The goal of this project is to introduce students to robotics, movement, and intelligent decision-making. Learners will build a robot car that uses sensors to detect and avoid obstacles automatically. This project teaches programming logic, motor control, and problem-solving through hands-on learning. By connecting the robot to real vehicles and robots seen in daily life, students understand how machines navigate safely. The project builds teamwork, creativity, and confidence while laying a strong foundation for robotics, automation, and future AI-based systems.

Follow these steps carefully. This section explains exactly what connects to what.

1. Power Connections

⚠️ Motors must use a separate battery.

• Connect Arduino GND to L298N GND (common ground).

• Connect battery positive (+) to L298N +12V.

• Connect battery negative (–) to L298N GND.

• Keep Arduino powered via USB or regulated 5V.

2. Connecting the DC Motors to L298N

• Connect Left Motor wires to OUT1 and OUT2 on L298N.

• Connect Right Motor wires to OUT3 and OUT4 on L298N.

👉 Motor direction depends on polarity; reverse wires if needed.

3. Connecting L298N Control Pins to Arduino

Remove the EN jumper caps to control speed via Arduino.

• ENA → Arduino pin 5 (PWM – speed control for left motor)

• IN1 → Arduino pin 8

• IN2 → Arduino pin 9

• IN3 → Arduino pin 10

• IN4 → Arduino pin 11

• ENB → Arduino pin 6 (PWM – speed control for right motor)

4. Connecting the Ultrasonic Sensor (HC-SR04)

• VCC → Arduino 5V

• GND → Arduino GND

• TRIG → Arduino pin 2

• ECHO → Arduino pin 3

👉 This sensor measures distance to obstacles in front of the car.


// Ultrasonic pins
int trigPin = 2;
int echoPin = 3;

// Motor driver pins
int ENA = 5;
int ENB = 6;
int IN1 = 8;
int IN2 = 9;
int IN3 = 10;
int IN4 = 11;

long duration;
int distance;

void setup() {
pinMode(trigPin, OUTPUT);
pinMode(echoPin, INPUT);

pinMode(ENA, OUTPUT);
pinMode(ENB, OUTPUT);
pinMode(IN1, OUTPUT);
pinMode(IN2, OUTPUT);
pinMode(IN3, OUTPUT);
pinMode(IN4, OUTPUT);

analogWrite(ENA, 150);
analogWrite(ENB, 150);
}

void loop() {
distance = getDistance();

if (distance > 20) {
moveForward();
} else {
stopCar();
delay(300);
turnRight();
delay(400);
}
}

int getDistance() {
digitalWrite(trigPin, LOW);
delayMicroseconds(2);
digitalWrite(trigPin, HIGH);
delayMicroseconds(10);
digitalWrite(trigPin, LOW);

duration = pulseIn(echoPin, HIGH);
return duration * 0.034 / 2;
}

void moveForward() {
digitalWrite(IN1, HIGH);
digitalWrite(IN2, LOW);
digitalWrite(IN3, HIGH);
digitalWrite(IN4, LOW);
}

void turnRight() {
digitalWrite(IN1, LOW);
digitalWrite(IN2, HIGH);
digitalWrite(IN3, HIGH);
digitalWrite(IN4, LOW);
}

void stopCar() {
digitalWrite(IN1, LOW);
digitalWrite(IN2, LOW);
digitalWrite(IN3, LOW);
digitalWrite(IN4, LOW);
}

• Robot not moving

  • Check motor power and battery

  • Ensure ENA/ENB jumpers removed

• Robot moves backward

  • Swap motor wires

• Robot hits obstacles

  • Increase distance threshold

• Motors run unevenly

  • Adjust PWM values

• How robots sense their environment

• How motor drivers control movement

• How algorithms guide robot behavior

• Basics of autonomous systems

• Teamwork, debugging, and engineering thinking

• Add LED eyes that react to obstacles

• Add a servo-mounted ultrasonic sensor

• Control speed based on distance

• Use WiFi to control robot remotely

• Build a maze and test navigation

AI Type: TinyML + Autonomous Intelligence

AI Extension 1: Smart Navigation AI

• Train AI to learn best turning directions

• Improves obstacle avoidance over time

• Introduces reinforcement learning concepts

AI Extension 2: Object Classification

• Use camera + AI

• Recognize walls, people, or boxes

• Decide different actions for each object

AI Extension 3: Behavior Learning Dashboard

• Send movement data via WiFi

• AI analyzes paths and mistakes

• Students visualize robot learning progress

👉 Younger learners focus on behavior; older learners train and analyze models.