An Automated Robotic Arm: A Machine Learning Approach

TL;DR

This paper presents the design and implementation of an automated robotic arm utilizing machine learning for object detection and placement, aiming to enhance industrial efficiency and accuracy.

Contribution

It introduces a machine learning-based approach for robotic arm automation, focusing on object identification and movement using TensorFlow.

Findings

Improved accuracy in object detection and placement.

Enhanced efficiency in pick-and-place tasks.

Successful integration of machine learning with robotic control.

Abstract

The term robot generally refers to a machine that looks and works in a way similar to a human. The modern industry is rapidly shifting from manual control of systems to automation, in order to increase productivity and to deliver quality products. Computer-based systems, though feasible for improving quality and productivity, are inflexible to work with, and the cost of such systems is significantly high. This led to the swift adoption of automated systems to perform industrial tasks. One such task of industrial significance is of picking and placing objects from one place to another. The implementation of automation in pick and place tasks helps to improve efficiency of system and also the performance. In this paper, we propose to demonstrate the designing and working of an automated robotic arm with the Machine Learning approach. The work uses Machine Learning approach for object identification detection and traversal, which is adopted with Tensor flow package for better and accurate results.