Automated Guided Vehicle

Abstract

Automated Guided Vehicles (AGVs) are autonomous robots designed to navigate structured environments using sensory and control systems. This paper presents the design and development of a line-following AGV, incorporating infrared sensors, ultrasonic sensors, and a microcontroller-based control system. The AGV is capable of detecting and following a predefined path while dynamically adapting to obstacles and environmental variations. The study explores key technologies, including sensor fusion, path-planning algorithms, and proportional-integral-derivative (PID) control, to enhance navigation accuracy. The implementation of AGVs in industrial automation, logistics, and commercial applications highlights their efficiency in reducing manual labor and improving material handling processes. Despite challenges in unstructured environments, advancements in artificial intelligence, machine learning, and real-time path adjustment are enhancing AGV adaptability. This research contributes to the growing field of autonomous robotics by addressing AGV limitations and exploring future trends in automation and intelligent transportation systems.

Introduction

Automated Guided Vehicles (AGVs) are essential in modern industrial automation, particularly in material handling and logistics. This paper discusses the evolution of AGVs, their benefits (efficiency, cost reduction, accuracy), and challenges (high costs, limited adaptability). It proposes a robust AGV design using infrared (IR) and ultrasonic sensors for line-following and obstacle detection, driven by a microcontroller-based system.

System Design:

1. System Architecture:

• Core Components: ATmega328p microcontroller, IR and ultrasonic sensors, L293D motor driver, servo/DC motors, 12V battery, and an automated charging module.

• Functionality: The AGV initializes with self-checks, follows a predefined path using IR sensors, avoids obstacles using ultrasonic sensors, completes tasks, and returns for charging.

2. Workflow:

• Startup and sensor calibration.

• Line-following using IR sensors with real-time control adjustments.

• Obstacle detection and rerouting or pausing.

• Task execution and automated battery charging.

Problem Statement:

Design an AGV that follows a path, navigates sharp turns and intersections, avoids obstacles in real time, and operates autonomously in various industrial settings with energy efficiency and stability.

Project Objectives:

• Precise path following and obstacle detection.

• PID-controlled motor movement for stability.

• Energy optimization and automated charging.

• Adaptability to complex environments.

Literature Review Insights:

• Sensor Fusion (LiDAR & Vision): Enhances navigation in dynamic environments.

• Path Planning Algorithms (A, Dijkstra):* Improve route efficiency.

• PID Control: Ensures smooth line-following.

• Energy-efficient Charging: Extends operational uptime.

Methodology:

• Hardware: Microcontroller, IR/ultrasonic sensors, motor drivers, DC/servo motors, 12V battery.

• Software: Arduino IDE, Proteus simulation, Embedded C.

• Steps: Component integration, algorithm development, real-time obstacle detection, charging system design, software testing, and performance evaluation.

System Architecture (Detailed):

• Navigation: IR sensors follow a path.

• Obstacle Detection: Ultrasonic sensors prevent collisions.

• Control: Microcontroller processes sensor input, commands motors via L293D.

• Power: Managed through a battery and automatic charging module.

• Motion: DC motors drive; servo motors handle steering.

Results:

• Path Accuracy: High precision with minimal deviation.

• Obstacle Avoidance: Reliable detection and avoidance.

• Navigation Stability: Achieved through PID control.

• Charging Efficiency: Seamless, automated battery management.

• Energy Use: Optimized for extended operation.

• Testing: Proven effective in various simulated industrial scenarios.

Conclusion

The designed AGV successfully demonstrated autonomous navigation using line-following and obstacle detection techniques. The implementation of PID control enhanced stability, making it suitable for industrial automation. Future research will focus on improving adaptability in dynamic environments using AI and deep learning algorithms.

References

[1] IEEE International Conference on Robotics and Automation, \"Automated Guided Vehicle Navigation Using LiDAR and Vision Sensors,\" IEEE, Vol-12, June 2019. [2] International Journal of Automation and Smart Technologies, \"Intelligent Path Planning for AGVs Using A* and Dijkstra Algorithms,\" Springer, Vol-7, March 2020. [3] Journal of Mechatronics and Automation, \"PID-Based Control Mechanism for Line-Following Robots,\" Elsevier, Vol-15, September 2018. [4] International Conference on Industrial Electronics and Energy Management, \"Energy-Efficient Charging Systems for AGVs,\" Elsevier, Vol-5, May 2021. [5] Robotics and Autonomous Systems, \"Sensor Fusion Techniques for Autonomous Vehicle Navigation,\" Elsevier, Vol-30, December 2020. [6] International Journal of Robotics Research, \"Advancements in AGV Technologies for Industrial

Copyright

Copyright © 2025 Geeta R. Kotkar, Purva K. Chaudhari, Sonali S. Shelke, Dr. Ravindra G. Dabhade, Mrs. Madhuri. S. Nimse. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.