CODE FOR ECO BOT

Arduino Code (For Navigation and Robotic Arm Control)
This Arduino code handles movement, obstacle avoidance, and the robotic arm for picking up objects. The AI-based vision will be processed on a connected system like a Raspberry Pi.

Hardware Assumptions
Motor Driver: L298N for controlling motors.
Ultrasonic Sensor: HC-SR04 for obstacle detection.
Robotic Arm: Servos for gripping and lifting.
#include <Servo.h>

// Motor driver pins
const int motorLeftForward = 5;
const int motorLeftBackward = 6;
const int motorRightForward = 9;
const int motorRightBackward = 10;

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

// Servo pins
Servo gripperServo;
Servo armServo;

// Ultrasonic sensor variables
long duration;
int distance;

// Function to measure distance
int getDistance() {
  digitalWrite(trigPin, LOW);
  delayMicroseconds(2);
  digitalWrite(trigPin, HIGH);
  delayMicroseconds(10);
  digitalWrite(trigPin, LOW);
  duration = pulseIn(echoPin, HIGH);
  return duration * 0.034 / 2; // Convert to cm
}

void setup() {
  // Initialize motor pins
  pinMode(motorLeftForward, OUTPUT);
  pinMode(motorLeftBackward, OUTPUT);
  pinMode(motorRightForward, OUTPUT);
  pinMode(motorRightBackward, OUTPUT);

  // Initialize ultrasonic sensor pins
  pinMode(trigPin, OUTPUT);
  pinMode(echoPin, INPUT);

  // Initialize servos
  gripperServo.attach(11);
  armServo.attach(12);
  gripperServo.write(90); // Initialize to open position
  armServo.write(0);      // Arm in resting position

  Serial.begin(9600); // Start serial communication
}

void loop() {
  distance = getDistance();
  Serial.println(distance);

  // Obstacle avoidance
  if (distance > 10 || distance == 0) { // If no obstacle, move forward
    digitalWrite(motorLeftForward, HIGH);
    digitalWrite(motorLeftBackward, LOW);
    digitalWrite(motorRightForward, HIGH);
    digitalWrite(motorRightBackward, LOW);
  } else { // Stop and pick up object
    digitalWrite(motorLeftForward, LOW);
    digitalWrite(motorRightForward, LOW);

    // Simulate picking up an object
    pickUpObject();
  }
}

void pickUpObject() {
  // Lower the arm
  armServo.write(90);
  delay(500);

  // Close the gripper
  gripperServo.write(0);
  delay(500);

  // Lift the arm
  armServo.write(0);
  delay(500);

  // Move backward briefly
  digitalWrite(motorLeftBackward, HIGH);
  digitalWrite(motorRightBackward, HIGH);
  delay(1000);

  // Stop moving
  digitalWrite(motorLeftBackward, LOW);
  digitalWrite(motorRightBackward, LOW);
}

ython Code (For AI-Based Plastic Detection)
This Python script runs on a Raspberry Pi, processes the video feed using OpenCV, and sends commands to the Arduino via Serial.

Requirements
Python libraries: OpenCV, NumPy, PySerial.
Pretrained model for detecting plastic waste.
python (cuz i know python best but i will rty js sorry about js if there are any errors )

import cv2
import serial
import time

# Initialize serial communication with Arduino
arduino = serial.Serial('COM3', 9600)  # Replace 'COM3' with your port
time.sleep(2)

# Load pre-trained model (e.g., YOLO or TensorFlow model)
# Replace with your trained model for plastic detection
model_path = 'model.pb'
net = cv2.dnn.readNet(model_path)

# Initialize webcam
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Preprocess frame for model
    blob = cv2.dnn.blobFromImage(frame, 1/255.0, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)
    detections = net.forward()

    # Analyze detections
    detected_plastic = False
    for detection in detections:
        # Example detection logic (use your model's output format)
        if detection[0] == "plastic":  # Replace with your class label
            detected_plastic = True
            break

    # Send commands to Arduino
    if detected_plastic:
        arduino.write(b'P')  # Command for picking up plastic
        print("Plastic detected!")
    else:
        arduino.write(b'N')  # No plastic detected
        print("No plastic detected.")

    # Display frame
    cv2.imshow("EcoBot Camera", frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()
arduino.close()
WARNNING I AM TRYING AI IMPEMENTATION IN JS COD CAN BE FALTY !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!)
const videoElement = document.getElementById("webcam");
const canvasElement = document.getElementById("outputCanvas");
const canvasCtx = canvasElement.getContext("2d");
let model;

// Initialize Serial Communication with Arduino (Using Web Serial API)
let port;
async function connectToArduino() {
  if ("serial" in navigator) {
    try {
      port = await navigator.serial.requestPort();
      await port.open({ baudRate: 9600 });
      console.log("Connected to Arduino.");
    } catch (error) {
      console.error("Failed to connect to Arduino:", error);
    }
  } else {
    console.error("Web Serial API is not supported in this browser.");
  }
}

// Load the COCO-SSD Model
async function loadModel() {
  model = await cocoSsd.load();
  console.log("Model loaded!");
}

// Start Webcam Stream
async function startWebcam() {
  const stream = await navigator.mediaDevices.getUserMedia({ video: true });
  videoElement.srcObject = stream;

  videoElement.onloadedmetadata = () => {
    videoElement.play();
    detectPlastic();
  };
}

// Plastic Detection
async function detectPlastic() {
  if (model && videoElement.readyState === 4) {
    const predictions = await model.detect(videoElement);

    // Clear canvas
    canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
    canvasCtx.drawImage(videoElement, 0, 0, canvasElement.width, canvasElement.height);

    let detectedPlastic = false;

    predictions.forEach((prediction) => {
      if (prediction.class === "bottle" || prediction.class === "plastic bag") {
        detectedPlastic = true;

        // Draw bounding box
        canvasCtx.strokeStyle = "green";
        canvasCtx.lineWidth = 4;
        canvasCtx.strokeRect(
          prediction.bbox[0],
          prediction.bbox[1],
          prediction.bbox[2],
          prediction.bbox[3]
        );

        // Draw label
        canvasCtx.font = "18px Arial";
        canvasCtx.fillStyle = "green";
        canvasCtx.fillText(
          `${prediction.class} (${Math.round(prediction.score * 100)}%)`,
          prediction.bbox[0],
          prediction.bbox[1] - 10
        );
      }
    });

    // Send commands to Arduino
    if (port && port.writable) {
      const writer = port.writable.getWriter();
      if (detectedPlastic) {
        await writer.write(new TextEncoder().encode("P")); // Command for "Plastic Detected"
        console.log("Plastic detected: Command 'P' sent.");
      } else {
        await writer.write(new TextEncoder().encode("N")); // Command for "No Plastic Detected"
        console.log("No plastic detected: Command 'N' sent.");
      }
      writer.releaseLock();
    }
  }

  requestAnimationFrame(detectPlastic); // Continue detecting
}

// Initialize Everything
(async () => {
  await connectToArduino(); // Connect to Arduino
  await loadModel();        // Load AI model
  startWebcam();            // Start webcam stream
})();