Название: AI and IoT-Based Intelligent Automation in Robotics
Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Жанр: Программы
isbn: 9781119711223
isbn:
There are a few robots which perform specific moves based on the instructions given upon starting.
There are a few robots which only perform the tasks specified by one person. Whichever task is specified first by the instructor is identified by the robot as the task specified, which is stored in its memory and performed as the stored task. Such types of robots are called “task level autonomous.”
There are a few robots which do whatever task it is instructed to do by the user; such types of robots are called “fully autonomous” [13].
1.6 Conclusion
Robotics is a technology spreading throughout all industries because of its many advantages, including its ability to reduce man power, save money by reducing man power, complete tasks very effectively and quickly, prevent human mistakes, be more easily maintained, quickly respond in a more responsive manner; along with many other applications in fields where the robot performs, such as in multinational corporations (MNCs). Because of the automation process used for unit testing, integration testing, system testing and acceptance testing in MNCs being performed only by robots, many people are losing their jobs. Moreover, there are many applications where the robot performs or plays a major role in various areas, a few of which are industry, business, research, dynamics, kinematics, bionics, biometrics, quantum computing, education, training, career training, certification, summer robotics camp, robotics competition, employment, software industry, software projects testing, occupation safety and health implications and many more. Future development of robots or the robotic field is vast, and in a decade there is a chance that people will be replaced with robots for all tasks in every sector. This is because of the many advantages of robots which have already been adopted in a few sectors, with many more sectors ready to adopt the process. On one hand, this will lead to many good changes, but on the other hand many small jobs will be lost and unemployment will increase, etc.
References
1. Qin, T., Li, P., Shen, S., VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator. IEEE Trans. Rob., 34, 4, 1004–1020, Aug. 2018.
2. Pequito, S., Khorrami, F., Krishnamurthy, P., Pappas, G.J., Analysis and Design of Actuation–Sensing–Communication Interconnection Structures Toward Secured/Resilient LTI Closed-Loop Systems. IEEE Trans. Control Network Syst., 6, 2, 667–678, June 2019.
3. Chang, X. and Yang, G., New Results on Output Feedback $H_{\infty} $ Control for Linear Discrete-Time Systems. IEEE Trans. Autom. Control, 59, 5, 1355–1359, May 2014.
4. Li, Z., Zhang, T., Ma, C., Li, H., Li, X., Robust Passivity Control for 2-D Uncertain Markovian Jump Linear Discrete-Time Systems. IEEE Access, 5, 12176–12184, 2017.
5. Yang, C., Ge, S.S., Xiang, C., Chai, T., Lee, T.H., Output Feedback NN Control for Two Classes of Discrete-Time Systems with Unknown Control Directions in a Unified Approach. IEEE Trans. Neural Networks, 19, 11, 1873–1886, Nov. 2008.
6. Münz, U., Pfister, M., Wolfrum, P., Sensor and Actuator Placement for Linear Systems Based on Optimization. IEEE Trans. Autom. Control, 59, 11, 2984–2989, Nov. 2014.
7. Sui, S., Tong, S., Chen, C.L.P., Finite-Time Filter Decentralized Control for Nonstrict-Feedback Nonlinear Large-Scale Systems. IEEE Trans. Fuzzy Syst., 26, 6, 3289–3300, Dec. 2018.
8. Rakovic, S.V. and Baric, M., Parameterized Robust Control Invariant Sets for Linear Systems: Theoretical Advances and Computational Remarks. IEEE Trans. Autom. Control, 55, 7, 1599–1614, July 2010.
9. Li, Y., Sun, K., Tong, S., Adaptive Fuzzy Robust Fault-Tolerant Optimal Control for Nonlinear Large-Scale Systems. IEEE Trans. Fuzzy Syst., 26, 5, 2899–2914, Oct. 2018.
10. Zhang, H. and Feng, G., Stability Analysis and $H_{\infty}$ Controller Design of Discrete-Time Fuzzy Large-Scale Systems Based on Piecewise Lyapunov Functions. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics), 38, 5, 1390–1401, Oct. 2008.
11. Bakule, L., Rodellar, J., Rossell, J.M., Robust Overlapping Guaranteed Cost Control of Uncertain State-Delay Discrete-Time Systems. IEEE Trans. Autom. Control, 51, 12, 1943–1950, Dec. 2006.
12. Liu, Y. and Tong, S., Adaptive NN Tracking Control of Uncertain Nonlinear Discrete-Time Systems with Nonaffine Dead-Zone Input. IEEE Trans. Cybern., 45, 3, 497–505, March 2015.
13. Li, D. and Li, D., Adaptive Control via Neural Output Feedback for a Class of Nonlinear Discrete-Time Systems in a Nested Interconnected Form. IEEE Trans. Cybern., 48, 9, 2633–2642, Sept. 2018.
14. Alzenad, M., El-Keyi, A., Yanikomeroglu, H., 3D placement of an unmanned aerial vehicle base station for maximum coverage of users with different QoS requirements. IEEE Wirel. Commun. Lett., 7, 38–41, 2018.
*Corresponding author: [email protected]
2
Techniques in Robotics for Automation Using AI and IoT
Sandeep Kr. Sharma, N. Gayathri*, S. Rakesh Kumar and Rajiv Kumar Modanval
School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, India
Abstract
Gone are the days when people use manual methods to perform every task; now the world has evolved and we have advanced technologies like artificial intelligence (AI) and the internet of things (IoT) that have changed our world outlook. With the rapid advancement in technology, we are gifted with lots of modern technologies that are being integrated into our day-to-day lives, making it much easier.
In this chapter, we will discuss various techniques used for automation, like AI and the IoT, which form the basis for robotics. There’s a technique called robotic process automation (RPA) which is very popular nowadays, which can be used to automate any computational process. One software that is used to practice and build the RPA system is UiPath Studio, which comes in handy for all sorts of scripts and contains many tools that can be used to make automated bots. Apart from that, we will be discussing and proposing some other such techniques and studying the requirements for AI and IoT in the automation of robots.
Defining the roles and algorithms in integration with machine learning (ML), we will also be looking at some case studies and various other applications for automation in different scenarios. With the increase in the popularity of AI, the day is not very far off when we will have a replacement for humans—not only a replacement, but also a more advanced form of humans. Today, robots are so smart that they are capable of mimicking human behavior and are so efficient that it will take a normal human about 100 to 1000 times more time to complete the task. In this way, they are making our lives so easy and comfortable.
Keywords: Artificial intelligence (AI), internet of things (IoT), robotics, automation, robots, machine learning
2.1 Introduction
Technically the word automation СКАЧАТЬ