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Top1. Introduction
Present scenario of industry is that robots are more in demand than actual workers and the reason behind this is simple, more accurate, more skills, better precise work, different versions and variety of bots for specifically different work. Repetitive work is better done with robots, for the robot the sensors work as common sense for the machine to process image to detect and identify an object and its characteristic which helps to perform a required task. In image processing an image (photograph or video frame) is taken as an input and output can be an image or anything related to it. It has many applications such as pick and place robot, fingerprint matching etc. (Juang, 2015).
Automation is increasing and affecting technology every year. Machine vision is considered to be a part of future technology in which much more scope is to be found. As time passes automation and robotics are more getting more and more involved in industries thereby increasing their growth in their respective fields. Today in different industries, robots with complicated sensors are used because they give output with high accuracy and precision. Implementation of different sensors boosted the use of robotics in the industries. They enhanced the robots and made them more capable. Sensors with vision capability gave eyesight to robots which is in fact a remarkable discovery. A robot controller extracts object information and the simulation program to control how the robotic arm moves, approaches and handles the object. Because of machine vision the bot receives a certain flexibility to detect object and manipulate it or any other surrounding object. Reliability and cost reduction are two main tasks where automation is concerned for any industry. The basic focus is on the details of a pick and place robot. Accurate positioning is an essential factor to be considered when making a pick and place robot for product quality and to achieve higher product rate.
Robotic arms are basically used for complicated tasks such as lifting heavy objects that cannot be lifted by a normal worker that too with high concentration and accuracy. This paper mainly focuses on how the process of the arm works while it handles the object with machine vision attached to it. The project overall facilitates object detection to a limit. A user interface is used with the project for human feed on the target destination. This project can be treated as an object handling system with machine vision. The basic design of the project consists of an arm which is able to pick and place objects in real time and the machine vision is used to identify them as to what is the classified name of the object being placed. A webcam placed on top is used for machine vision to identify object in real time. If looked by an industrial point of view the industrial use of the project eliminates work done by human, gives precision with more accuracy than given by a normal worker. This also increases speed in the industry where amount of work done is concerned. Although it positively increases many factors when concerned to any industry but it has also resulted in a negative aspect which is the reduction of human labor. Robotic arms have taken over majority of the work previously done by the human labor and it will keep it on with continuous new technologies or sensors being integrated with the arm to take it capable of doing something new. As far as work done for precision and accuracy is concerned in any industrial workspace robotic arm will be given first priority (Gupta, 2018). There has been a sustainable development of the industries. The impact of industry 4.0 adoption with practice view and capability of dynamic view are used to conceptualize the adoption on 10R advancement of capabilities with sustainable development (Bag, 2020). There is also a large amount of data theoretically available, due to these robotic arms, the things have fastened and the data is being collected more rapidly for which the concept of Big data analysis comes in and this can be used to enhance sustainable supply chain performance of the data available (Bag, 2019).