How I Competed and Won the First Module at NERC — Pakistan’s Premier Robotics Contest

Muhammad Ali Mustafa — Sat, 31 Jan 2026 21:01:09 +0000

Introduction

The National Engineering Robotics Contest (NERC) is one of Pakistan’s most prestigious robotics competitions. Organized annually at NUST College of Electrical & Mechanical Engineering (CEME), NERC brings together engineering students from across the country to test their skills in robotics, logic building, and real-time problem solving.

Participating in Mini NERC and winning the first module was a defining moment in my robotics journey. This article shares what NERC is about, what the competition demands, and what I learned from the experience.

What is NERC?

The National Engineering Robotics Contest (NERC) is a large-scale robotics event designed to promote innovation, teamwork, and practical engineering skills.

It features multiple categories, including:

Autonomous and semi-autonomous robots
Line following and navigation challenges
Competitive robot battles and races
Task-based and logic-driven challenges

Each category pushes teams to design robots that are not only functional, but reliable under pressure.

Mini NERC: My Entry into Competitive Robotics

Mini NERC serves as a smaller but intense version of the main competition. It introduces participants to:

Competition rules and scoring
Real-time performance evaluation
Hardware and logic constraints
Time and stress management

For me, Mini NERC was the first time I truly experienced robotics outside a lab environment.

Understanding the First Module

The first module focused on fundamental robotics skills, including:

Accurate line following
Handling turns and junctions
Stable motion control
Logical decision-making

Although it looked simple, even small logic errors could lead to complete failure.

Our Approach

Instead of chasing maximum speed, we focused on structured and predictable logic.

Key design choices:

Clear sensor state definitions
Priority-based decision handling
Controlled speed for stability
Extensive testing under realistic conditions

This allowed our robot to behave consistently throughout the run.

Challenges We Faced

Some of the challenges we encountered were:

Sensor noise causing false detections
Timing mismatches during turns
Minor alignment errors at higher speeds

We addressed these by:

Adding debouncing logic
Simplifying control flow
Tuning delays carefully

These changes significantly improved reliability.

Winning the First Module

During the final run, the robot:

Followed the line smoothly
Handled junctions without confusion
Completed the module reliably

Securing first position in the first module of Mini NERC validated our focus on clean logic and preparation.

What NERC Taught Me

Participating in NERC taught me that:

Robotics is more about thinking than hardware
Simple, clean logic outperforms complex systems
Reliability matters more than aggressive performance
Team coordination is as important as technical skill

These lessons now guide how I approach all robotics and embedded systems projects.

Why NERC Matters for Engineering Students

NERC provides students with:

Real-world engineering challenges
Exposure to competitive robotics
Opportunities to learn under pressure
A platform to showcase skills and creativity

It bridges the gap between academic learning and practical engineering.

Conclusion

Winning the first module at Mini NERC was more than a competition victory — it was a learning milestone.

NERC strengthened my foundation in robotic logic, embedded systems, and decision-making, and motivated me to continue exploring advanced robotics and autonomous systems.

From Sensors to Decision: Logic Building in a Line Following Robot

Muhammad Ali Mustafa — Sun, 28 Dec 2025 17:05:34 +0000

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.

Forem: Muhammad Ali Mustafa

How I Competed and Won the First Module at NERC — Pakistan’s Premier Robotics Contest

Introduction

What is NERC?

Mini NERC: My Entry into Competitive Robotics

Understanding the First Module

Our Approach

Challenges We Faced

Winning the First Module

What NERC Taught Me

Why NERC Matters for Engineering Students

Conclusion

From Sensors to Decision: Logic Building in a Line Following Robot

Introduction

Understanding the Problem

Sensor Setup and Input Interpretation

From Conditions to Decisions

Handling Junctions with Counters

Adding Conditional Logic with Color Detection

Challenges Faced

What This Project Taught Me

Conclusion