Components Required:
1. ARDUINO UNO
2. INFRARED(IR) SENSORS
3. DC MOTOR
4. MOTOR DRIVER
5. ROBOT CHASSIS
6. WHEELS
7. POWER SUPPLY
8. CONNECTING WIRES
9. BREADBOARD
10. JUMPER CABLES

2. Literature Review

2.1 Traditional Threshold-Based Methods

Traditional line-following robots employ threshold-based methods for line detection, where sensors detect
changes in reflectivity and trigger predefined responses when certain thresholds are crossed. These methods
are simple to implement, with the robot adjusting its movement based on predefined thresholds. However, they
often struggle with accuracy and reliability, especially in environments with varying lighting conditions and
surface textures. The binary nature of threshold-based decision-making can lead to erratic behaviour, causing
the robot to deviate from the desired path frequently. As a result, these methods are limited in their applicability
to more complex paths and environments.

2.2 ADVANCED CONTROL STRATEGIES

Recent advancements in line-following robot design have explored advanced control strategies such as fuzzy
logic and neural networks. Fuzzy logic systems emulate human decision-making processes by allowing for
imprecise inputs and outputs, enabling robots to navigate more dynamically through uncertain environments.
Neural networks, on the other hand, use machine learning algorithms to adapt and improve performance over
time based on experience. These advanced control strategies offer superior performance and adaptability
compared to traditional methods. However, they require extensive tuning and computational resources,
making them challenging to implement without specialized expertise and significant computational power.

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www.ijcrt.org © 2024 IJCRT | Volume 12, Issue 6 June 2024 | ISSN: 2320-2882

2.3 PID CONTROL APPROACH

THE PID (PROPORTIONAL-INTEGRAL-DERIVATIVE) CONTROL APPROACH IS A WIDELY USED METHOD FOR LINE-
FOLLOWING ROBOTS. IT OPERATES BY CONTINUOUSLY CALCULATING AN ERROR SIGNAL BASED ON THE
DIFFERENCE BETWEEN THE DESIRED PATH AND THE ROBOT'S ACTUAL POSITION. THE PID CONTROLLER THEN
ADJUSTS THE ROBOT'S MOVEMENT BY APPLYING PROPORTIONAL, INTEGRAL, AND DERIVATIVE CONTROL
ACTIONS TO MINIMISE THIS ERROR. PID CONTROL OFFERS A BALANCED APPROACH, PROVIDING SMOOTH AND
ACCURATE MOVEMENT WHILE REMAINING RELATIVELY SIMPLE TO IMPLEMENT AND TUNE. IT CAN EFFICIENTLY
HANDLE VARIATIONS IN LINE DETECTION AND ENVIRONMENTAL CONDITIONS, MAKING IT SUITABLE FOR REAL-
TIME APPLICATIONS LIKE LINE-FOLLOWING ROBOTS. ADDITIONALLY, PID CONTROL PROVIDES STABILITY
ININTRODUCTION
robot movement, minimising oscillations and ensuring precise path following.

3. Methodology

3.1 Hardware Design

1. Microcontroller (Arduino Uno)

The Arduino Uno serves as the brain of the robot, processing sensor data and controlling motor movement
based on predefined algorithms.

2. Infrared (IR) Sensors

IR sensors are strategically positioned on the robot to detect the line on the ground. These sensors emit infrared
light and measure the intensity of reflected light to determine the presence and position of the line

3. DC Motors
DC motors provide the driving force for the robot's movement. The rotation of the motors allows the robot to
manoeuvre along the detected line.

4. Motor Driver (L298N)

The motor driver acts as an interface between the Arduino Uno and the DC motors. It provides the necessary
power and control signals to regulate motor speed and direction.

5. Chassis
The chassis serves as the structural framework that supports and protects the internal components of the robot.
It is typically made of durable materials such as plastic or aluminium and is designed to withstand the rigours
of robotic movement

6. Wheels
Wheels are attached to the DC motors and provide traction and stability for the robot as it moves along the
line. The size and type of wheels may vary depending on the terrain and intended use of the robot.

7. Power Supply
A power supply, such as a battery pack or external power adapter, provides the necessary electrical energy to
operate the microcontroller, sensors, motors, and other electronic components

8. Mounting Hardware
Screws, nuts, and spacers are used to securely attach components to the chassis and ensure proper alignment
and stability of the robot.

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Circuit Diagram

Block Diagram

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3.2 SOFTWARE DESIGN

The software is developed using the Arduino IDE. The core logic involves reading the IR sensor values,
computing the error between the desired and actual positions, and adjusting the motor speeds using a PID
control algorithm.

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www.ijcrt.org © 2024 IJCRT | Volume 12, Issue 6 June 2024 | ISSN: 2320-2882

4. EXPERIMENT AND RESULT

4.1 Experimental Setup

The robot was tested on a track with various configurations, including straight segments, curves, and
intersections. The performance was evaluated based on the robot's ability to maintain alignment with the line
and its stability in movement

4.2 Result

The successful navigation of the robot along the specified path was consistent across various test
configurations, showcasing the effectiveness of its capabilities. The implementation of the PID (Proportional-
Integral-Derivative) controller yielded noticeable enhancements in the robot's stability and precision,
particularly when compared to the performance of a simpler threshold-based controller. Through meticulous
experimentation, the optimal PID parameters were established as ( K_p = 2.0 ), ( K_i = 5.0 ), and ( K_d =
1.0 ), further solidifying the robot's adherence to the desired trajectory. Notably, the robot displayed minimal
oscillation, swiftly rectifying any deviations from its intended path and affirming its adeptness in maintaining
course accuracy.

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www.ijcrt.org © 2024 IJCRT | Volume 12, Issue 6 June 2024 | ISSN: 2320-2882

4.3 Conclusion

This study successfully designed and developed a line-following robot using an Arduino Uno and PID control.
The robot achieved high precision and stability, making it ideal for applications in AGVs and other autonomous
systems. The findings underscore the effectiveness of PID control in line-following robots and lay the
groundwork for further advancements in this field.

REFERENCES

1) Lee, J. H., "A survey on the PID control algorithm," IEEE Transactions on Industrial Electronics, vol. 64,
no. 2, pp. 301-309, 2017.
2) Mahmoud, M. S., PID control: Analysis, design, and technology, Springer, 2018.
3) Smith, A., "Design and implementation of a line-following robot using fuzzy logic," International
Journal of Robotics Research, vol. 12, no. 3, pp. 123-135, 2019.
4) US Kurmi, “Study of different face recognition algorithms and challenges” International Journal of
Engineering Research 3(2) 112-115.
5) Enhancing Performance of Wide Area CIoT SDN by US-ML Based Optimum Controller Placement
A Khera, US Kurmi Research Reports on Computer Science, 112-121
6) ENHANCEMENT OF CELLULAR NETWORK USING APPLICATION OF INDUSTRIAL IOT IN
WAN COMMUNICATION US Kurmi, A Khera IN Patent App. 202,421,002,313
7) A review–design of area and power efficient digital FIR filter based on faithfully rounded truncated
12‐bit constant S Gupta, US Kurmi International Journal of Computer Applications 149 (6)
8) Online Communities and Forums
Participating in online communities and forums dedicated to robotics and DIY projects can provide access to
a wealth of knowledge and expertise. Websites such as Robotics Stack Exchange, Arduino Forum, and
Reddit's r/robotics are excellent platforms for asking questions, sharing ideas, and learning from experienced
enthusiasts and professionals.

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