Design and Optimization of Mobile Robotics for Industry 5.0

Rathishchandra R. Gatti, PhD is the Dean of Research and Development and professor in the Departments of Mechanical Engineering and Robotics and Automation at the Sahyadri College of Engineering and Management with over 20 years of experience. He has published four patents, seven books, and more than 40 peer-reviewed publications. His research interests include robotics, AI in engineering, physical AI, and machine data analytics. Chandra Singh is an assistant professor in the Department of Electronics and Communication Engineering at Nitte University. He has published more than 35 articles and six patents, and edited ten books. His research interests include optical networking and communication, wireless communication, Internet of Things, and machine learning. Ajith B.S., PhD is an associate professor in the Department of Mechanical and Robotics Engineering and Associate Dean of Intellectual Property Rights at the Sahyadri College of Engineering and Management. He has published more than 18 journal papers, presented ten conference papers, authored many patents, and contributed to a number of books. His research focuses on biofuels, renewable energy, combustion, and heat transfer. E. Fantin Irudaya Raj, PhD is a professor of Electrical and Electronics Engineering at the Aditanar College of Engineering with over a decade of experience. He has published more than 35 journal publications, 50 conference papers, patents, and book contributions. His research interests include power electronic drives, Internet of Things, smart cities, image processing, and AI techniques.

• Preface xviiPart 1: Foundations of Industry 5.0 and Emerging Technologies 11 Advancing Design Principles for Industry 5.0 with a Focus on Human-Centered Innovation 3Dankan Gowda V., Algubelly Yashwanth Reddy, V. Nuthan Prasad, Ved Srinivas and K.D.V. Prasad1.1 Introduction 41.2 Literature Survey 61.3 Core Principles of Human-Centered Design 81.4 Technological Advancements Enabling Human-Centered Innovation 101.5 Methodologies for Implementing Human-Centered Innovation 131.6 Challenges and Barriers to Adoption 151.7 Results and Discussion 171.8 Future Directions for Research and Practice 211.9 Conclusion 21References 222 Methods and Mechanics for Robot Navigation in Different Environments 25Canute Sherwin, Chandra Singh and Prashanth Kumar2.1 Introduction 262.2 Path Planning 292.3 Mobile Robot Navigation Mapping 302.3.1 Visual Mapping and Positioning 302.3.2 LiDAR Mapping and Positioning 312.3.3 Sensor Fusion Mapping and Positioning 312.4 Machine Learning 322.5 Large Language Models (LLMs) 332.5.1 Robot’s Environment Perception 342.5.2 High Level Planning 342.5.3 Low Level Planning 352.5.4 Human–Robot Interaction 362.5.5 Multi-Robot Coordination 362.6 Deep Learning Approaches 372.7 Reinforcement Learning (RL) 392.8 Conclusions 40References 413 Detailed Investigation of Autonomous Vehicles in the Context of Industry 5.0 47C. Sweetline Jenita, E. Fantin Irudaya Raj, S. Sivananaithaperumal and N. Pon Subathira3.1 Introduction 483.2 Self-Driving Systems – Overview 503.3 Sensors in Autonomous Vehicle 533.3.1 Camera 553.3.2 LiDAR 553.3.3 Radar 563.4 Actuators 573.5 Decision-Making Algorithms and Controllers in Self-Driving Systems 593.6 Conclusion 61References 624 Emerging Technologies in Industrial Automation with Robotic Applications 67M. Appadurai, E. Fantin Irudaya Raj, M. Chithambara Thanu and P. Gayathri4.1 Introduction 684.2 Robotics in Additive Manufacturing 684.3 Robotic Welding Systems 714.4 Digital Twins for Robotic System Optimization 734.5 Robotics in Hazardous Environments 754.5.1 Robotics in Nuclear Environments 754.5.2 Robotics in Space Exploration 764.5.3 Robotics in Deep Sea Exploration 764.5.4 Robotics in Disaster Response 774.6 Robotic Maintenance Systems for Predictive Analytics 774.7 Mobile Robotics in Dynamic Industrial Environments 804.8 Conclusion 82References 82Part 2: Robotics and Mobile Integration in Industry 5.0 875 IoT and Mobile Robotics