Development of an Intelligent Picking System for Robotic Arms Based on Machine Vision

Intelligent manufacturing is gradually becoming an important development trend in the industrial field. Machine vision, as an artificial intelligence technology, is increasingly applied in the field of automation. This article will take the intelligent picking system for robotic arms based on machine vision as an example to explore the development and application of the system.

System Overview

The intelligent picking system for robotic arms based on machine vision mainly consists of four parts: the image acquisition module, the image processing module, the robotic arm control module, and the actuator module. The image acquisition module is responsible for capturing real-time images of the working scene, the image processing module performs pre-processing and feature extraction on the images, the robotic arm control module generates corresponding control strategies based on the results of image processing, and the actuator module is responsible for the precise movement of the robotic arm.

1、System Development Process

2、Requirement Analysis: Based on the actual application scenario, clarify the functions that the system needs to achieve, such as identifying target objects, precise picking, etc.

3、Hardware Selection: Choose the appropriate hardware equipment, including cameras, image processors, robotic arms, etc.

4、Image Processing Algorithm Design: Design appropriate image processing algorithms for the target objects in the requirement analysis, such as edge detection, morphological processing, feature extraction, etc.

5、Robotic Arm Control Strategy Design: Design the control strategy for the robotic arm based on the results of image processing, such as path planning, speed control, etc.

6、Software Development: Write the program code for image processing and robotic arm control to implement the core functions of the system.

Step 1: Image Acquisition
The first step in image processing is image acquisition. We can use cameras or other image sensors to obtain real-time images. In Python, the OpenCV library can be used for image acquisition. Here is a simple example of image acquisition code:

import cv2
# Create a VideoCapture object
cap = cv2.VideoCapture(0)
# Check if the camera is successfully opened
if not cap.isOpened():
    print("Failed to open camera")
    exit()
# Loop to get images
while True:
    # Read a frame
    ret, frame = cap.read()
    # If no image is read, exit the loop
    if not ret:
        print("Failed to read image")
        break
    # Display the image
    cv2.imshow('frame', frame)
    # Press 'q' to exit the loop
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
# Release resources and close the window
cap.release()
cv2.destroyAllWindows()

Step 2: Image Processing
After obtaining the image, we need to process the image to achieve the desired functions. For example, we can use image segmentation, feature extraction, and other methods to process the image. Here is a simple example of image processing code:

import cv2
import numpy as np
# Read the image
img = cv2.imread('image.jpg')
# Convert to a grayscale image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Apply a Gaussian filter for blurring
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# Apply the Canny edge detection algorithm
edges = cv2.Canny(blurred, 30, 150)
# Display the original image and the processed image
cv2.imshow('Original Image', img)
cv2.imshow('Edges', edges)
# Press 'q' to close the window
cv2.waitKey(0)
cv2.destroyAllWindows()

Robotic Arm Control Program Code Writing

Step 3: Obtain the Status of the Robotic Arm
When writing the control program for the robotic arm, the first thing you need to do is to obtain the status of the robotic arm. We can write an interface program to communicate with the robotic arm controller. Here is an example of obtaining the status of the robotic arm using serial communication:

#include <stdio.h>
#include <pthread.h>
// Define the structure of the robotic arm status
typedef struct {
    int joint_positions[6];
    int gripper_position;
} ArmStatus;
// Function declaration
void *get_arm_status(void *arg);
int main() {
    // Initialize the status of the robotic arm
    ArmStatus arm_status;
    // Create a thread to obtain the status of the robotic arm
    pthread_t thread;
    pthread_create(&thread, NULL, get_arm_status, &arm_status);
    // Thread synchronization
    pthread_mutex_t mutex = PTHREAD_MUTEX_INITIALIZER;
    pthread_mutex_lock(&mutex);
    // Loop to display the status of the robotic arm
    while (1) {
        printf("Joint 1: %d\n",
               arm_status.joint_positions[0]);
        // ... (Continue printing the status of other joints)
        // Delay
        sleep(1);
        // Unlock mutex
        pthread_mutex_unlock(&mutex);
    }
    // Wait for the thread to finish
    pthread_join(thread, NULL);
    return 0;
}

6. System Integration and Debugging: Integrate all modules together, perform system debugging to ensure that all modules work together and achieve the expected effect.
7. Application Fields of the System
8. Manufacturing Industry: The intelligent picking system can be applied to material handling and assembly on the production line, improving production efficiency and reducing labor costs.
9. Logistics Industry: In the sorting and handling of goods in warehouses and logistics centers, the intelligent picking system can improve sorting accuracy and handling efficiency.
10. Medical Field: In the fields of surgical robots and medical equipment, the intelligent picking system can achieve precise operations and improve surgical and treatment effects.
11. Agriculture: In the field of agricultural automation, the intelligent picking system can be applied to scenarios such as fruit picking and plant disease and pest control, improving agricultural production efficiency.
12. Summary

The intelligent picking system for robotic arms based on machine vision has a broad application prospect and provides an efficient, accurate, and intelligent solution for various industries. With the continuous advancement of technology, the system is expected to be applied in more fields in the future, helping to promote the development of intelligent manufacturing in China. At the same time, the system also faces certain challenges in practical applications, such as the real-time performance of image processing algorithms and the accuracy of robotic arm control, which require further research and optimization.