AI and IoT-Based Intelligent Automation in Robotics

Ashutosh Kumar Dubey received his PhD degree in Computer Science and Engineering from JK Lakshmipat University, Jaipur, Rajasthan, India. He is currently in the Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India. His research areas are data mining, optimization, machine learning, cloud computing, artificial intelligence, big data, IoT and object-oriented programming.Abhishek Kumar is a Doctorate in computer science from the University of Madras and more than 50 publications in reputed peer reviewed national and international journals, books & conferences. His research interests include artificial intelligence, image processing, computer vision, data mining, machine learning. S. Rakesh Kumar received his M.E. degree in Computer Science and Engineering from Anna University Chennai in 2016. His main research areas are big data analytics, network security and cloud computing.N. Gayathri received her B. Tech as well as M. Tech. degree in Computer Science and Engineering from Thiagarajar College of Engineering, Madurai, India. Her research interests include cloud computing, big data analytics and network security.Pasenjit Das PhD is an associate professor at Chitkara University, Himachal Pradesh, India. He has 15 + years’ experience in industry and academia and his research areas are data mining, machine learning and image processing.

• Preface xvii1 Introduction to Robotics 1Srinivas Kumar Palvadi, Pooja Dixit and Vishal Dutt1.1 Introduction 11.2 History and Evolution of Robots 31.3 Applications 61.4 Components Needed for a Robot 71.5 Robot Interaction and Navigation 101.5.1 Humanoid Robot 111.5.2 Control 111.5.3 Autonomy Levels 121.6 Conclusion 12References 132 Techniques in Robotics for Automation Using AI and IoT 15Sandeep Kr. Sharma, N. Gayathri, S. Rakesh Kumar and Rajiv Kumar Modanval2.1 Introduction 162.2 Brief History of Robotics 162.3 Some General Terms 172.4 Requirements of AI and IoT for Robotic Automation 202.5 Role of AI and IoT in Robotics 212.6 Diagrammatic Representations of Some Robotic Systems 232.7 Algorithms Used in Robotics 252.8 Application of Robotics 272.9 Case Studies 302.9.1 Sophia 302.9.2 ASIMO 302.9.3 Cheetah Robot 302.9.4 IBM Watson 312.10 Conclusion 31References 313 Robotics, AI and IoT in the Defense Sector 35Rajiv Kumar Modanval, S. Rakesh Kumar, N. Gayathri and Sandeep Kr. Sharma3.1 Introduction 363.2 How Robotics Plays an Important Role in the Defense Sector 363.3 Review of the World’s Current Robotics Capabilities in the Defense Sector 383.3.1 China 383.3.2 United State of America 393.3.3 Russia 403.3.4 India 413.4 Application Areas of Robotics in Warfare 433.4.1 Autonomous Drones 433.4.2 Autonomous Tanks and Vehicles 443.4.3 Autonomous Ships and Submarines 453.4.4 Humanoid Robot Soldiers 473.4.5 Armed Soldier Exoskeletons 483.5 Conclusion 503.6 Future Work 50References 504 Robotics, AI and IoT in Medical and Healthcare Applications 53Pooja Dixit, Manju Payal, Nidhi Goyal and Vishal Dutt4.1 Introduction 534.1.1 Basics of AI 534.1.1.1 AI in Healthcare 544.1.1.2 Current Trends of AI in Healthcare 554.1.1.3 Limits of AI in Healthcare 564.1.2 Basics of Robotics 574.1.2.1 Robotics for Healthcare 574.1.3 Basics of IoT 594.1.3.1 IoT Scenarios in Healthcare 604.1.3.2 Requirements of Security 614.2 AI, Robotics and IoT: A Logical Combination 624.2.1 Artificial Intelligence and IoT in Healthcare 624.2.2 AI and Robotics 634.2.2.1 Limitation of Robotics in Medical Healthcare 664.2.3 IoT with Robotics 664.2.3.1 Overview of IoMRT 674.2.3.2 Challenges of IoT Deployment 694.3 Essence of AI, IoT, and Robotics in Healthcare 704.4 Future