Originally published on Medium: https://medium.com/@muhammad-ali-mustafa/from-sensors-to-decisions-logic-building-in-a-line-following-robot-2c15f9d0d324
Introduction
When people hear “line following robot,” they often imagine a simple system that just follows a black line. I used to think the same. But while building my own line following robot using Arduino Mega, I realized that the real challenge wasn’t the motors or sensors — it was logic building.
This project taught me how raw sensor signals are transformed into decisions, actions, and controlled motion.
Understanding the Problem
At a basic level, a line following robot must:
- Detect a line using sensors
- Decide how to move based on sensor input
- Handle junctions, turns, and special conditions
In reality, this means dealing with:
- Noisy sensor data
- Timing issues
- Multiple conditions happening at once
That’s where structured logic becomes essential.
Sensor Setup and Input Interpretation
My robot uses:
- Front IR sensors for line alignment
- Middle sensors for junction detection
- TCS3200 color sensor for color-based decision making
Instead of treating each sensor independently, I grouped sensor states into logical conditions:
- On-line
- Slight deviation
- Junction detected
- Color-triggered condition
This made the control flow much cleaner and easier to debug.
From Conditions to Decisions
Once sensor states were defined, the next step was mapping them to actions.
For example:
- If both front sensors detect white → move forward
- If left detects black → steer left
- If right detects black → steer right
At junctions, I used:
- Counters
- Debouncing logic
- Timed delays
This allowed the robot to distinguish between normal line crossings and actual junctions.
Handling Junctions with Counters
A key learning point was junction counting.
Instead of hardcoding turns, I used a counter-based approach, where:
- Each detected junction increments a counter
- Specific counter values trigger specific actions (turn left, turn right, stop, etc.)
This made the path programmable and scalable without changing the hardware.
Adding Conditional Logic with Color Detection
To make the system more intelligent, I integrated a color sensor.
At certain junctions:
- The robot checks for a specific color
- Based on detection, it alters its future path
This introduced the idea of decision dependency, where earlier sensor input affects later behavior — a very important concept in robotics and control systems.
Challenges Faced
Some real-world problems I encountered:
- False junction detection due to sensor noise
- Timing mismatches between movement and detection
- Overlapping conditions causing unpredictable behavior
Solving these required:
- Better debouncing
- Clear priority rules
- Breaking complex logic into smaller functions
What This Project Taught Me
This project helped me understand that robotics is less about hardware and more about structured thinking.
I learned:
- How to design decision trees
- How sensors translate into logic
- How small timing errors affect system behavior
- Why clean, readable logic is critical in embedded systems
Conclusion
Building a line following robot was not just a beginner robotics project — it was an introduction to decision-making systems.
From sensors to decisions, every step required logical clarity. This experience strengthened my foundation in embedded systems, robotics, and control-oriented thinking, and motivated me to explore more complex autonomous systems.
I plan to continue improving this system by adding PID control and better sensor fusion techniques in future iterations.
Top comments (1)
Nice Information