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Submit a ProposalAI-Powered Robotic Perception Systems
 | Designs, Developments, and Applications Edited by Amit Kumar Tyagi Copyright: 2026 | Expected Pub Date:2026/10/30 ISBN: 9781394399192 | Hardcover | 708 pages |
One Line Description
Bridging essential theoretical foundations with practical blueprints for real-world execution, this comprehensive guide presents the frameworks needed to build the next generation of intelligent, AI-driven healthcare robots.
Description
Artificial intelligence has revolutionized how robots perceive and understand the world around them. The rapid evolution of technology has revolutionized the field of medicine, and nowhere is this more evident than in the integration of robots into modern healthcare systems. This book discusses the core principles and transformative potential of AI-powered perception systems in robotics. It explores how modern robots leverage AI technologies to recognize objects, navigate environments, and make autonomous decisions. By addressing both theoretical foundations and cutting-edge advancements,
the book highlights how AI enhances a robot’s ability to interpret complex sensory inputs and perform tasks with higher intelligence and adaptability. This book provides a detailed roadmap for building such systems, covering the integration of perception hardware with AI models for real-time scene understanding. It discusses key architectural elements, such as data processing pipelines, training datasets, and embedded intelligence. Developers, researchers, and engineers can benefit from in-depth discussions on software frameworks, neural network training methods, and simulation tools used
in robotic vision applications. By bridging theory with practice, the book serves as a comprehensive guide for advancing intelligent robotic systems for the future.
Readers will find the volume:
• Explores how artificial intelligence is integrated with robotic perception to enable autonomous decision-making;
• Focuses on how robots perceive, interpret, and interact with their environment in real-time using sensors and AI models;
• Provides a comprehensive view of designing and developing perception systems in robotics;
• Highlights various industrial and societal applications, such as autonomous vehicles, smart manufacturing, service robotics, healthcare robots, and disaster response;
• Introduces future directions for surgical robots as an example, including the potential of artificial intelligence, augmented reality, and haptic feedback.
Back to Top
Artificial intelligence has revolutionized how robots perceive and understand the world around them. The rapid evolution of technology has revolutionized the field of medicine, and nowhere is this more evident than in the integration of robots into modern healthcare systems. This book discusses the core principles and transformative potential of AI-powered perception systems in robotics. It explores how modern robots leverage AI technologies to recognize objects, navigate environments, and make autonomous decisions. By addressing both theoretical foundations and cutting-edge advancements,
the book highlights how AI enhances a robot’s ability to interpret complex sensory inputs and perform tasks with higher intelligence and adaptability. This book provides a detailed roadmap for building such systems, covering the integration of perception hardware with AI models for real-time scene understanding. It discusses key architectural elements, such as data processing pipelines, training datasets, and embedded intelligence. Developers, researchers, and engineers can benefit from in-depth discussions on software frameworks, neural network training methods, and simulation tools used
in robotic vision applications. By bridging theory with practice, the book serves as a comprehensive guide for advancing intelligent robotic systems for the future.
Readers will find the volume:
• Explores how artificial intelligence is integrated with robotic perception to enable autonomous decision-making;
• Focuses on how robots perceive, interpret, and interact with their environment in real-time using sensors and AI models;
• Provides a comprehensive view of designing and developing perception systems in robotics;
• Highlights various industrial and societal applications, such as autonomous vehicles, smart manufacturing, service robotics, healthcare robots, and disaster response;
• Introduces future directions for surgical robots as an example, including the potential of artificial intelligence, augmented reality, and haptic feedback.
Back to Top
Author / Editor Details
Amit Kumar Tyagi, PhD is an Assistant Professor at the National Institute of Fashion Technology in New Delhi, India. He has edited more than 40 books, authored five books, and published several award-winning papers in international journals. His research focuses on next-generation machine-based communications, blockchain technology, smart and secure computing, and privacy.
Amit Kumar Tyagi, PhD is an Assistant Professor at the National Institute of Fashion Technology in New Delhi, India. He has edited more than 40 books, authored five books, and published several award-winning papers in international journals. His research focuses on next-generation machine-based communications, blockchain technology, smart and secure computing, and privacy.
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Table of Contents
Preface
Part 1: Basic and Fundamentals
1. Introduction to Artificial Intelligence and Machine Learning in Robotics
Savita Kumari Sheoran and Ritika
1.1 Introduction
1.2 Artificial Intelligence and Machine Learning
1.3 AI and ML Applications in Robotics
1.3.1 Sensory Data Interpretation
1.3.2 Perception Data Interpretation
1.3.3 Decision-Making
1.3.4 Autonomous Navigation Behavior
1.3.5 Human-Robot Interaction
1.3.6 Healthcare
1.3.7 Logistics and Supply Chain Management
1.3.8 Construction
1.3.9 Exploration and Mining
1.3.10 Hospitality and Food Services
1.4 Machine Learning Algorithms for Robotics
1.4.1 Supervised Learning
1.4.2 Unsupervised Learning
1.4.3 Reinforcement Learning
1.5 Computer Vision for Object Detection and Recognition
1.5.1 Image Acquisition
1.5.2 Pre-Processing
1.5.3 Object Detection
1.5.4 Object Recognition
1.6 Reinforcement Learning for Decision-Making
1.6.1 Hierarchical Reinforcement Learning
1.6.2 Model-Based Reinforcement Learning
1.6.3 Exploration versus Exploitation
1.6.4 Agent-Environment Interaction
1.6.5 Rewards and Feedback
1.7 Real-Time Data Processing and Analysis
1.7.1 Sensor Fusion
1.7.2 Edge Computing
1.7.3 Simultaneous Localization and Mapping (SLAM)
1.7.4 Actuators
1.8 Challenges in Integrating AI and ML in Robotics
1.8.1 Data Requirements
1.8.2 Real-Time Processing
1.8.3 Robustness and Safety
1.8.4 Ethical and Social Implication
1.9 Future of AI and ML in Robotics
