4-Wheel Fuzzy Logic Line-Following Robot (MATLAB & Arduino/ESP32)
🧩 Product Overview
Experience the next level of intelligent mobile robotics with this 4-wheel differential-drive fuzzy logic line-following robot.
This project merges MATLAB-based AI simulation, Mamdani/Sugeno fuzzy control, and real-time hardware interfacing through Arduino or ESP32.
Ideal for students, researchers, and robotics enthusiasts, this professional package demonstrates soft-computing control systems, MATLAB-Arduino communication, and autonomous navigation — all in one ready-to-run project.
🚀 Key Features
✅ 4-Wheel Differential-Drive Model — complete with realistic kinematic and dynamic equations
✅ Fuzzy Logic Controller (FIS) — human-like steering for smooth, stable line tracking
✅ Real-Time MATLAB Simulation — detailed visualization with multiple track patterns
✅ Hardware-Ready Setup — plug-and-play integration with Arduino/ESP32
✅ Adjustable Speed Modes — Normal / Fast / Turbo operation
✅ Dynamic Sensor Simulation — left, center, and right IR sensors fully modeled
✅ Collision-Free Environment — professional 2D simulation layout
✅ Comprehensive Documentation — includes modeling, equations, diagrams, and design theory
🧠 Technical Details
| Parameter | Specification |
|---|---|
| Programming Platform | MATLAB R2022a or later |
| Controller Type | Fuzzy Logic (Mamdani/Sugeno) |
| Robot Type | 4-Wheel Differential Drive |
| Sensors | 3 IR Line Sensors (Left, Center, Right) |
| Output Variables | Left & Right Wheel Velocity |
| Communication | Serial (USB or Wi-Fi) |
| Supported Hardware | Arduino Uno / Mega / ESP32 |
| Control Type | Closed-loop feedback with fuzzy rules |
📦 What’s Included
💾 Software
-
✅ Complete MATLAB Simulation Code (
.m+.fis) -
✅ Hardware Integration Script (Arduino Serial Ready)
-
✅ MATLAB GUI (optional) for visual control
-
✅ Mamdani Fuzzy Logic System with editable rules
📘 Documentation
-
📄 Detailed Report (.docx )
💡 Learning Outcomes
🎯 Understand fuzzy logic controller design for mobile robots
🎯 Derive and apply kinematic models for differential-drive systems
🎯 Integrate MATLAB simulation with Arduino hardware
🎯 Analyze sensor feedback and control system performance
🎯 Apply MATLAB Fuzzy Logic Toolbox in real robotics projects
🔧 Hardware Integration (Optional Real-World Setup)
Required Components:
| Component | Qty | Function |
|---|---|---|
| Arduino Uno / ESP32 | 1 | Main controller |
| L298N Motor Driver | 1 | Dual DC motor control |
| IR Line Sensors | 3 | Line detection (L, C, R) |
| DC Motors | 2 | Drive wheels |
| Robot Chassis (4WD) | 1 | Base platform |
| Li-ion Battery Pack | 1 | Power supply |
| Jumper Wires + Breadboard | — | Interconnections |
Communication Options:
-
USB Serial (Default) – MATLAB ↔ Arduino communication
-
Wi-Fi (ESP32) – for wireless line tracking
💻 How to Run
🧠 MATLAB Simulation
-
Open MATLAB.
-
Set working folder to project directory.
-
Run the main file:
-
Choose a map (circle, rectangle, maze, etc.).
-
Observe live trajectory animation, sensor states, and fuzzy control behavior.
🔌 Hardware Mode
-
Upload
lineFollowerSerial.inoto Arduino/ESP32. -
Connect sensors and motors as described in wiring guide.
-
In MATLAB, enable:
-
Run the simulation — MATLAB will send control data in real time.
📈 Simulation Outputs
📊 Trajectory Visualization – robot path vs reference
📉 Error Plots – lateral and heading deviations
⚙️ Motor Velocities – left and right wheel outputs
🧩 Fuzzy Surface View – inference map of control actions
💼 Best Suited For
🎓 Engineering Students (Robotics / Mechatronics / AI / Control)
🔬 Final Year / Research Projects
🤖 Robotics & IoT Enthusiasts
💻 Automation Startups & Institutes
🏫 Teaching & Lab Demonstrations
📞 Support & Contact
👨💻 Developer: EngrProgrammer
📧 Email: mrengineer294@gmail.com
📸 Instagram: @engrprogrammer2494
📺 YouTube: @engrprogrammer
🌐 Shop: https://engrprogrammer-shop.fourthwall.com
⭐ Your feedback helps improve future projects — don’t forget to leave a review!