🚀 WERA Sharp IR Library (ESP32)

Arduino library for reading the Sharp IR distance sensor GP2Y0A21YK0F using ESP32, with data-driven regression models (Power & Inverse) and median filtering for stable and accurate distance measurement.

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✨ Features

• 📏 Distance output in centimeters (cm)
• 📊 Power Regression Model (best fit from experimental data)
• 📈 Inverse Regression Model (matches IR sensor characteristics)
• 🔇 Median filtering (25 samples) for noise reduction
• ⚡ Optimized for ESP32 (12-bit ADC + attenuation)
• 🎯 High accuracy (R² ≈ 0.9997)

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📌 Sensor Used

This library is specifically designed and calibrated for:

Sharp GP2Y0A21YK0F Infrared Distance Sensor

Sensor Characteristics:

• Measurement range: 10 cm – 80 cm
• Output: Analog voltage (non-linear)
• Highly sensitive to noise and surface reflectivity
• Requires calibration for accurate distance estimation

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📦 Installation

Manual Installation

git clone https://github.com/username/WERA_SharpIR.git

Move the folder to:

Documents/Arduino/libraries/

Restart Arduino IDE.

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🧪 Usage Example

#include <WERA_SharpIR.h>

WERASharpIR sensor(35);

void setup() {
  Serial.begin(115200);
}

void loop() {
  float d_power   = sensor.readCM();    // Power regression
  float d_inverse = sensor.readCMv2();  // Inverse regression

  Serial.print("Power: ");
  Serial.print(d_power);

  Serial.print(" | Inverse: ");
  Serial.println(d_inverse);

  delay(500);
}

📌 See the examples/ folder for:

• Power regression usage
• Inverse regression usage
• Mode switching via Serial

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📊 Regression Models

🔹 Power Regression

ADC = 22581.6 × distance^(-0.9184)

Inverse form:

distance = (ADC / 22581.6)^(1 / -0.9184)

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🔹 Inverse Regression

ADC = 29103.3 / (distance + 0.89) + 49.57

Inverse form:

distance = 29103.3 / (ADC - 49.57) - 0.89

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📈 Performance

Model	MAE	RMSE	MAPE
Power	~0.0745	~0.0745	~0.31%
Inverse	~0.1536	~0.1536	~0.64%

📌 Both models achieve very high accuracy (R² ≈ 0.9997) 📌 The Power model shows lower error on the experimental dataset

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⚙️ Hardware

• ESP32
• Sharp GP2Y0A21YK0F
• Sensor supply: 5V
• ADC resolution: 12-bit (0–4095)

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🧠 How It Works

1. Reads analog voltage from the IR sensor (ADC)
2. Applies median filtering (25 samples)
3. Converts ADC value into distance using regression models
4. Clamps output to valid range (10–80 cm)

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⚠️ Notes

• Measurements below 10 cm and above 80 cm are less reliable

• Sensor output is non-linear → regression is required

• Noise may occur due to power supply or environment

• Choose model based on use case:

  • Power → best experimental accuracy
  • Inverse → physically representative model

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🔥 Use Cases

• 🤖 Robotics navigation (WERA)
• 🚧 Obstacle detection
• 🧠 Smart sensing systems
• 📡 IoT monitoring

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📸 Preview

Add regression graph or hardware setup image:

docs/graph.png

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👨‍💻 Author

Ramdesh Gustiar Electrical Engineering Student Project: WERA (Whiteboard Eraser Automation)

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📜 License

MIT License — free to use and modify.

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⭐ Support

If this project helps you:

• ⭐ Star this repository
• 🍴 Fork it
• 🐛 Open an issue for bugs or suggestions

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🚀 Future Development

• Auto calibration
• Multi-sensor support
• Adaptive filtering
• IoT integration (Firebase)

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