Logo

AI & Automation Hub

Robotics 15 min read

Integrating Vision Models with Robotics

Sarah Chen avatar

Contributotor

Integrating Vision Models with Robotics
Featured image for Integrating Vision Models with Robotics

A technical deep dive into using OpenCV and YOLOv8 to control robotic arms for sorting tasks.

Bridging Vision and Action

Computer vision and robotics are a powerful combination. This guide demonstrates how to use YOLOv8 for object detection and integrate it with a robotic arm for automated sorting.

Hardware Setup

You’ll need:

  • A robotic arm (we’re using a 6-DOF arm)
  • A webcam or camera module
  • A computer with GPU (RTX 3060 or better)

Setting Up YOLOv8

Install the ultralytics package:

Code 
pip install ultralytics opencv-python numpy pyserial

Load and configure the model:

Code 
from ultralytics import YOLO
import cv2

# Load YOLOv8
model = YOLO('yolov8n.pt')

# Initialize camera
cap = cv2.VideoCapture(0)

Object Detection Loop

Create a real-time detection system:

Code 
def detect_objects():
    ret, frame = cap.read()
    if not ret:
        return None

    # Run YOLOv8 inference
    results = model(frame, conf=0.5)

    # Extract bounding boxes and classes
    detections = []
    for r in results:
        boxes = r.boxes
        for box in boxes:
            x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
            confidence = box.conf[0].cpu().numpy()
            class_id = int(box.cls[0].cpu().numpy())

            detections.append({
                'bbox': (x1, y1, x2, y2),
                'confidence': confidence,
                'class': model.names[class_id]
            })

    return detections, frame

Robotic Arm Control

Interface with the robotic arm:

Code 
import serial
import time

class RoboticArm:
    def __init__(self, port='/dev/ttyUSB0', baudrate=115200):
        self.serial = serial.Serial(port, baudrate, timeout=1)
        time.sleep(2)  # Wait for connection

    def move_to(self, x, y, z):
        """Move arm to specified coordinates"""
        command = f"G1 X{x} Y{y} Z{z}\n"
        self.serial.write(command.encode())
        time.sleep(0.5)

    def pick_object(self):
        """Activate gripper"""
        self.serial.write(b"M3 S255\n")
        time.sleep(1)

    def release_object(self):
        """Release gripper"""
        self.serial.write(b"M3 S0\n")
        time.sleep(1)

Coordinate Transformation

Convert camera coordinates to robot coordinates:

Code 
def camera_to_robot(bbox, camera_matrix):
    """
    Transform 2D bounding box to 3D robot coordinates
    """
    # Get center of bounding box
    x_center = (bbox[0] + bbox[2]) / 2
    y_center = (bbox[1] + bbox[3]) / 2

    # Apply camera calibration matrix
    # This is simplified - in practice, you'd use cv2.calibrateCamera
    robot_x = (x_center - camera_matrix['cx']) * camera_matrix['scale']
    robot_y = (y_center - camera_matrix['cy']) * camera_matrix['scale']
    robot_z = camera_matrix['base_height']

    return robot_x, robot_y, robot_z

Sorting Logic

Implement the sorting behavior:

Code 
def sort_objects():
    arm = RoboticArm()

    # Define sorting positions for different object types
    sort_positions = {
        'apple': (100, 50, 20),
        'orange': (100, -50, 20),
        'banana': (150, 0, 20)
    }

    while True:
        detections, frame = detect_objects()

        if not detections:
            continue

        for detection in detections:
            obj_class = detection['class']

            if obj_class in sort_positions:
                # Get object position
                x, y, z = camera_to_robot(
                    detection['bbox'],
                    CAMERA_MATRIX
                )

                # Pick object
                arm.move_to(x, y, z + 50)  # Approach
                arm.move_to(x, y, z)       # Lower
                arm.pick_object()           # Grab
                arm.move_to(x, y, z + 50)  # Lift

                # Move to sorting bin
                sort_x, sort_y, sort_z = sort_positions[obj_class]
                arm.move_to(sort_x, sort_y, sort_z + 50)
                arm.move_to(sort_x, sort_y, sort_z)
                arm.release_object()

                # Return to home position
                arm.move_to(0, 0, 100)

        # Display detection results
        cv2.imshow('Detection', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

Safety Considerations

  1. Emergency Stop: Always have a physical e-stop button
  2. Workspace Bounds: Implement software limits to prevent collisions
  3. Error Handling: Catch and handle communication errors gracefully

Conclusion

Combining modern computer vision with robotics opens up endless possibilities. This pipeline can be adapted for various tasks from manufacturing to agriculture.

#robotics #computer vision #YOLOv8 #OpenCV

Related Articles

More articles coming soon...

Discussion (14)

Sarah J Sarah Jenkins

Great article! The explanation of the attention mechanism was particularly clear. Could you elaborate more on how sparse attention differs in implementation?

Sarah Chen Sarah Chen Author

Thanks Sarah! Sparse attention essentially limits the number of tokens each token attends to, often using a sliding window or fixed patterns. I'll be covering this in Part 2 next week.

Dev Guru Dev Guru

The code snippet for the attention mechanism is super helpful. It really demystifies the math behind it.