1.9.1 Human-Aware Robots
1.9.2 SWAE Robots
1.9.3 Lifelong Learning
1.10 Conclusion
References
2. Basics of Robotics and the Internet of Things (IoT)
R. Felista Sugirtha Lizy and Ir. Bambang Sugiyono Agus Purwono
2.1 Introduction
2.2 Literature Survey
2.3 IoT-Enabled Robotics Edge Computing
2.3.1 Edge Computing Enhances Real-Time Decision-Making in Robotics and in Internet of Things Systems
2.4 Robotics and the Internet of Things: Artificial Intelligence and Machine Learning
2.4.1 Use of AI in Robotics
2.5 IoT Security and Privacy of Robotic Systems
2.6 Collaborative Robotics (Cobots) in IoT Ecosystems
2.6.1 IoT Enhances the Capabilities of Cobots
2.7 5G’s Impact on IoT Integration and Robotics
2.8 Robotic Process Automation (RPA) and IoT in Business
2.9 Smart Manufacturing and the Future of Industrial IoT
2.10 Human-Robot Interaction in IoT-Enabled Environments
2.11 IoT-Driven Autonomous Mobile Robots (AMRS)
2.12 Blockchain for IoT and Robotics Security
2.13 Smart Agriculture: Robotics and IoT Integration
2.14 Swarm Robotics and IoT
2.15 IoT in Healthcare Robotics
2.16 Robotics in the Internet of Medical Things (IoMT)
2.17 Sustainability and Energy Efficiency in IoT-Enabled Robotics
2.18 Conclusion
References
3. Digital Twins and Simulation in Robotics
V. Lathika, Kaladevi A.C. and Ramya Perumal
3.1 Introduction
3.1.1 Digital Twin Technology
3.1.2 Components of Digital Twin
3.1.3 Industrial Robot Digital Twin Model Implementation
3.1.4 AI-Powered Digital Twins
3.1.5 Digital Twin Applications
3.1.6 Digital Twins and Industrial Robots
3.1.7 Real-World Applications of Digital Twins in Industrial Robotics
3.1.8 Challenges of Digital Twin Technology in Industrial Robots
3.2 Significance of Virtual Prototyping in Digital Twinning
3.2.1 Virtual Prototyping
3.2.2 Working of Virtual Prototyping
3.2.3 Virtual Prototyping: A Game Changer for Industrial Robots
3.2.4 Virtual Prototyping Tools
3.3 Real-Time Data Integration
3.3.1 Importance of Real-Time Data Integration for Digital Twins
3.3.2 Work Flow of Real-Time Data Integration for Digital Twins
3.3.3 Hardware Components
3.3.4 Communication Protocols for Streaming Data in Real Time
3.3.5 Best Practices for Effective Real-Time Data Integration in Digital Twins
3.3.6 Importance of AI and Machine Learning in Real-Time Data Processing
3.3.7 Examples of Real-Time Integration in Digital Twins
3.3.8 Future Trends of Digital Twins
3.4 Conclusion
References
4. Advancements in Sensory Perception for Precision Robotic
Manipulation: A Comprehensive Survey
Meet Thakur, Garima Jain, Ankush Jain and Prashant Kumar
4.1 Introduction
4.2 Sensor in a Dexterous Robotic Hand
4.3 Early Development: Force and Pressure Sensing
4.4 Electronic Skin and Full-Hand Tactile Coverage
4.5 Perception for Dexterous Manipulation
4.6 Discussion and Challenges of Perception in Dexterous
Manipulation
4.7 Conclusion
References
5. Future Challenges and Opportunities with Modern Technology to Achieve Sustainability
Amit Kumar Tyagi
5.1 Introduction to Sustainability and Zero Waste
5.1.1 Key Principles of Zero Waste
5.1.1.1 Benefits of Zero Waste
5.1.2 Importance of Sustainability in the Modern World
5.1.3 Overview of Modern Technology’s Role in Sustainability
5.2 Current State of Technology and Sustainability
5.2.1 Overview of Current Sustainable Technologies for Today’s Generation
5.2.1.1 Renewable Energy Technologies
5.2.1.2 Energy Efficiency Solutions
5.2.1.3 Sustainable Transportation
5.2.1.4 Circular Economy Innovations
5.2.1.5 Water Management and Conservation
5.2.1.6 Sustainable Agriculture Practices
5.2.1.7 Environmental Monitoring and Conservation
5.2.1.8 Digital Solutions for Sustainability
5.2.2 A Case Study: Successful Sustainable Initiatives in Today’s Era
5.2.2.1 Renewable Energy Adoption
5.2.2.2 Circular Economy Principles
5.2.2.3 Lessons Learned and Future Directions
5.2.3 Assessment of Current Challenges and Gaps toward Achieving Sustainability
5.2.3.1 Addressing Challenges and Filling Gaps
5.3 Future Challenges in Achieving Sustainability with Modern
Technologies
5.3.1 Technological Challenges with Modern Technologies in Achieving Sustainability
5.3.2 Environmental Challenges with Modern Technologies in Achieving Sustainability
5.3.3 Economic, Social, and Ethical Challenges with Modern Technologies in Achieving Sustainability
5.4 Popular Issues Faced in Achieving Sustainability with Modern Technologies
5.5 Future Opportunities with Modern Technology for Achieving Sustainability
5.5.1 Technological and Environmental Advancements with Modern Technology for Achieving Sustainability
5.5.1.1 Benefits of Advancements
5.5.1.2 Challenges and Considerations
5.5.2 Economic and Social Opportunities with Modern Technology for Achieving Sustainability
5.6 Policy and Regulatory Framework for Modern Technology in Achieving Sustainability
5.7 Strategies for Overcoming Challenges toward Modern Technology for Achieving Sustainability
5.8 The Role of Individuals and Communities for Achieving Sustainability with Modern Technology
5.9 Conclusion
References
6. Augmented Reality and Virtual Reality for Robotics: Innovations, Applications, and Future Trends
J. Vijila, Godfrey Winster Sathianesan, Gnanavel. S., M. Baskar and Mahmoud Elsisi
6.1 Introduction to AR/VR in Robotics
6.1.1 Overview of Augmented Reality (AR) and Virtual Reality (VR)
6.1.2 The Role of AR/VR in Robotics
6.1.3 Key Technologies and Tools in AR/VR for Robotics
6.1.4 Challenges and Future Trends
6.2 AR/VR in Robot Programming and Maintenance
6.2.1 AR-Based Interactive Robot Programming
6.2.2 VR Simulations for Robot Path Planning
6.2.3 Remote Maintenance and Troubleshooting Using AR
6.2.4 Case Studies on AR/VR in Robot Maintenance
6.3 Training and Simulation for Robotics Operations
6.3.1 VR-Based Training for Robot Operators
6.3.2 Simulation Technologies for Robotics Training
6.3.3 Benefits of AR/VR in Skill Development
6.4 Enhancing Human-Robot Interaction with AR/VR
6.4.1 Improving Collaboration between Humans and Robots
6.4.2 AR/VR Interfaces for Robot Control
6.4.3 Gesture and Voice-Based Interaction
6.4.4 Cognitive and Psychological Aspects of AR/VR in Robotics
6.5 Case Studies of AR/VR in Industrial Robotics
6.5.1 AR/VR in Smart Manufacture
6.5.2 Robotics in Warehousing and Logistics with AR/VR
6.5.3 Automotive Industry Use Cases
6.5.4 Healthcare and Assistive Robotics Applications
6.6 Future Directions and Innovations
6.6.1 Emerging Trends in AR/VR for Robotics
6.6.2 AI Integration with AR/VR in Robotics
6.6.3 Ethical and Security Considerations
6.6.4 Future Research Opportunities
6.7 Conclusion
References
7. Smart Mobility: Developing and Monitoring of Autonomous
Robotic System Under Distinct Environmental Conditions
A. Rehash Rushmi Pavitra, S. Sharanya and R. Radha
7.1 Introduction
7.1.1 Overview and Guidelines
7.2 Review of Literature
7.3 Evaluation of Autonomous Robotic Systems