Applications of Robotics, AI, and IoT 714.5 Conclusion 72References 725 Towards Analyzing Skill Transfer to Robots Based on Semantically Represented Activities of Humans 75Devi.T, N. Deepa, S. Rakesh Kumar, R. Ganesan and N. Gayathri5.1 Introduction 765.2 Related Work 775.3 Overview of Proposed System 785.3.1 Visual Data Retrieval 795.3.2 Data Processing to Attain User Objective 805.3.3 Knowledge Base 825.3.4 Robot Attaining User Goal 835.4 Results and Discussion 835.5 Conclusion 85References 856 Healthcare Robots Enabled with IoT and Artificial Intelligence for Elderly Patients 87S. Porkodi and D. Kesavaraja6.1 Introduction 886.1.1 Past, Present, and Future 886.1.2 Internet of Things 886.1.3 Artificial Intelligence 896.1.4 Using Robotics to Enhance Healthcare Services 896.2 Existing Robots in Healthcare 906.3 Challenges in Implementation and Providing Potential Solutions 906.4 Robotic Solutions for Problems Facing the Elderly in Society 986.4.1 Solutions for Physical and Functional Challenges 986.4.2 Solutions for Cognitive Challenges 986.5 Healthcare Management 996.5.1 Internet of Things for Data Acquisition 996.5.2 Robotics for Healthcare Assistance and Medication Management 1026.5.3 Robotics for Psychological Issues 1036.6 Conclusion and Future Directions 103References 1047 Robotics, AI, and the IoT in Defense Systems 109Manju Payal, Pooja Dixit, T.V.M. Sairam and Nidhi Goyal7.1 AI in Defense 1107.1.1 AI Terminology and Background 1107.1.2 Systematic Sensing Applications 1117.1.3 Overview of AI in Defense Systems 1127.2 Overview of IoT in Defense Systems 1147.2.1 Role of IoT in Defense 1167.2.2 Ministry of Defense Initiatives 1177.2.3 IoT Defense Policy Challenges 1177.3 Robotics in Defense 1187.3.1 Technical Challenges of Defense Robots 1207.4 AI, Robotics, and IoT in Defense: A Logical Mix in Context 1237.4.1 Combination of Robotics and IoT in Defense 1237.4.2 Combination of Robotics and AI in Defense 1247.5 Conclusion 126References 1278 Techniques of Robotics for Automation Using AI and the IoT 129Kapil Chauhan and Vishal Dutt8.1 Introduction 1308.2 Internet of Robotic Things Concept 1318.3 Definitions of Commonly Used Terms 1328.4 Procedures Used in Making a Robot 1338.4.1 Analyzing Tasks 1338.4.2 Designing Robots 1348.4.3 Computerized Reasoning 1348.4.4 Combining Ideas to Make a Robot 1348.4.5 Making a Robot 1348.4.6 Designing Interfaces with Different Frameworks or Robots 1348.5 IoRT Technologies 1358.6 Sensors and Actuators 1378.7 Component Selection and Designing Parts 1388.7.1 Robot and Controller Structure 1408.8 Process Automation 1418.8.1 Benefits of Process Automation 1418.8.2 Incorporating AI in Process Automation 1418.9 Robots and Robotic Automation 1428.10 Architecture of the Internet of Robotic Things 1428.10.1 Concepts of Open Architecture Platforms 1438.11 Basic Abilities 1438.11.1 Discernment Capacity 1438.11.2 Motion Capacity 1448.11.3 Manipulation Capacity 1448.12 More Elevated Level Capacities 1458.12.1 Decisional Self-Sufficiency 1458.12.2 Interaction Capacity 1458.12.3 Cognitive Capacity 1468.13 Conclusion 146References 1469 An Artificial Intelligence-Based Smart Task Responder: Android Robot for Human Instruction Using LSTM Technique 149T. Devi, N. Deepa, SP. Chokkalingam, N. Gayathri and S. Rakesh Kumar9.1 Introduction 1509.2 Literature Review 1529.3 Proposed System 1529.4 Results and Discussion 1579.5 Conclusion 161References 16210 AI, IoT and Robotics in the Medical and Healthcare Field 165V. Kavidha, N. Gayathri and S. Rakesh Kumar10.1 Introduction 16510.2 A Survey of Robots and AI Used in the Health Sector 16710.2.1 Surgical Robots 16710.2.2 Exoskeletons 16810.2.3 Prosthetics 