Integration for Transforming Smart Manufacturing in Industry 5.0 89Dankan Gowda V., Priya Dongare-Jadhav, Noushad Yashan, Madan Mohanrao Jagtap and Suganthi Neelagiri5.1 Introduction 905.1.1 Context and Motivation 905.1.2 Role of IoT and Mobile Robotics 925.1.3 Objectives of the Chapter 925.2 Industry 5.0: A Paradigm Shift 935.2.1 Industry 5.0 Vs. Industry 4.0 935.2.2 Core Principles of Industry 5.0 945.2.3 Technological Advancements Driving Industry 5.0 955.3 The Role of IoT in Smart Manufacturing 975.3.1 IoT Architecture 975.3.2 Applications of IoT in Manufacturing 975.3.3 IoT-Enabled Smart Factory 985.4 Mobile Robotics in Manufacturing 985.4.1 Types of Mobile Robots 985.4.2 Key Functions of Mobile Robotics 995.4.3 Human-Robot Collaboration 995.4.4 Technological Integration 1005.5 Integration of IoT and Mobile Robotics in Smart Manufacturing 1005.5.1 Challenges in Integration 1005.5.2 Framework for Integration 1015.5.3 Data Sharing and Real-Time Communication 1015.5.4 Use Case: Real-Time Monitoring and Control 1015.6 Case Studies and Applications 1025.6.1 Global Industry Examples 1025.6.2 Benefits Achieved 1025.6.3 Lessons Learned 1025.7 Results and Discussion 1035.7.1 Key Findings from Literature and Case Studies 1035.7.2 Impact on Manufacturing Efficiency and Flexibility 1035.7.3 Human-Centric Manufacturing and Worker Empowerment 1055.7.4 Sustainability and Environmental Impact 1065.8 Challenges in the Integration of IoT and Mobile Robotics 1095.8.1 Technical and Operational Barriers 1095.8.2 Scalability Issues 1095.8.3 Standardization and Interoperability 1105.9 Future Trends and Research Directions 1105.9.1 AI and Machine Learning Integration 1105.9.2 5G and Edge Computing 1115.9.3 Cyber-Physical Systems and Digital Twins 1115.10 Conclusion 111References 1126 Innovative Approaches to Designing and Optimizing Mobile Robotics for Advanced Collaboration in Industry 5.0 115Mandeep Kaur, P. Arockia Mary, Dankan Gowda V., L.R. Sujithra and Priya Dongare Jadhav6.1 Introduction 1166.2 Technological Foundations of Mobile Robotics in Industry 5.0 1186.3 Literature Survey 1206.4 Proposed Innovative Approaches to Mobile Robotics Design 1236.5 Mobile Robotics for Advanced Collaboration 1256.6 Case Studies 1286.7 Results and Discussion 1316.8 Conclusion 134References 1357 Applications and Challenges of Digital Twins in Industry 5.0for Advanced Industrial Systems 139Dankan Gowda V., Galiveeti Poornima, Kottala Sri Yogi, Madan Mohanrao Jagtap and Shekhar R.7.1 Introduction 1407.2 Literature Survey 1427.3 Framework of Digital Twins in Industry 5.0 1447.4 Applications of Digital Twins 1467.5 Challenges in Implementing Digital Twins 1497.6 Results and Discussion 1517.7 Conclusion 155References 1558 Mobile Robotics for Agriculture: Design and Implementation of an Autonomous Robo-Snake 159Chandra Singh, Rathishchandra R. Gatti, K.V.S.S.S.S. Sairam and D.K. Sreekantha Karanam Desai8.1 Introduction 1608.2 Literature Survey 1618.3 Problem Statement 1668.4 Objectives 1678.5 Methodology 167Conclusion 171References 171Part 3: Human-Robot Collaboration and Interaction 1739 Synergistic Thinking: Human–Robot Partnership for Smarter Decisions 175Chandra Singh, Rathishchandra R. Gatti, Ganesha H. S. Harve, K.V.S.S.S.S. Sairam, Durga Prasad and Pavithra Poornima9.1 Introduction to Human–Robot Collaboration in Mobile Robotics 1769.1.1 Importance of AI Algorithms in Mobile Robotics 1779.2 Fundamentals of Decision Making in Mobile Robots 1789.3 Emerging Technologies in Mobile Robotics 1809.4 Cooperation Strategies 1819.5 Applications in Mobile Robotics 1829.6 Conclusion 184Bibliography 18410 Collaborative Robotics in Factory 5.0: Redefining Modern Production 187Chandra Singh, Rathishchandra R. Gatti, Ganesha H. S. Harve, K.V.S.S.S.S. Sairam, Durga Prasad and Pavithra PoornimaIntroduction to Factory 5.0 188Collaborative Robots (Cobots) and AI in Factory 5.0 189Augmented Reality (AR) and Virtual Reality (VR) 189Human-Centric Design in Factory 5.0 190Applications in Human–Robot Collaboration 191Logistics and