7.4 Comprehensive Details on Mobile Robots
7.5 Proposed Methodology
7.5.1 Mobile Robot Navigation Using Fuzzy Logic Controller
7.5.2 Backpropagation Algorithm in Navigation
7.5.3 Artificial Bee Colony Planning
7.5.4 Navigation Using Landmarks
7.5.5 Path Planning
7.5.6 Environment and Robot Simulators
7.6 Insights into Research Summary
References
8. Robotic Perception Systems Driven by AI: Prospects, Architecture, Advancement, and Uses
Dipti Shukla
8.1 Introduction
8.2 Environment Representation
8.2.1 Evolution of AI in Robotics
8.2.2 Core Technologies: Foundational Elements Empowering AI in Robotics
8.3 Application of AI and ML Techniques in Advancing Robotic Perception Systems
8.4 The STRANDS Initiative
8.4.1 The Rob DREAM Initiative
8.4.2 The SPENCER Project
8.4.3 The AUTOCITS Initiative
8.4.4 Recent Advances in AI-Enabled Robotics
8.5 Conclusions and Remarks
References
Part 2: Methods and Principles
9. AI Enhances Robotic Perception Systems for Smarter, More
Accurate Environmental Understanding
Dipti Shukla
9.1 Introduction
9.1.1 An Overview of Perception in Robots
9.1.2 Evolution of Artificial Intelligence in Robotics and Autonomous Systems
9.1.3 Objectives and Scope of the Chapter
9.1.4 Emergence of AI in Robotics
9.2 Fundamentals of Robotic Perception
9.2.1 Types of Perception Systems
9.3 Robotic Perception Using AI Techniques
9.4 Architectures of Perception Systems
9.5 Application of Automated Discernment Driven by AI
9.6 Case Considerations in AI-Driven Automated Recognition
Frameworks
9.7 Challenges and Confinements in AI-Driven Automated Discernment
9.8 Conclusion
References
10. Enhancing Clinical Mobility for the Visually Impaired:
A YOLOv8 and GPT-3.5 Powered Assistive System with Real-Time Obstacle Detection and Voice Feedback
Monika Agarwal, Aparajita Sinha and Kaushal Prashant Patil 10.1 Introduction
10.2 Literature Survey
10.2.1 Key Takeaways and Research Gaps
10.3 Methodology and Architecture
10.3.1 A. Pipeline Architecture
10.3.2 B. Model Architecture - YOLOv8
10.3.3 C. Dataset
10.3.4 D. Model Training
10.3.5 E. Model Validation and Inferencing
10.3.6 F. Language Model - GPT-3.5
10.3.7 G. Google Text-to-Speech
10.4 Experimentation and Results
10.4.1 A. Validation Metrics
10.4.2 B. Inferencing
10.5 Bridging the Gap in Assistive Navigation
10.5.1 Advancements in Object Detection and Feedback Mechanisms
10.5.2 Custom Dataset for Enhanced Real-World Performance
10.5.3 Performance and Computational Efficiency
10.5.4 Limitations
10.6 Conclusion
References
11. A Hybrid Quantum-Enhanced Computing through Robotics in Healthcare
Aggarwal Ritu and Aggarwal Eshaan
11.1 Introduction
11.2 Related Work
11.3 QTML Technology Embedded with QTGRU and QTLSTM
11.3.1 Support Vector Machine
11.4 Proposed Methodology
11.4.1 Objective of This Study
11.4.2 Proposed QTML Architecture with the Dataset
11.5 Results and Discussion
Conclusion
References
12. Post-Quantum Cryptography in Resource Constrained Embedded Systems: Algorithmic Optimizations, Hardware Acceleration
Suhani Agarwal, Aswani Kumar Cherukuri and Amit Kumar Tyagi
12.1 Introduction
12.2 Cryptographic Algorithms in Embedded Systems: A Simplified Overview
12.3 Threat from Quantum Computing to Embedded Cryptographic Algorithms
12.4 Mitigating Quantum Threats in Embedded Environments
12.5 Post-Quantum Cryptographic Algorithms Suitable for Embedded Systems
12.6 Current State-of-the-Art in Embedded Applications of PQC
12.7 Conclusion and Future Work
References
13. Blockchain Innovation: Foundational Ideas and Related
Practical Applications
M. Ramprasath, Elangovan G., Prakash Duraisamy and V. Kavitha
13.1 Introduction
13.1.1 An Overview of Blockchain Computing Technology
13.1.2 Getting Started with Different Blockchain Platforms
13.1.3 Distributed Ledger Technology
13.2 Related Works
13.2.1 Sequence of Blocks and Chains Overview
13.2.2 Hashing
13.3 Blockchain Architecture
13.3.1 Blockchain Application
13.3.2 Blockchain Technology in Medicine
13.3.3 Data Security Using Blockchain
13.3.4 Wireless Networks and Blockchain Technology
13.3.5 Connecting Things Using Blockchain Technology
13.3.6 Distributed Ledger Technology for Grid Optimization
13.4 Conclusion, Restrictions, and Plans for the Future
Bibliography
14. AI Revolutionizing Applications Worldwide: A Comprehensive Overview of Its Potential Applications
R. Felista Sugirtha Lizy and Amit Kumar Tyagi
14.1 Introduction
14.2 Foundations of Artificial Intelligence Technologies
14.3 AI in Healthcare and Mental Health
14.4 AI in Education
14.5 Artificial Intelligence in Food and Agriculture
14.6 Artificial Intelligence in Industry 4.0 Manufacturing
14.7 Artificial Intelligence in Smart Cities and Transportation
14.8 AI in Finance, Business, and Governance
14.9 AI in Environmental Sustainability
14.10 Ethical, Legal, and Social Implications
14.11 Future Directions
14.12 Conclusion
Bibliography
15. Blockencrypt - Industrial Robotic Security Systems
Ambika N.
15.1 Introduction
15.2 Background
15.2.1 Blockchain
15.3 Literature Survey
15.4 Previous Work
15.5 Assumptions
15.5.1 Proposed Work
15.5.2 Analysis of the Work
15.5.3 Advantages of the Proposed System
15.6 Future Work
15.7 Conclusion
References
Part 3: Applications and Use Cases
16. Surgical Robots in Smart Hospitals
Nandini Rajesh and Somya R. Goyal
16.1 Introduction
16.2 Related Works
16.3 Surgical Robots and the Smart Hospital Ecosystem
16.4 Surgical Advancements in the Field
16.5 The Future of Surgical Robots
16.6 Conclusion and Future Work
References
17. Blockchain–AI–IoT–Based Applications for Smart Cities:
A Review of Architectures, Integration Trends, and Future Research Directions
Amit Kumar Tyagi and Shabnam Kumari
17.1 Introduction to Blockchain, Artificial Intelligence, and Internet of Things Technologies
17.1.1 Importance of Integrating Blockchain, AI, and IoT in Smart Cities
17.2 Architectures of Blockchain–AI–IoT Applications
17.2.1 Fundamental Components of Blockchain, AI, and IoT Integration
17.2.2 Architectural Frameworks and Models of Blockchain, AI, and IoT Integration
17.2.2.1 Layered Architectures: Blockchain, AI, and IoT Integration
17.2.2.2 Peer-to-Peer Architectures and Decentralization in Blockchain, AI, and IoT Integration
17.2.3 Case Studies of Existing Architectures
17.2.3.1 Example 1: Smart Grid Management Using Blockchain, AI, and IoT Integration
17.2.3.2 Example 2: Transportation and Logistics Optimization Using Blockchain, AI, and IoT Integration
17.3 Integration Trends
17.3.1 Current Trends in Blockchain–AI–IoT Integration
17.3.2 Benefits of Blockchain–AI–IoT Integration in Smart Cities in Today’s Era
17.4 Applications of Blockchain, AI, and IoT Integration—In General
17.4.1 Applications of Blockchain, AI, and IoT Integration—From Smart City Point of View
17.5 Future Research Directions for Blockchain, AI, and IoT
Integration in Smart Cities
17.5.1 Emerging Technologies and Innovations in Blockchain, AI, and IoT Integration in Smart Cities