17010.2.4 Artificial Organs 17110.2.5 Pharmacy and Hospital Automation Robots 17210.2.6 Social Robots 17310.2.7 Big Data Analytics 17510.3 Sociotechnical Considerations 17610.3.1 Sociotechnical Influence 17610.3.2 Social Valence 17710.3.3 The Paradox of Evidence-Based Reasoning 17810.4 Legal Considerations 18010.4.1 Liability for Robotics, AI and IoT 18010.4.2 Liability for Physicians Using Robotics, AI and IoT 18110.4.3 Liability for Institutions Using Robotics, AI and IoT 18210.5 Regulating Robotics, AI and IoT as Medical Devices 18310.6 Conclusion 185References 18511 Real-Time Mild and Moderate COVID-19 Human Body Temperature Detection Using Artificial Intelligence 189K. Logu, T. Devi, N. Deepa, S. Rakesh Kumar and N. Gayathri11.1 Introduction 19011.2 Contactless Temperature 19111.2.1 Bolometers (IR-Based) 19211.2.2 Thermopile Radiation Sensors (IR-Based) 19311.2.3 Fiber-Optic Pyrometers 19311.2.4 RGB Photocell 19411.2.5 3D Sensor 19511.3 Fever Detection Camera 19611.3.1 Facial Recognition 19711.3.2 Geometric Approach 19811.3.3 Holistic Approach 19811.3.4 Model-Based 19811.3.5 Vascular Network 19911.4 Simulation and Analysis 20011.5 Conclusion 203References 20312 Drones in Smart Cities 205Manju Payal, Pooja Dixit and Vishal Dutt12.1 Introduction 20612.1.1 Overview of the Literature 20612.2 Utilization of UAVs for Wireless Network 20912.2.1 Use Cases for WN Using UAVs 20912.2.2 Classifications and Types of UAVs 21012.2.3 Deployment of UAVS Using IoT Networks 21312.2.4 IoT and 5G Sensor Technologies for UAVs 21412.3 Introduced Framework 21712.3.1 Architecture of UAV IoT 21712.3.2 Ground Control Station 21812.3.3 Data Links 21812.4 UAV IoT Applications 22312.4.1 UAV Traffic Management 22312.4.2 Situation Awareness 22312.4.3 Public Safety/Saving Lives 22512.5 Conclusion 227References 22713 UAVs in Agriculture 229DeepanshuSrivastava, S. RakeshKumar and N. Gayathri13.1 Introduction 23013.2 UAVs in Smart Farming and Take-Off Panel 23013.2.1 Overview of Systems 23013.3 Introduction to UGV Systems and Planning 23413.4 UAV-Hyperspectral for Agriculture 23613.5 UAV-Based Multisensors for Precision Agriculture 23913.6 Automation in Agriculture 24213.7 Conclusion 245References 24514 Semi-Automated Parking System Using DSDV and RFID 247Mayank Agrawal, Abhishek Kumar Rawat, Archana, SandhyaKatiyar and Sanjay Kumar14.1 Introduction 24714.2 Ad Hoc Network 24814.2.1 Destination-Sequenced Distance Vector (DSDV) Routing Protocol 24814.3 Radio Frequency Identification (RFID) 24914.4 Problem Identification 25014.5 Survey of the Literature 25014.6 PANet Architecture 25114.6.1 Approach for Semi-Automated System Using DSDV 25214.6.2 Tables for Parking Available/Occupied 25314.6.3 Algorithm for Detecting the Empty Slots 25514.6.4 Pseudo Code 25514.7 Conclusion 256References 25615 Survey of Various Technologies Involved in Vehicle-to-Vehicle Communication 259Lisha Kamala K., Sini Anna Alex and Anita Kanavalli15.1 Introduction 25915.2 Survey of the Literature 26015.3 Brief Description of the Techniques 26215.3.1 ARM and Zigbee Technology 26215.3.2 VANET-Based Prototype 26215.3.2.1 Calculating Distance by Considering Parameters 26315.3.2.2 Calculating Speed by Considering Parameters 26315.3.3 Wi-Fi–Based Technology 26315.3.4 Li-Fi–Based Technique 26415.3.5 Real-Time Wireless System 26615.4 Various Technologies Involved in V2V Communication 26715.5 Results and Analysis 26715.6 Conclusion 268References 26816 Smart Wheelchair 271Mekala Ajay, Pusapally Srinivas and Lupthavisha Netam16.1 Background 27116.2 System Overview 27516.3 Health-Monitoring System Using IoT 27516.4 Driver Circuit of Wheelchair Interfaced with Amazon Alexa 27616.5 MATLAB