Warehousing 191Logistics: Amazon’s Robotic Fulfillment Centers 191Challenges and Opportunities in Human–Robot Collaboration for Factory 5.0 191Applications of Cobots 195Future Trends in Cobot Technology 195Conclusion 196References 19611 Human–Robot Interaction in Industry 5.0 199Babitha Hemanth, Kripa T., Sumiksha Shetty and Smitha A. B.11.1 Importance of Human–Robot Interaction 20011.2 Growth of Artificial Intelligence and Machine Learning for Mobile Robots 20111.2.1 Intelligence-Driven Customization and Optimization in Autonomous Mobile Robotics 20211.3 Integration with Emerging Technologies 20311.4 Synergy with IoT 20411.4.1 Mobile Robots Integrated with IoT for Enhanced Communication and Data Sharing Across Industrial Systems 20411.4.2 Benefits of IoT-Enabled Mobile Robots in Real-Time Monitoring and Coordination 20511.5 Blockchain for Data Security 20711.5.1 Using Blockchain to Ensure Secure Data Transactions and Communication Between Mobile Robots and Other Industrial Systems 20711.6 Enhanced Connectivity 20811.6.1 Advanced Connectivity Technologies (e.g., 5G) Improving the Performance and Coordination of Mobile Robots in Dynamic Environments 20811.7 Human-Centric Innovations in Mobile Robotics 20911.8 Improving Human Well-Being and Job Satisfaction 20911.8.1 Alleviating Physical Strain: What Human Employees Gain from Mobile Robots Support in Terms of Redundant or Unsafe Duties 21011.8.2 Features Designed to Enhance Safety and Comfort in Human–Robot Collaboration 21111.9 Creating Collaborative Environments 21211.9.1 Innovations that Enable Seamless Interaction Between Mobile Robots and Human Operators 21211.9.2 Examples of Collaborative Robots (Cobots) and their Impact on Efficiency and Job Satisfaction 21311.10 Challenges and Future Directions in Human–Robot Interaction (HRI) 21511.11 Future Trends and Innovation in Human–Robot Interaction 215References 219Part 4: Specialized Applications and Innovations 22112 Augmented Reality in Healthcare: Applications, Security, and Mobile Robotics Integration 223S. Darwin, A. Rega and E. Fantin Irudaya Raj12.1 Introduction 22412.2 Profitable Benefits of AR in Education 22612.2.1 Medical Field 22612.2.2 Engineering Field 22712.2.2.1 Confrontation Factors in Augmented Reality-Based Wireless Communication 22812.3 Patients Home Care through AR 23012.3.1 Healthcare Intervention Using Wearable AR 23012.3.2 Rehabilitation Practices Using AR 23312.4 Surgeries Using AR Technology 23412.5 Services of AR in Healthcare 23712.5.1 Monitoring and Guidance in Health Care 23712.6 Challenges 23812.7 AR’s Potential in the Medical Field 23812.8 Conclusion 239References 24013 Enhancing Data Security, Sustainability, and Robotics Integration in IoT-Enabled Healthcare Systems 247Manjunatha Badiger, Jose Alex Mathew, Sushma P. S., Sharathchandra N. R., Gurusiddayya Hiremath and Manjunatha E. C.13.1 Introduction 24813.1.1 Overview of IoT in Healthcare: Applications and Significance in Patient Care 24813.1.2 The Intertwined Challenges of Data Security and Sustainability in IoT Healthcare Systems 25013.1.3 Importance of Addressing these Issues for Enhancing System Reliability and Patient Outcomes 25113.2 Data Security in IoT-Enabled Healthcare Systems 25113.2.1 Common Vulnerabilities in IoT Healthcare 25213.2.2 Regulatory Landscape and Compliance Requirements 25313.2.3 Consequences of Security Lapses 25413.3 Strategies for Enhancing Data Security 25613.3.1 Advanced Encryption Standards and Secure Communication Protocols 25613.3.2 Role of Blockchain in Ensuring Data Integrity and Traceability 25613.3.3 Biometric and Multi-Factor Authentication Mechanisms 25713.3.4 AI-Based Threat Detection and Response Systems 25713.4 Robotics in IoT-Enabled Healthcare 25813.4.1 Role of Robotics in Enhancing Healthcare Delivery and Patient Outcomes 25913.4.2 Secure Integration of IoT and Robotic Systems for Real-Time Monitoring and Surgical Assistance 25913.4.3 Energy-Efficient Designs for Robotic Healthcare Devices 26013.4.4 Robotics and AI Synergy for Personalized and Autonomous Healthcare