17.5.1.1 Edge Computing and Blockchain Integration in Smart Cities
17.5.1.2 AI Algorithms for Predictive Analytics in IoT-Based Smart Cities
17.5.2 Potential Use Cases and Applications of Blockchain, AI, and IoT Integration in Smart Cities
17.5.2.1 Healthcare and Public Health Management Using Blockchain, AI, and IoT Integration in Smart Cities
17.5.2.2 Disaster Management and Resilience Planning Using Blockchain, AI, and IoT Integration in Smart Cities
17.5.3 Research Challenges and Opportunities in Blockchain, AI, and IoT Integration in Smart Cities
17.6 Regulatory and Policy Frameworks for Smart Cities
17.7 Conclusion
References
18. Robot Teaching in Smart Manufacturing: A Review of Methods, Technologies, and Future Directions
Subhadip Das, Pramit Brata Chanda and Subir Kumar Sarkar
18.1 Introduction
18.2 Traditional Robot Teaching Methods
18.2.1 Teach Pendant Programming
18.2.1.1 Benefits of Teaching Pendant Programming
18.2.1.2 Additional Benefits
18.2.1.3 Recent Developments: Open-Source Teach Pendants
18.2.2 Offline Programming (OLP)
18.2.2.1 Advantages of Offline Programming
18.2.3 Lead-Through Programming
18.2.3.1 Types of Lead-Through Programming
18.2.3.2 Advantages of Lead-Through Programming
18.2.4 Script-Based Programming
18.2.5 Manual Programming
18.2.5.1 Challenges of Traditional Robot Teaching Methods
18.3 Advanced Robot Teaching Methods
18.3.1 Learning from Demonstration (LFD)
18.3.1.1 Advantages of Learning from Demonstration
18.3.1.2 Challenges of Learning from Demonstration
18.3.2 Reinforcement Learning
18.3.2.1 Advantages of Reinforcement Learning
18.3.2.2 Challenges of Reinforcement Learning
18.3.3 Digital Twin Technology
18.3.3.1 Advantages of Digital Twin Technology
18.3.3.2 Challenges of Digital Twin Technology
18.3.4 Vision-Based Teaching
18.3.4.1 Advantages of Vision-Based Teaching
18.3.4.2 Challenges of Vision-Based Teaching
18.3.5 Haptic Teaching
18.3.5.1 Advantages of Haptic Teaching
18.3.5.2 Challenges of Haptic Teaching
18.3.6 Augmented Reality (AR) Teaching
18.3.6.1 Advantages of AR Teaching
18.3.6.2 Challenges of AR Teaching
18.3.7 Collaborative Robotics (Cobots)
18.3.7.1 Advantages of Collaborative Robotics
18.3.7.2 Challenges of Collaborative Robotics
18.3.8 Cloud-Based Teaching
18.3.8.1 Advantages of Cloud-Based Teaching
18.3.8.2 Challenges of Cloud-Based Teaching
18.4 Emerging Technologies in Robot Teaching
18.4.1 Augmented Reality (AR) and Virtual Reality (VR) in Robot Teaching
18.4.1.1 AR-Enabled Interactive Robot Programming
18.4.1.2 VR Simulations for Training and Maintenance
18.4.1.3 Benefits of AR and VR in Robot Teaching
18.5 Edge Computing and IoT Integration in Robot Teaching
18.5.1 Real-Time Data Processing for Adaptive Learning
18.5.2 IoT-Driven Communication and Coordination
18.5.3 Advantages of Edge Computing and IoT
18.5.4 Challenges in Edge Computing and IoT
18.5.5 Programming by Demonstration (PbD)
18.5.5.1 Key Principles
18.5.5.2 Advantages
18.5.5.3 Challenges
18.5.5.4 Applications
18.6 Machine Learning and AI Integration
18.6.1 Challenges in Implementing Advanced Robot Teaching
18.6.1.1 High Initial Costs and Integration Complexity
18.6.1.2 Interoperability and Standardization Issues
18.6.1.3 Workforce Adaptation and Skill Gaps
18.6.1.4 Cybersecurity Risks and Data Privacy Concerns
18.6.1.5 Ethical Considerations and Job Displacement
18.6.1.6 Real-Time Adaptability and Dynamic Environments
18.6.1.7 Sim-to-Real Transfer Challenges
18.6.1.8 Data Collection and Sample Efficiency
18.6.1.9 Human-Robot Collaboration and Safety
18.6.2 Computational Demands and Infrastructure Requirements
18.7 Case Studies and Real-World Applications
18.8 Digital Twins and Sim-to-Real Transfer
18.8.1 Multi-Agent and Multi-Task Learning
18.9 Conclusion
Bibliography
19. Synergy of Artificial Intelligence, Blockchain, Internet of Things, Digital Twin, with Edge Computing (ABIDE):
Applications and Future Directions
Amit Kumar Tyagi
19.1 Introduction
19.2 Overview of Key Concepts and Technologies
19.2.1 Edge Computing (EC)
19.2.2 Blockchain
19.2.3 Digital Twin
19.2.4 Internet of Things
19.2.5 Artificial Intelligence
19.3 Techniques and Architectures
19.3.1 Blockchain
19.3.2 Digital Twin
19.3.3 IoT and EC
19.3.4 AI-Powered EC Models
19.4 Applications and Use Cases
19.4.1 Smart Cities and Intelligent Transportation Systems
19.4.2 Healthcare and Real-Time Medical Diagnostics
19.4.3 Industrial Automation and Predictive Maintenance
19.4.4 Cybersecurity and Privacy-Preserving Edge Networks
19.4.5 Supply Chain and Logistics Management
19.5 Security, Privacy, and Performance Considerations
19.5.1 Security Threats in EC and Countermeasures
19.5.2 Blockchain-Enhanced Security and Data Integrity
19.5.3 Digital Twin-Enabled Risk Assessment and Mitigation
19.5.4 AI-Driven Anomaly Detection and Intrusion Detection Systems
19.6 Conclusion
References
20. Enhancing Safety and Well-Being: The Role of Facial Emotion Recognition and Drowsiness Detection
Sandhya D., Ruddarraju Abhilash Varma, Vaidehi Vijayakumar and Xavier Fernando
20.1 Introduction
20.2 Related Literature
20.3 Proposed Work on FERDD
20.3.1 Architecture for Facial Emotion Recognition-Based Drowsiness Detection
20.3.2 Algorithm for Facial Emotion Recognition-Based Drowsiness Detection
20.3.3 Pseudo Code for Facial Emotion Recognition-Based Drowsiness Detection
20.3.4 Implementation Details
20.4 Results and Discussion
20.4.1 Performance Comparison
20.4.2 Discussion on Related Work
20.5 Conclusion and Future Work
References
Part 4: Challenges and Opportunities
21. Ethical Considerations and Workforce Impacts
Perarasi T., Manoj R., Shoukath Ali K. and Arfat Ahmad Khan
21.1 Introduction
21.1.1 Overview of Ethical and Workforce Considerations in AI-Powered Robotics
21.1.2 The Growing Role of AI and Robotics in Various Industries
21.1.3 The Need for Ethical Guidelines and Workforce Adaptation
21.2 Ethical Implications of Robot Autonomy
21.2.1 Understanding Robot Autonomy and Decision-Making
21.2.2 Ethical Dilemmas in Autonomous Robotics
21.2.3 Moral Responsibility and Accountability
21.2.4 Bias, Fairness, and Discrimination in AI-Driven Robots
21.3 Human Employment and Role Transformation
21.3.1 The Impact of AI-Powered Robotics on Job Markets
21.3.2 Transforming Roles and New Avenues
21.3.3 Socioeconomic Disparities and Job Polarization
21.3.4 AI-Powered Robots as Workforce Enhancers
21.4 Ethical Challenges in Human-Robot Collaboration
21.4.1 Human-Robot Interaction: Ethical Considerations
21.4.2 Trust and Reliability in AI-Driven Robotic Systems
21.4.3 Privacy from Data Security During Human-Robot Collaboration
21.4.4 Level of Regulation and Inclusion of Legal Frameworks for Human-Robot Cooperation
21.5 Psychological and Social Impacts of AI-Powered Robotics
on Work Culture
21.6 Preparing Workforce for Robotic Integration
21.6.1 The Role of Education in AI and Robotics Integration
