Robotic Arm Integrated With Object Recognition Using Vision-Based Opencv And CNN

This paper depicts about a smart image-recognition system for a robotic arm that combines OpenCV and Convolutional Neural Network (CNN). The objective of this paper is to move objects by using visual input and allow the robotic arm to identify and move objects using visual input. A camera captures real-time images of the environment and serves as the robot’s eye. Features are extracted by first processing these images using OpenCV, including tasks such as filtering, contour detection. A trained CNN model then analyses this processed information, classifying and recognizing objects accurately. With the object identified, the robotic arm takes the required action, such as picking or sorting items based on predefined instructions. The high precision and reliability achieved by the integration of OpenCV and CNN allow the system’s op eration even in varying lighting and background conditions, reducing the need for human intervention and increasing the efficiency of automated systems. The method proposed is applicable in many fields, from smart manufacturing and industrial automation to medical robotics and agriculture. The paper thus demonstrates how the combination of computer vision with CNN can make robotic arms more flexible, capable of performing complex tasks and intelligent in changing environments.