Solutions 26013.5 Sustainability Challenges in IoT Healthcare Systems 26113.5.1 Energy Demands of IoT Devices and their Impact on Sustainability 26113.5.2 Environmental and Operational Implications of Inefficient Energy Management 26213.5.3 Critical Need for Balancing Performance with Energy Consumption 26313.6 Energy Efficiency in IoT Healthcare 26313.6.1 Adoption of Low-Power Communication Protocols 26313.6.2 Edge Computing to Minimize Energy-Intensive Cloud Communication 26513.6.3 Energy-Harvesting Technologies for Device Longevity 26513.6.4 Design Considerations for Creating Energy-Efficient IoT Networks 26713.7 Case Study 26813.7.1 Strengthening Cybersecurity for a Leading Private Hospital in London 26913.7.2 Case Study: BP’s Integration of Wearables Into Employee Wellness Programs 27013.8 Conclusion 271References 27114 Role of Blockchain and Mobile Robotics in Industry 5.0 – A Detailed Investigation 275P. Gayathri, A. Ravi, E. Fantin Irudaya Raj and M. Appadurai14.1 Introduction 27614.2 Evolution of Industry 5.0 27614.3 Portrayal of Block Chain 27714.4 Architecture of IoT 27814.5 STM and STC Chain in BC 27914.6 Mobile Robotics Technologies 27914.7 Mobile Robotics Views from A to Z 28014.8 Risks in Industry 5.0 28114.9 Cloud Solutions in Industry 5.0 28314.10 Limitations for Industry 5.0 28614.11 Control Approaches 28614.12 Revised Remodels in Industry 5.0 28814.13 Applications of Industry 5.0 28914.14 Applications of BC 28914.15 Upcoming Research for Industry 5.0 29014.16 Future Developments for Industry 6.0 29114.17 Conclusion 291Bibliography 29215 Sustainability and Resilience in Industry 5.0: Leveraging Machine Learning and AI Technologies 303Dankan Gowda V., Nidadavolu Venkat D.S.S.V. Prasad Raju, Kottala Sri Yogi, Mandeep Kaur and Srinivas D.15.1 Introduction 30415.2 Conceptual Framework of Industry 5.0 30615.3 Literature Survey 30815.4 Machine Learning Techniques for Sustainability 31015.5 AI Technologies Driving Resilience 31215.6 Sustainable Supply Chain Management 31515.7 Results and Discussion 31715.8 Future Directions and Challenges 32115.9 Conclusion 322References 32316 Development of an Auto Navigation Robot with LiDAR Technology 327Shrividya G., Sushma P. S., Charan, Chirag Ballal, Chethan K. T., Deepak V. S. and Usha Desai16.1 Introduction 32816.2 Methodology 33016.3 Design and Implementation 33116.4 Results and Discussion 33316.5 Conclusion 335References 33617 Design of Self-Sustaining Wall Projected Virtual Reality-Based Home and Industrial Automation System 339J. Naga Vishnu Vardhan, G. Rama Lakshmi, G. R. L. V. N. Srinivasa Raju, P. Sindhu, T. Sai Deepika, Iffath Fathima, Prasanna Laxmi and Usha Desai17.1 Introduction 34017.2 Methodology 34217.3 Results and Discussion 34417.4 Conclusion 348References 34818 Review of Sensor Fusion Applications in Autonomous Vehicles 351Aditya Avinash and Rathishchandra Ramachandra Gatti18.1 Introduction 35118.1.1 Challenges Faced by Sensors in AVs 35218.2 Sensor Modalities in AVs 35418.3 Sensor Calibration 35918.4 Sensor Fusion Techniques 36118.5 Applications and Case Studies 36418.6 Challenges and Future Directions 36818.7 Conclusion 370References 37119 Mobile Robotics in Industry 5.0: Leveraging AI and Machine Learning for Human-Centric Automation 375Suchetha G., Harinakshi C., Masooda and Chinmai Shetty19.1 Introduction to Industry 5.0 and Mobile Robotics 37619.2 AI and ML Concepts Empower Mobile Robotics in Industry 5.0 38119.3 Key AI Algorithms in Mobile Robotics 38219.4 Core Technologies in Mobile Robotics for Industry 5.0 38419.4.1 Natural Language Processing (NLP) and Voice Recognition: Facilitating Verbal Communication 38419.5 Applications and Use Cases of Mobile Robotics in Industry 5.0 38519.5.1 Collaborative Robotics on Production Floors 38519.6 Technical Challenges and Limitations in Mobile Robotics for Industry 5.0 38619.6.1 Data Processing and Real-Time Decision Making 38619.7 Future Trends and Innovations in Mobile Robotics for Industry 5.0 38719.8 Conclusion 388References 389About the Editors 391Index 393