21.6.2 Reskilling and Upskilling Initiatives
21.6.3 Ethical Leadership and Workforce Readiness
21.6.4 Social Policies and Economic Measures
21.7 Conclusion and Future Directions
References
22. Search Engine Optimization Techniques for Effecting Natural Language Processing
Amit Kumar Tyagi, Shabnam Kumari, Utkarsh Kumar and Priyanga Subbiah
22.1 Introduction to Search Engine Optimization (SEO)
22.1.1 Basics of SEO Techniques
22.1.2 Importance of SEO in Natural Language Processing (NLP)
22.1.3 Key Features, Types, and Limitations of SEO in NLP
22.2 Foundations and Evolution of SEO Techniques
22.3 Integration of SEO Techniques in NLP for Smart Era
22.4 Advanced SEO Techniques for NLP
22.5 SEO Tools and Platforms for NLP
22.6 Open Issues and Challenges towards Using SEO in NLP
22.7 Case Studies
22.7.1 E-Commerce: Optimizing Product Descriptions for NLP and SEO
22.7.2 Local Businesses: Enhancing Online Presence with NLP-Driven SEO
22.8 Future Research Opportunities towards SEO in NLP
22.9 Conclusion
References
23. Industry 5.0 for Smart Cities—Background, Working Models, Challenges, and a Way Forward
Amit Kumar Tyagi and Shabnam Kumari
23.1 Introduction
23.1.1 Overview of Industry 5.0 and Its Evolution
23.1.1.1 Evolution of Industrial Revolutions
23.1.1.2 Key Features of Industry 5.0
23.1.1.3 Implications for Smart Cities
23.1.2 Importance of Industry 5.0 in the Context of Smart Cities
23.2 Background
23.2.1 The Conceptual Foundation of Industry 5.0
23.2.2 Key Technologies Enabling Industry 5.0
23.3 Working Models
23.3.1 Integration of Industry 5.0 with Smart City Infrastructure
23.3.2 Case Studies of Successful Implementations in Current Era
23.3.2.1 Smart Energy Grids and Renewable Integration in Industry 5.0
23.3.2.2 Advanced Mobility Solutions and Transportation in Industry 5.0
23.4 Popular Issues and Challenges in Adopting Industry 5.0
in Smart Cities
23.4.1 Popular Issues in Adopting Industry 5.0 in Smart Cities
23.4.2 Technological Challenges in Adopting Industry 5.0 in Smart Cities
23.4.3 Social and Economic Challenges in Adopting Industry 5.0 in Smart Cities
23.5 Adopting Industry 5.0 in Smart Cities: A Way Forward
23.5.1 Key Strategies for Overcoming Challenges in Adopting Industry 5.0 in Smart Cities
23.5.2 Policy Recommendations for a Safer and Smart City Administrators
23.6 Future Trends with Emerging Technologies in Industry 5.0
23.7 Sustainable and Resilient Smart Cities in the Next Century
23.8 Conclusion
References
24. Next-Generation Robotic Perception Artificial Intelligence Design, Development, and Real-World Impacts
Martins Olatoye Arowolo, Rafiu Mope Isiaka, Kingsley Theophilus Igulu, Ayodeji Nurudeen Mohammed, Adejare Adeyemo, Marvis Ayoola Arowolo, Adeola Margaret Olarewaju and Micheal Olaolu Arowolo
24.1 Introduction
24.2 Review of Literature
24.2.1 Computer Vision and Deep Learning
24.2.1.1 Convolutional Neural Networks
24.2.1.2 Vision Transformers
24.2.1.3 Self-Supervised Learning
24.2.2 LiDAR and 3D Perception
24.2.2.1 Three-Dimensional Perception and Point Cloud Analysis
24.2.2.2 Evaluation of Depth and Environmental Simulation
24.2.2.3 Applications of LiDAR and 3D Perception
24.2.2.4 Barriers and Potential Avenues
24.2.3 Sensor Fusion and Multimodal Learning
24.2.3.1 Kalman Filtering and Sensor Fusion
24.2.3.2 Deep Learning for Multimodal Sensor Fusion
24.2.3.3 Benefits of Sensor Fusion and Multimodal Learning
24.2.3.4 Challenges in Sensor Fusion and Multimodal Learning
24.2.3.5 Potential Pathways
24.2.4 Edge AI for Real-Time Perception
24.3 Thriving Deep Learning for Robotic Perception
24.3.1 CNNs in Perception
24.3.1.1 Overview of CNN Architecture
24.3.1.2 Component Summary
24.3.1.3 Main CNN Architectures
24.3.1.4 Self-Supervised Learning in Perception
24.3.1.5 Contrastive Learning in Perception
24.3.1.6 Generative Models in Perception
24.3.2 Neuromorphic Computing in Perceptual AI
24.3.2.1 Spiking Neural Networks
24.3.2.2 Transformers in Robotic Vision
24.3.2.3 Self-Attention Mechanism
24.3.3 Reinforcement Learning for Autonomous Agents
24.4 Applications of AI-Powered Robotic Perception
24.4.1 Autonomous Vehicles
24.4.2 Healthcare and Medical Robotics
24.4.3 Industrial Automation
24.4.4 Intelligent Agriculture
24.4.5 Geological Cognizance and Environmental Monitoring
24.4.6 AI-Enhanced Space Exploration
24.4.7 Geological Understanding and AI Integration
24.4.8 AI-Augmented Scientific Analysis of Languages and Language Revival
24.5 Challenges and Future Directions
24.5.1 Insufficient Data and Generalization
24.5.2 Limitations of Real-Time Processing
24.5.3 Ethical and Safety Considerations
24.5.4 Energy-Efficient AI Models
24.5.5 Robust Multimodal Perception
24.5.6 Synergy between Humans and AI
24.5.7 The Potential of AI-Enhanced Perception
24.6 Conclusion
References
Index
Back to Top
Preface
Part 1: Basic and Fundamentals
1. Introduction to Artificial Intelligence and Machine Learning in Robotics
Savita Kumari Sheoran and Ritika
1.1 Introduction
1.2 Artificial Intelligence and Machine Learning
1.3 AI and ML Applications in Robotics
1.3.1 Sensory Data Interpretation
1.3.2 Perception Data Interpretation
1.3.3 Decision-Making
1.3.4 Autonomous Navigation Behavior
1.3.5 Human-Robot Interaction
1.3.6 Healthcare
1.3.7 Logistics and Supply Chain Management
1.3.8 Construction
1.3.9 Exploration and Mining
1.3.10 Hospitality and Food Services
1.4 Machine Learning Algorithms for Robotics
1.4.1 Supervised Learning
1.4.2 Unsupervised Learning
1.4.3 Reinforcement Learning
1.5 Computer Vision for Object Detection and Recognition
1.5.1 Image Acquisition
1.5.2 Pre-Processing
1.5.3 Object Detection
1.5.4 Object Recognition
1.6 Reinforcement Learning for Decision-Making
1.6.1 Hierarchical Reinforcement Learning
1.6.2 Model-Based Reinforcement Learning
1.6.3 Exploration versus Exploitation
1.6.4 Agent-Environment Interaction
1.6.5 Rewards and Feedback
1.7 Real-Time Data Processing and Analysis
1.7.1 Sensor Fusion
1.7.2 Edge Computing
1.7.3 Simultaneous Localization and Mapping (SLAM)
1.7.4 Actuators
1.8 Challenges in Integrating AI and ML in Robotics
1.8.1 Data Requirements
1.8.2 Real-Time Processing
1.8.3 Robustness and Safety
1.8.4 Ethical and Social Implication
1.9 Future of AI and ML in Robotics
1.9.1 Human-Aware Robots
1.9.2 SWAE Robots
1.9.3 Lifelong Learning
1.10 Conclusion
References
2. Basics of Robotics and the Internet of Things (IoT)
R. Felista Sugirtha Lizy and Ir. Bambang Sugiyono Agus Purwono
2.1 Introduction
2.2 Literature Survey
2.3 IoT-Enabled Robotics Edge Computing
2.3.1 Edge Computing Enhances Real-Time Decision-Making in Robotics and in Internet of Things Systems
2.4 Robotics and the Internet of Things: Artificial Intelligence and Machine Learning
2.4.1 Use of AI in Robotics
2.5 IoT Security and Privacy of Robotic Systems
2.6 Collaborative Robotics (Cobots) in IoT Ecosystems
2.6.1 IoT Enhances the Capabilities of Cobots
