Section outline

  • Project – Object Recognition Robot

    In this hands-on section, you will build a robot capable of recognizing everyday objects using a machine learning model. By combining computer vision and real-time inference, this project helps solidify your understanding of deploying AI on embedded hardware.

    • 🎯 Project Objective

      To create a mobile or stationary robot that captures images through a camera, classifies the objects in view using a trained ML model, and performs actions like lighting LEDs, moving motors, or sorting based on classification results.

      🧰 Required Components

      • Raspberry Pi (or Jetson Nano)
      • Pi Camera or USB webcam
      • Pre-trained TensorFlow Lite model (Teachable Machine or custom-trained)
      • GPIO-connected LEDs or servos for output actions
      • Optional: Motor driver module if mobility is required

    • 📸 Step 1: Setting Up the Camera

      Begin by setting up the Pi Camera or USB webcam. Ensure it is enabled in system settings and test it using Python:

      import cv2
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
cv2.imshow('Camera Feed', frame)

      Ensure the camera image is clear and the frame rate is acceptable for real-time use.

      📁 Step 2: Load the Trained Model

      Use a trained .tflite model that can classify a few specific objects — for example: apple, bottle, pencil, etc. Use TensorFlow Lite interpreter to load and prepare it:

      import tflite_runtime.interpreter as tflite
interpreter = tflite.Interpreter(model_path="object_model.tflite")
interpreter.allocate_tensors()

      🖼️ Step 3: Image Preprocessing

      Resize and normalize camera frames to match the model input requirements:

      resized = cv2.resize(frame, (224, 224))
input_data = np.expand_dims(resized, axis=0).astype(np.float32) / 255.0
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()

      📊 Step 4: Interpret Results

      Get prediction values and identify the most probable class:

      output_data = interpreter.get_tensor(output_details[0]['index'])
predicted_index = np.argmax(output_data)
object_labels = ["Apple", "Bottle", "Pencil"]
print("Detected:", object_labels[predicted_index])

      🤖 Step 5: Take Action Based on Prediction

      Now that your robot can recognize objects, trigger different outputs:

      • Turn on specific LEDs for different objects
      • Move a servo to drop the item into a specific bin
      • Display results on an LCD or log to a file

      Example: Sorting objects with servo rotation:

      if predicted_index == 0:
    servo.angle = 30  # Apple
elif predicted_index == 1:
    servo.angle = 90  # Bottle
else:
    servo.angle = 150 # Pencil

      🧪 Testing and Troubleshooting

      • Ensure lighting is consistent to avoid misclassification
      • Test with real-world objects that match your training images
      • Calibrate servo angles and GPIO outputs before final deployment

      ✅ Outcomes and Learnings

      By completing this project, you’ve successfully connected machine learning predictions to physical robotic actions. This end-to-end implementation demonstrates the real power of AI in robotics — perception, classification, and interaction. You’re now ready to take on more complex applications in smart automation and robotics intelligence.