Simulations 27716.5.1 Obstacle Detection 27716.5.2 Implementing Path Planning Algorithms 27816.5.3 Differential Drive Robot for Path Following 28016.6 Conclusion 28216.7 Future Work 282Acknowledgment 283References 28317 Defaulter List Using Facial Recognition 285Kavitha Esther, Akilindin S.H., Aswin S. and Anand P.17.1 Introduction 28617.2 System Analysis 28717.2.1 Problem Description 28717.2.2 Existing System 28717.2.3 Proposed System 28717.3 Implementation 28917.3.1 Image Pre-Processing 28917.3.2 Polygon Shape Family Pre-Processing 28917.3.3 Image Segmentation 28917.3.4 Threshold 28917.3.5 Edge Detection 29117.3.6 Region Growing Technique 29117.3.7 Background Subtraction 29117.3.8 Morphological Operations 29117.3.9 Object Detection 29217.4 Inputs and Outputs 29217.5 Conclusion 292References 29318 Visitor/Intruder Monitoring System Using Machine Learning 295G. Jenifa, S. Indu, C. Jeevitha and V. Kiruthika18.1 Introduction 29618.2 Machine Learning 29618.2.1 Machine Learning in Home Security 29718.3 System Design 29718.4 Haar-Cascade Classifier Algorithm 29818.4.1 Creating the Dataset 29818.4.2 Training the Model 29918.4.3 Recognizing the Face 29918.5 Components 29918.5.1 Raspberry Pi 29918.5.2 Web Camera 30018.6 Experimental Results 30018.7 Conclusion 302Acknowledgment 302References 30319 Comparison of Machine Learning Algorithms for Air Pollution Monitoring System 305Tushr Sethi and R. C. Thakur19.1 Introduction 30519.2 System Design 30619.3 Model Description and Architecture 30719.4 Dataset 30819.5 Models 31019.6 Line of Best Fit for the Dataset 31219.7 Feature Importance 31319.8 Comparisons 31519.9 Results 31819.10 Conclusion 318References 32120 A Novel Approach Towards Audio Watermarking Using FFT and CORDIC-Based QR Decomposition 323Ankit Kumar, Astha Singh, Shiv Prakash and Vrijendra Singh20.1 Introduction and Related Work 32420.2 Proposed Methodology 32620.2.1 Fast Fourier Transform 32820.2.2 CORDIC-Based QR Decomposition 32920.2.3 Concept of Cyclic Codes 33120.2.4 Concept of Arnold’s Cat Map 33120.3 Algorithm Design 33120.4 Experiment Results 33420.5 Conclusion 337References 33821 Performance of DC-Biased Optical Orthogonal Frequency Division Multiplexing in Visible Light Communication 339S. Ponmalar and Shiny J.J.21.1 Introduction 34021.2 System Model 34121.2.1 Transmitter Block 34121.2.2 Receiver Block 34221.3 Proposed Method 34221.3.1 Simulation Parameters for OptSim 34321.3.2 Block Diagram of DCO-OFDM in OptSim 34321.4 Results and Discussion 34421.5 Conclusion 352References 35322 Microcontroller-Based Variable Rate Syringe Pump for Microfluidic Application 355G. B. Tejashree, S. Swarnalatha, S. Pavithra, M. C. Jobin Christ and N. Ashwin Kumar22.1 Introduction 35622.2 Related Work 35722.3 Methodology 35822.3.1 Hardware Design 35922.3.2 Hardware Interface with Software 36022.3.3 Programming and Debugging 36122.4 Result 36222.5 Inference 36322.5.1 Viscosity (η) 36522.5.2 Time Taken 36522.5.3 Syringe Diameter 36622.5.4 Deviation 36622.6 Conclusion and Future Works 366References 36823 Analysis of Emotion in Speech Signal Processing and Rejection of Noise Using HMM 371S. Balasubramanian23.1 Introduction 37223.2 Existing Method 37323.3 Proposed Method 37423.3.1 Proposed Module Description 37523.3.2 MFCC 37623.3.3 Hidden Markov Models 37923.4 Conclusion 382References 38324 Securing Cloud Data by Using Blend Cryptography with AWS Services 385Vanchhana Srivastava, Rohit Kumar Pathak and Arun Kumar24.1 Introduction 38524.1.1 AWS 38724.1.2 Quantum Cryptography 38824.1.3 ECDSA 38924.2 Background 38924.3 Proposed Technique 39224.3.1 How the System Works 39324.4 Results 39424.5 Conclusion 396References 396Index 399