2.7 5G’s Impact on IoT Integration and Robotics
2.8 Robotic Process Automation (RPA) and IoT in Business
2.9 Smart Manufacturing and the Future of Industrial IoT
2.10 Human-Robot Interaction in IoT-Enabled Environments
2.11 IoT-Driven Autonomous Mobile Robots (AMRS)
2.12 Blockchain for IoT and Robotics Security
2.13 Smart Agriculture: Robotics and IoT Integration
2.14 Swarm Robotics and IoT
2.15 IoT in Healthcare Robotics
2.16 Robotics in the Internet of Medical Things (IoMT)
2.17 Sustainability and Energy Efficiency in IoT-Enabled Robotics
2.18 Conclusion
References
3. Digital Twins and Simulation in Robotics
V. Lathika, Kaladevi A.C. and Ramya Perumal
3.1 Introduction
3.1.1 Digital Twin Technology
3.1.2 Components of Digital Twin
3.1.3 Industrial Robot Digital Twin Model Implementation
3.1.4 AI-Powered Digital Twins
3.1.5 Digital Twin Applications
3.1.6 Digital Twins and Industrial Robots
3.1.7 Real-World Applications of Digital Twins in Industrial Robotics
3.1.8 Challenges of Digital Twin Technology in Industrial Robots
3.2 Significance of Virtual Prototyping in Digital Twinning
3.2.1 Virtual Prototyping
3.2.2 Working of Virtual Prototyping
3.2.3 Virtual Prototyping: A Game Changer for Industrial Robots
3.2.4 Virtual Prototyping Tools
3.3 Real-Time Data Integration
3.3.1 Importance of Real-Time Data Integration for Digital Twins
3.3.2 Work Flow of Real-Time Data Integration for Digital Twins
3.3.3 Hardware Components
3.3.4 Communication Protocols for Streaming Data in Real Time
3.3.5 Best Practices for Effective Real-Time Data Integration in Digital Twins
3.3.6 Importance of AI and Machine Learning in Real-Time Data Processing
3.3.7 Examples of Real-Time Integration in Digital Twins
3.3.8 Future Trends of Digital Twins
3.4 Conclusion
References
4. Advancements in Sensory Perception for Precision Robotic
Manipulation: A Comprehensive Survey
Meet Thakur, Garima Jain, Ankush Jain and Prashant Kumar
4.1 Introduction
4.2 Sensor in a Dexterous Robotic Hand
4.3 Early Development: Force and Pressure Sensing
4.4 Electronic Skin and Full-Hand Tactile Coverage
4.5 Perception for Dexterous Manipulation
4.6 Discussion and Challenges of Perception in Dexterous
Manipulation
4.7 Conclusion
References
5. Future Challenges and Opportunities with Modern Technology to Achieve Sustainability
Amit Kumar Tyagi
5.1 Introduction to Sustainability and Zero Waste
5.1.1 Key Principles of Zero Waste
5.1.1.1 Benefits of Zero Waste
5.1.2 Importance of Sustainability in the Modern World
5.1.3 Overview of Modern Technology’s Role in Sustainability
5.2 Current State of Technology and Sustainability
5.2.1 Overview of Current Sustainable Technologies for Today’s Generation
5.2.1.1 Renewable Energy Technologies
5.2.1.2 Energy Efficiency Solutions
5.2.1.3 Sustainable Transportation
5.2.1.4 Circular Economy Innovations
5.2.1.5 Water Management and Conservation
5.2.1.6 Sustainable Agriculture Practices
5.2.1.7 Environmental Monitoring and Conservation
5.2.1.8 Digital Solutions for Sustainability
5.2.2 A Case Study: Successful Sustainable Initiatives in Today’s Era
5.2.2.1 Renewable Energy Adoption
5.2.2.2 Circular Economy Principles
5.2.2.3 Lessons Learned and Future Directions
5.2.3 Assessment of Current Challenges and Gaps toward Achieving Sustainability
5.2.3.1 Addressing Challenges and Filling Gaps
5.3 Future Challenges in Achieving Sustainability with Modern
Technologies
5.3.1 Technological Challenges with Modern Technologies in Achieving Sustainability
5.3.2 Environmental Challenges with Modern Technologies in Achieving Sustainability
5.3.3 Economic, Social, and Ethical Challenges with Modern Technologies in Achieving Sustainability
5.4 Popular Issues Faced in Achieving Sustainability with Modern Technologies
5.5 Future Opportunities with Modern Technology for Achieving Sustainability
5.5.1 Technological and Environmental Advancements with Modern Technology for Achieving Sustainability
5.5.1.1 Benefits of Advancements
5.5.1.2 Challenges and Considerations
5.5.2 Economic and Social Opportunities with Modern Technology for Achieving Sustainability
5.6 Policy and Regulatory Framework for Modern Technology in Achieving Sustainability
5.7 Strategies for Overcoming Challenges toward Modern Technology for Achieving Sustainability
5.8 The Role of Individuals and Communities for Achieving Sustainability with Modern Technology
5.9 Conclusion
References
6. Augmented Reality and Virtual Reality for Robotics: Innovations, Applications, and Future Trends
J. Vijila, Godfrey Winster Sathianesan, Gnanavel. S., M. Baskar and Mahmoud Elsisi
6.1 Introduction to AR/VR in Robotics
6.1.1 Overview of Augmented Reality (AR) and Virtual Reality (VR)
6.1.2 The Role of AR/VR in Robotics
6.1.3 Key Technologies and Tools in AR/VR for Robotics
6.1.4 Challenges and Future Trends
6.2 AR/VR in Robot Programming and Maintenance
6.2.1 AR-Based Interactive Robot Programming
6.2.2 VR Simulations for Robot Path Planning
6.2.3 Remote Maintenance and Troubleshooting Using AR
6.2.4 Case Studies on AR/VR in Robot Maintenance
6.3 Training and Simulation for Robotics Operations
6.3.1 VR-Based Training for Robot Operators
6.3.2 Simulation Technologies for Robotics Training
6.3.3 Benefits of AR/VR in Skill Development
6.4 Enhancing Human-Robot Interaction with AR/VR
6.4.1 Improving Collaboration between Humans and Robots
6.4.2 AR/VR Interfaces for Robot Control
6.4.3 Gesture and Voice-Based Interaction
6.4.4 Cognitive and Psychological Aspects of AR/VR in Robotics
6.5 Case Studies of AR/VR in Industrial Robotics
6.5.1 AR/VR in Smart Manufacture
6.5.2 Robotics in Warehousing and Logistics with AR/VR
6.5.3 Automotive Industry Use Cases
6.5.4 Healthcare and Assistive Robotics Applications
6.6 Future Directions and Innovations
6.6.1 Emerging Trends in AR/VR for Robotics
6.6.2 AI Integration with AR/VR in Robotics
6.6.3 Ethical and Security Considerations
6.6.4 Future Research Opportunities
6.7 Conclusion
References
7. Smart Mobility: Developing and Monitoring of Autonomous
Robotic System Under Distinct Environmental Conditions
A. Rehash Rushmi Pavitra, S. Sharanya and R. Radha
7.1 Introduction
7.1.1 Overview and Guidelines
7.2 Review of Literature
7.3 Evaluation of Autonomous Robotic Systems
7.4 Comprehensive Details on Mobile Robots
7.5 Proposed Methodology
7.5.1 Mobile Robot Navigation Using Fuzzy Logic Controller
7.5.2 Backpropagation Algorithm in Navigation
7.5.3 Artificial Bee Colony Planning
7.5.4 Navigation Using Landmarks
7.5.5 Path Planning
7.5.6 Environment and Robot Simulators
7.6 Insights into Research Summary
References
8. Robotic Perception Systems Driven by AI: Prospects, Architecture, Advancement, and Uses
Dipti Shukla
8.1 Introduction
8.2 Environment Representation
8.2.1 Evolution of AI in Robotics
8.2.2 Core Technologies: Foundational Elements Empowering AI in Robotics
8.3 Application of AI and ML Techniques in Advancing Robotic Perception Systems
