An Automated Robotic Arm: A Machine Learning Approach
This work addresses the need for flexible and cost-effective automation in industrial settings, though it appears incremental as it applies existing machine learning methods to a common robotic task.
The paper tackles the problem of automating pick-and-place tasks in industry by designing a robotic arm that uses machine learning for object identification and traversal, achieving improved efficiency and performance.
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.