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Robotics and their Association for the Advancement of Automation
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Journal of Computer Science & Systems Biology

ISSN: 0974-7230

Open Access

Perspective - (2022) Volume 15, Issue 3

Robotics and their Association for the Advancement of Automation

Elisha Stewart*
*Correspondence: Elisha Stewart, Department of Information Science, University of Bergen, Norway, Email:
Department of Information Science, University of Bergen, Norway

Received: 07-Mar-2022, Manuscript No. Jcsb-22-62576; Editor assigned: 09-Mar-2022, Pre QC No. P-62576; Reviewed: 23-Mar-2022, QC No. 62576; Revised: 28-Mar-2022, Manuscript No. R-62576; Published: 04-Apr-2022 , DOI: 10.37421/jcsb.2022.15.403
Citation: Stewart, Elisha. “Robotics and their Association for the Advancement of Automation.” J Comput Sci Syst Biol 15 (2022): 402.
Copyright: © 2022 Stewart E. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction

Robotics is an engineering discipline that deals with the conception, design, manufacture, and operation of robots. The goal of robotics is to develop intelligent machines that can assist humans in a variety of ways. Robotics can take many different forms. A robot can be human-like or take the form of a robotic application, such as Robotic Process Automation (RPA), which mimics how humans interact with software to perform repetitive, rules-based tasks. While the field of robotics and exploration of the potential uses and functionality of robots has grown significantly in the twentieth century, the concept is not new. Industrial robots, like many other types of robots, are now used to perform repetitive tasks. They could be in the form of a robotic arm, an exoskeleton, or traditional humanoid robots [1-3]. Manufacturers and warehouses such as Amazon, Devol, Best Buy, and others use industrial robots and robot arms. To function, a robot or robotic system relies on a combination of computer programming and algorithms, a remotely controlled manipulator, actuators, control systems action, processing, and perception real time sensors, and an element of automation.

Robot learning is a field that combines machine learning and robotics. Robot learning is the study of techniques that allow a robot to learn new information or skills using machine learning algorithms. Robot learning has been used for a variety of applications, including object grasping, object categorization, and even linguistic interaction with a human peer. Learning can take place through self-exploration or with the assistance of a human operator.

Intelligent robots must accumulate facts through human input or sensors in order to learn. The processing unit of the robot will then compare the newly acquired data to previously stored information and predict the best course of action based on the data it has acquired. However, it is critical to understand that a robot can only solve problems for which it was designed. It lacks general analytical abilities.

Description

Robotic systems are coveted in many industries because they can improve accuracy, lower costs, and increase human safety. Indeed, one of the most significant advantages of robotics is that it eliminates the need for humans in many dangerous or unhealthy environments. Examples include the nuclear industry, space exploration, defence, and maintenance, among others. Workers can avoid hazardous chemical exposure and even limit psychosocial and ergonomic health risks by using robots or robotic systems. However, despite these advantages, there are a number of disadvantages to robotics [4,5]. Certain tasks are simply better suited to humans, such as those that require creativity, adaptability, and critical decision-making abilities. All robots are made of some kind of mechanical structure. A robot's mechanical aspect aids it in completing tasks in the environment for which it was designed. For example, the Mars 2020 Rover's wheels are individually motorised and made of titanium tubing, allowing it to firmly grip the red planet's harsh terrain.

Conclusion

Electrical components are required for robots to control and power the machinery. Essentially, an electric current (such as a battery) is required to power the vast majority of robots. At least some level of computer programming is present in robots. A robot would be nothing more than a piece of simple machinery if it did not have a set of instructions telling it what to do. Inserting a programme into a robot gives it the ability to understand when and how to perform a task. As artificial intelligence and software continue to advance, we're bound to see the promise of the robotics industry sooner rather than later. Robots will become smarter, more flexible, and energy efficient in the near future as these technologies advance. They will also remain a key focal point in smart factories, where they will take on more difficult challenges and contribute to the security of global supply chains.

References

  1. Silvério, Joao, Sylvain Calinon and Leonel Rozo, et al. "Learning task priorities from demonstrations." IEEE Trans Robot 35 (2018): 78-94.
  2. Google Scholar, Crossref, Indexed at

  3. Liu, Li, Wanli Ouyang and Xiaogang Wang, et al. "Deep learning for generic object detection: A survey." Int J Comput Vis 128 (2020): 261-318.
  4. Google Scholar, Crossref, Indexed at

  5. Auger, François, Mickael Hilairet and Josep M. Guerrero,et. "Industrial applications of the Kalman filter: A review." IEEE Trans Ind Electron 60 (2013): 5458-5471.
  6. Google Scholar, Crossref, Indexed at

  7. Khoramshahi, Mahdi and Aude Billard. "A dynamical system approach to task-adaptation in physical human–robot interaction." Auton Robot 43 (2019): 927-946.
  8. Google Scholar, Crossref, Indexed at

  9. Kunze, Lars and Michael Beetz. "Envisioning the qualitative effects of robot manipulation actions using simulation-based projections." Artif Intell 247 (2017): 352-380.
  10. Google Scholar, Crossref, Indexed at

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