8.4 The STRANDS Initiative
8.4.1 The Rob DREAM Initiative
8.4.2 The SPENCER Project
8.4.3 The AUTOCITS Initiative
8.4.4 Recent Advances in AI-Enabled Robotics
8.5 Conclusions and Remarks
References
Part 2: Methods and Principles
9. AI Enhances Robotic Perception Systems for Smarter, More
Accurate Environmental Understanding
Dipti Shukla
9.1 Introduction
9.1.1 An Overview of Perception in Robots
9.1.2 Evolution of Artificial Intelligence in Robotics and Autonomous Systems
9.1.3 Objectives and Scope of the Chapter
9.1.4 Emergence of AI in Robotics
9.2 Fundamentals of Robotic Perception
9.2.1 Types of Perception Systems
9.3 Robotic Perception Using AI Techniques
9.4 Architectures of Perception Systems
9.5 Application of Automated Discernment Driven by AI
9.6 Case Considerations in AI-Driven Automated Recognition
Frameworks
9.7 Challenges and Confinements in AI-Driven Automated Discernment
9.8 Conclusion
References
10. Enhancing Clinical Mobility for the Visually Impaired:
A YOLOv8 and GPT-3.5 Powered Assistive System with Real-Time Obstacle Detection and Voice Feedback
Monika Agarwal, Aparajita Sinha and Kaushal Prashant Patil 10.1 Introduction
10.2 Literature Survey
10.2.1 Key Takeaways and Research Gaps
10.3 Methodology and Architecture
10.3.1 A. Pipeline Architecture
10.3.2 B. Model Architecture - YOLOv8
10.3.3 C. Dataset
10.3.4 D. Model Training
10.3.5 E. Model Validation and Inferencing
10.3.6 F. Language Model - GPT-3.5
10.3.7 G. Google Text-to-Speech
10.4 Experimentation and Results
10.4.1 A. Validation Metrics
10.4.2 B. Inferencing
10.5 Bridging the Gap in Assistive Navigation
10.5.1 Advancements in Object Detection and Feedback Mechanisms
10.5.2 Custom Dataset for Enhanced Real-World Performance
10.5.3 Performance and Computational Efficiency
10.5.4 Limitations
10.6 Conclusion
References
11. A Hybrid Quantum-Enhanced Computing through Robotics in Healthcare
Aggarwal Ritu and Aggarwal Eshaan
11.1 Introduction
11.2 Related Work
11.3 QTML Technology Embedded with QTGRU and QTLSTM
11.3.1 Support Vector Machine
11.4 Proposed Methodology
11.4.1 Objective of This Study
11.4.2 Proposed QTML Architecture with the Dataset
11.5 Results and Discussion
Conclusion
References
12. Post-Quantum Cryptography in Resource Constrained Embedded Systems: Algorithmic Optimizations, Hardware Acceleration
Suhani Agarwal, Aswani Kumar Cherukuri and Amit Kumar Tyagi
12.1 Introduction
12.2 Cryptographic Algorithms in Embedded Systems: A Simplified Overview
12.3 Threat from Quantum Computing to Embedded Cryptographic Algorithms
12.4 Mitigating Quantum Threats in Embedded Environments
12.5 Post-Quantum Cryptographic Algorithms Suitable for Embedded Systems
12.6 Current State-of-the-Art in Embedded Applications of PQC
12.7 Conclusion and Future Work
References
13. Blockchain Innovation: Foundational Ideas and Related
Practical Applications
M. Ramprasath, Elangovan G., Prakash Duraisamy and V. Kavitha
13.1 Introduction
13.1.1 An Overview of Blockchain Computing Technology
13.1.2 Getting Started with Different Blockchain Platforms
13.1.3 Distributed Ledger Technology
13.2 Related Works
13.2.1 Sequence of Blocks and Chains Overview
13.2.2 Hashing
13.3 Blockchain Architecture
13.3.1 Blockchain Application
13.3.2 Blockchain Technology in Medicine
13.3.3 Data Security Using Blockchain
13.3.4 Wireless Networks and Blockchain Technology
13.3.5 Connecting Things Using Blockchain Technology
13.3.6 Distributed Ledger Technology for Grid Optimization
13.4 Conclusion, Restrictions, and Plans for the Future
Bibliography
14. AI Revolutionizing Applications Worldwide: A Comprehensive Overview of Its Potential Applications
R. Felista Sugirtha Lizy and Amit Kumar Tyagi
14.1 Introduction
14.2 Foundations of Artificial Intelligence Technologies
14.3 AI in Healthcare and Mental Health
14.4 AI in Education
14.5 Artificial Intelligence in Food and Agriculture
14.6 Artificial Intelligence in Industry 4.0 Manufacturing
14.7 Artificial Intelligence in Smart Cities and Transportation
14.8 AI in Finance, Business, and Governance
14.9 AI in Environmental Sustainability
14.10 Ethical, Legal, and Social Implications
14.11 Future Directions
14.12 Conclusion
Bibliography
15. Blockencrypt - Industrial Robotic Security Systems
Ambika N.
15.1 Introduction
15.2 Background
15.2.1 Blockchain
15.3 Literature Survey
15.4 Previous Work
15.5 Assumptions
15.5.1 Proposed Work
15.5.2 Analysis of the Work
15.5.3 Advantages of the Proposed System
15.6 Future Work
15.7 Conclusion
References
Part 3: Applications and Use Cases
16. Surgical Robots in Smart Hospitals
Nandini Rajesh and Somya R. Goyal
16.1 Introduction
16.2 Related Works
16.3 Surgical Robots and the Smart Hospital Ecosystem
16.4 Surgical Advancements in the Field
16.5 The Future of Surgical Robots
16.6 Conclusion and Future Work
References
17. Blockchain–AI–IoT–Based Applications for Smart Cities:
A Review of Architectures, Integration Trends, and Future Research Directions
Amit Kumar Tyagi and Shabnam Kumari
17.1 Introduction to Blockchain, Artificial Intelligence, and Internet of Things Technologies
17.1.1 Importance of Integrating Blockchain, AI, and IoT in Smart Cities
17.2 Architectures of Blockchain–AI–IoT Applications
17.2.1 Fundamental Components of Blockchain, AI, and IoT Integration
17.2.2 Architectural Frameworks and Models of Blockchain, AI, and IoT Integration
17.2.2.1 Layered Architectures: Blockchain, AI, and IoT Integration
17.2.2.2 Peer-to-Peer Architectures and Decentralization in Blockchain, AI, and IoT Integration
17.2.3 Case Studies of Existing Architectures
17.2.3.1 Example 1: Smart Grid Management Using Blockchain, AI, and IoT Integration
17.2.3.2 Example 2: Transportation and Logistics Optimization Using Blockchain, AI, and IoT Integration
17.3 Integration Trends
17.3.1 Current Trends in Blockchain–AI–IoT Integration
17.3.2 Benefits of Blockchain–AI–IoT Integration in Smart Cities in Today’s Era
17.4 Applications of Blockchain, AI, and IoT Integration—In General
17.4.1 Applications of Blockchain, AI, and IoT Integration—From Smart City Point of View
17.5 Future Research Directions for Blockchain, AI, and IoT
Integration in Smart Cities
17.5.1 Emerging Technologies and Innovations in Blockchain, AI, and IoT Integration in Smart Cities
17.5.1.1 Edge Computing and Blockchain Integration in Smart Cities
17.5.1.2 AI Algorithms for Predictive Analytics in IoT-Based Smart Cities
17.5.2 Potential Use Cases and Applications of Blockchain, AI, and IoT Integration in Smart Cities
17.5.2.1 Healthcare and Public Health Management Using Blockchain, AI, and IoT Integration in Smart Cities
17.5.2.2 Disaster Management and Resilience Planning Using Blockchain, AI, and IoT Integration in Smart Cities
17.5.3 Research Challenges and Opportunities in Blockchain, AI, and IoT Integration in Smart Cities
17.6 Regulatory and Policy Frameworks for Smart Cities
