Autonomous Vehicle and Line Tracking Project

Project Context and Problem Statement

Automated Guided Vehicles (AGVs) are a backbone of modern logistics and smart factories, as they transport materials accurately without human intervention.

This project addresses autonomous navigation by designing a vehicle capable of following a predefined path with high precision and correcting its deviations in real time. It highlights the role of navigation intelligence in simplifying and securing industrial transport operations.

Technical Components and Engineering Design

To achieve accurate path tracking, the project uses a strong mechanical structure and a responsive sensing system:

• Chassis and motion system (4WD): a solid chassis equipped with four driving wheels connected to four DC motors, ensuring high stability and traction suitable for different surfaces.
• Optical sensing system: IR line tracking sensors placed at the front of the vehicle to read the color contrast between the black line and the white background.
• Control unit and power management: an Arduino board used as the central brain, connected to a motor driver to distribute power and control the speed and direction of each wheel separately.

Implementation and Methodology

The system uses a continuous feedback loop to keep the vehicle on track:

1. Continuous reading: the IR sensors emit infrared rays; the rays reflect on white and are absorbed by black, allowing the system to determine the path position.
2. Data analysis: the control unit receives these readings in fractions of a second.
3. Movement correction (navigation intelligence): if the robot is centered, all four motors keep moving forward. If it deviates to the right, the left wheels slow down or stop to bring it back to the path, and the opposite happens when it deviates left.

Connection to the Sustainable Development Goals

This project supports the Sustainable Development Goals through:

• Industry, innovation and infrastructure (Goal 9): presenting a small-scale model of automation technologies that improve factory efficiency and reduce logistics accidents.

Team and Task Distribution

The project was completed through integrated technical cooperation to ensure harmony between the vehicle mechanics and the tracking algorithm:

• Vision engineering and navigation intelligence: handled by Loukman El Addouti, who focused on programming the infrared sensors and developing the control algorithm to keep the vehicle on the path without deviation.
• Motion engineering and path tracking: handled by Mohamed Amine Safiyati, who assembled the 4WD chassis, connected the motors to the driver module and programmed the wheel response to ensure smooth movement and accurate turns.