Robot Car for Competition: Speed and Line Following

Building a robot car for competition is one of the most rewarding STEM projects you can undertake. Whether you are preparing for a line-following race, a speed challenge, or an autonomous navigation event, the fundamentals remain the same: a well-designed chassis, properly matched motors, reliable sensor integration, and efficient code.

This guide covers everything you need to build a competition-ready robot car for competition events across India, from component selection to advanced tuning techniques that separate winning robots from the rest.

Types of Robot Car Competitions

Robot car events at Indian techfests generally fall into three categories:

Line Follower (Speed)

The robot follows a black line on a white surface as fast as possible. Tracks include curves of varying radii, intersections, T-junctions, and sometimes broken lines (dashes). This is the most common category at events from Technoxian to college-level techfests.

Obstacle-Avoiding Racer

The robot must navigate a course while detecting and avoiding obstacles using ultrasonic or IR sensors. Speed matters, but crashing into obstacles incurs heavy time penalties.

Drag Race

Pure speed on a straight track. The fastest robot wins. This is all about motor power, weight reduction, and traction. No sensors needed beyond a start trigger.

Chassis Design and Materials

The chassis is the skeleton of your robot car. Your choice of material and design directly affects performance:

Material Options

Design Principles

• Low centre of gravity: Mount batteries at the lowest point. This prevents tipping during sharp turns.
• Compact footprint: Shorter wheelbase = tighter turning radius. For line followers, this matters enormously on curves.
• Sensor overhang: Mount the sensor array ahead of the front axle for better prediction of upcoming curves.
• Weight distribution: Place heavier components (batteries, motors) towards the driven wheels for better traction.

Motor Selection and Gearing

Your motor choice is arguably the most critical decision for a competition robot car. Here is what to consider:

Motor Types for Competition

• N20 micro gear motors: Excellent for small, fast line followers. Available in gear ratios from 30:1 (high speed) to 1000:1 (high torque). A 100-300 RPM variant at 6V is ideal for most line followers.
• BO motors (yellow gear motors): The standard hobby motor. Cheap (₹60-80 each) but not competitive for serious events due to inconsistent speed between units and poor precision.
• Pololu micro metal gearmotors: Premium option. Consistent performance, encoder-ready, multiple ratio options. ₹400-700 each.

Sensor Array for Line Following

The number and arrangement of sensors directly determines how smoothly your robot tracks the line:

Sensor Configurations

• 3 sensors: Minimum viable. Centre sensor tracks the line; left and right detect deviations. Adequate for slow, simple tracks.
• 5 sensors: The sweet spot for most competitions. Provides enough resolution for smooth PID control on medium-speed runs.
• 7-8 sensors: Used by top competitors. Allows the robot to “see” further ahead on curves and handle intersections reliably.

Sensor Spacing

Space sensors so that at least 2-3 sensors cover the line width (typically 2 cm) at any time. For a 5-sensor array with 2 cm line, sensors spaced 8-10 mm apart in the centre with wider spacing on the edges works well.

Use QRD1114 or TCRT5000 reflective IR sensors. The TCRT5000 is more common in India and costs just ₹15-20 per unit. Alternatively, buy a pre-made 5-channel or 8-channel array module for ₹150-250.

PID Control Algorithm

PID control is what separates a wobbling robot from a smooth, fast one. Here is the implementation:

// PID Line Follower
float Kp = 25.0;   // Proportional gain
float Ki = 0.0;    // Integral gain (start at 0)
float Kd = 15.0;   // Derivative gain

int lastError = 0;
float integral = 0;
int baseSpeed = 180; // PWM base speed (0-255)

void loop() {
  int position = readSensorArray(); // Returns -3 to +3
  int error = position;

  integral += error;
  integral = constrain(integral, -50, 50); // Anti-windup

  int derivative = error - lastError;

  float correction = (Kp * error) + (Ki * integral) + (Kd * derivative);

  int leftSpeed = baseSpeed + correction;
  int rightSpeed = baseSpeed - correction;

  leftSpeed = constrain(leftSpeed, 0, 255);
  rightSpeed = constrain(rightSpeed, 0, 255);

  setMotorSpeed(leftSpeed, rightSpeed);
  lastError = error;
}

Tuning tips:

1. Start with only Kp. Increase until the robot oscillates back and forth over the line.
2. Reduce Kp by 20% and add Kd. Increase Kd until oscillation is damped.
3. Only add Ki if the robot consistently drifts to one side (which usually indicates a mechanical issue, not a control problem).
4. Tune at your target competition speed, not at slow speed. PID values change with speed.

Speed Optimisation Techniques

Once basic line following works, these techniques push performance further:

• Adaptive speed: Run faster on straights, slow down on curves. Detect curves by monitoring the rate of change of sensor error.
• Look-ahead: Use the outer sensors to predict upcoming curves and begin slowing down before the turn.
• Acceleration control: Ramp up speed gradually on straights rather than jumping to max speed. This prevents wheel spin.
• Motor calibration: No two motors are identical. Measure actual RPM of each motor and apply a compensation factor in code.

Power Management and Battery Choice

Power affects everything — motor speed, sensor accuracy, and runtime:

• Li-Po 7.4V 2S (1000-2000 mAh): The standard for competition robots. Light, powerful, and rechargeable. ₹400-800.
• Li-ion 18650 (2S configuration): Heavier than Li-Po but safer and more durable. Good for practice and qualifying rounds.
• AA batteries (6V pack): Only for beginners. Voltage drops under load cause inconsistent motor speed.

Important: Use a separate voltage regulator (5V buck converter) to power the Arduino and sensors from the main battery. This ensures stable logic voltage even when motors draw heavy current during acceleration.

Frequently Asked Questions