17.7 Conclusion
References
18. Robot Teaching in Smart Manufacturing: A Review of Methods, Technologies, and Future Directions
Subhadip Das, Pramit Brata Chanda and Subir Kumar Sarkar
18.1 Introduction
18.2 Traditional Robot Teaching Methods
18.2.1 Teach Pendant Programming
18.2.1.1 Benefits of Teaching Pendant Programming
18.2.1.2 Additional Benefits
18.2.1.3 Recent Developments: Open-Source Teach Pendants
18.2.2 Offline Programming (OLP)
18.2.2.1 Advantages of Offline Programming
18.2.3 Lead-Through Programming
18.2.3.1 Types of Lead-Through Programming
18.2.3.2 Advantages of Lead-Through Programming
18.2.4 Script-Based Programming
18.2.5 Manual Programming
18.2.5.1 Challenges of Traditional Robot Teaching Methods
18.3 Advanced Robot Teaching Methods
18.3.1 Learning from Demonstration (LFD)
18.3.1.1 Advantages of Learning from Demonstration
18.3.1.2 Challenges of Learning from Demonstration
18.3.2 Reinforcement Learning
18.3.2.1 Advantages of Reinforcement Learning
18.3.2.2 Challenges of Reinforcement Learning
18.3.3 Digital Twin Technology
18.3.3.1 Advantages of Digital Twin Technology
18.3.3.2 Challenges of Digital Twin Technology
18.3.4 Vision-Based Teaching
18.3.4.1 Advantages of Vision-Based Teaching
18.3.4.2 Challenges of Vision-Based Teaching
18.3.5 Haptic Teaching
18.3.5.1 Advantages of Haptic Teaching
18.3.5.2 Challenges of Haptic Teaching
18.3.6 Augmented Reality (AR) Teaching
18.3.6.1 Advantages of AR Teaching
18.3.6.2 Challenges of AR Teaching
18.3.7 Collaborative Robotics (Cobots)
18.3.7.1 Advantages of Collaborative Robotics
18.3.7.2 Challenges of Collaborative Robotics
18.3.8 Cloud-Based Teaching
18.3.8.1 Advantages of Cloud-Based Teaching
18.3.8.2 Challenges of Cloud-Based Teaching
18.4 Emerging Technologies in Robot Teaching
18.4.1 Augmented Reality (AR) and Virtual Reality (VR) in Robot Teaching
18.4.1.1 AR-Enabled Interactive Robot Programming
18.4.1.2 VR Simulations for Training and Maintenance
18.4.1.3 Benefits of AR and VR in Robot Teaching
18.5 Edge Computing and IoT Integration in Robot Teaching
18.5.1 Real-Time Data Processing for Adaptive Learning
18.5.2 IoT-Driven Communication and Coordination
18.5.3 Advantages of Edge Computing and IoT
18.5.4 Challenges in Edge Computing and IoT
18.5.5 Programming by Demonstration (PbD)
18.5.5.1 Key Principles
18.5.5.2 Advantages
18.5.5.3 Challenges
18.5.5.4 Applications
18.6 Machine Learning and AI Integration
18.6.1 Challenges in Implementing Advanced Robot Teaching
18.6.1.1 High Initial Costs and Integration Complexity
18.6.1.2 Interoperability and Standardization Issues
18.6.1.3 Workforce Adaptation and Skill Gaps
18.6.1.4 Cybersecurity Risks and Data Privacy Concerns
18.6.1.5 Ethical Considerations and Job Displacement
18.6.1.6 Real-Time Adaptability and Dynamic Environments
18.6.1.7 Sim-to-Real Transfer Challenges
18.6.1.8 Data Collection and Sample Efficiency
18.6.1.9 Human-Robot Collaboration and Safety
18.6.2 Computational Demands and Infrastructure Requirements
18.7 Case Studies and Real-World Applications
18.8 Digital Twins and Sim-to-Real Transfer
18.8.1 Multi-Agent and Multi-Task Learning
18.9 Conclusion
Bibliography
19. Synergy of Artificial Intelligence, Blockchain, Internet of Things, Digital Twin, with Edge Computing (ABIDE):
Applications and Future Directions
Amit Kumar Tyagi
19.1 Introduction
19.2 Overview of Key Concepts and Technologies
19.2.1 Edge Computing (EC)
19.2.2 Blockchain
19.2.3 Digital Twin
19.2.4 Internet of Things
19.2.5 Artificial Intelligence
19.3 Techniques and Architectures
19.3.1 Blockchain
19.3.2 Digital Twin
19.3.3 IoT and EC
19.3.4 AI-Powered EC Models
19.4 Applications and Use Cases
19.4.1 Smart Cities and Intelligent Transportation Systems
19.4.2 Healthcare and Real-Time Medical Diagnostics
19.4.3 Industrial Automation and Predictive Maintenance
19.4.4 Cybersecurity and Privacy-Preserving Edge Networks
19.4.5 Supply Chain and Logistics Management
19.5 Security, Privacy, and Performance Considerations
19.5.1 Security Threats in EC and Countermeasures
19.5.2 Blockchain-Enhanced Security and Data Integrity
19.5.3 Digital Twin-Enabled Risk Assessment and Mitigation
19.5.4 AI-Driven Anomaly Detection and Intrusion Detection Systems
19.6 Conclusion
References
20. Enhancing Safety and Well-Being: The Role of Facial Emotion Recognition and Drowsiness Detection
Sandhya D., Ruddarraju Abhilash Varma, Vaidehi Vijayakumar and Xavier Fernando
20.1 Introduction
20.2 Related Literature
20.3 Proposed Work on FERDD
20.3.1 Architecture for Facial Emotion Recognition-Based Drowsiness Detection
20.3.2 Algorithm for Facial Emotion Recognition-Based Drowsiness Detection
20.3.3 Pseudo Code for Facial Emotion Recognition-Based Drowsiness Detection
20.3.4 Implementation Details
20.4 Results and Discussion
20.4.1 Performance Comparison
20.4.2 Discussion on Related Work
20.5 Conclusion and Future Work
References
Part 4: Challenges and Opportunities
21. Ethical Considerations and Workforce Impacts
Perarasi T., Manoj R., Shoukath Ali K. and Arfat Ahmad Khan
21.1 Introduction
21.1.1 Overview of Ethical and Workforce Considerations in AI-Powered Robotics
21.1.2 The Growing Role of AI and Robotics in Various Industries
21.1.3 The Need for Ethical Guidelines and Workforce Adaptation
21.2 Ethical Implications of Robot Autonomy
21.2.1 Understanding Robot Autonomy and Decision-Making
21.2.2 Ethical Dilemmas in Autonomous Robotics
21.2.3 Moral Responsibility and Accountability
21.2.4 Bias, Fairness, and Discrimination in AI-Driven Robots
21.3 Human Employment and Role Transformation
21.3.1 The Impact of AI-Powered Robotics on Job Markets
21.3.2 Transforming Roles and New Avenues
21.3.3 Socioeconomic Disparities and Job Polarization
21.3.4 AI-Powered Robots as Workforce Enhancers
21.4 Ethical Challenges in Human-Robot Collaboration
21.4.1 Human-Robot Interaction: Ethical Considerations
21.4.2 Trust and Reliability in AI-Driven Robotic Systems
21.4.3 Privacy from Data Security During Human-Robot Collaboration
21.4.4 Level of Regulation and Inclusion of Legal Frameworks for Human-Robot Cooperation
21.5 Psychological and Social Impacts of AI-Powered Robotics
on Work Culture
21.6 Preparing Workforce for Robotic Integration
21.6.1 The Role of Education in AI and Robotics Integration
21.6.2 Reskilling and Upskilling Initiatives
21.6.3 Ethical Leadership and Workforce Readiness
21.6.4 Social Policies and Economic Measures
21.7 Conclusion and Future Directions
References
22. Search Engine Optimization Techniques for Effecting Natural Language Processing
Amit Kumar Tyagi, Shabnam Kumari, Utkarsh Kumar and Priyanga Subbiah
22.1 Introduction to Search Engine Optimization (SEO)
22.1.1 Basics of SEO Techniques
22.1.2 Importance of SEO in Natural Language Processing (NLP)
22.1.3 Key Features, Types, and Limitations of SEO in NLP
22.2 Foundations and Evolution of SEO Techniques
22.3 Integration of SEO Techniques in NLP for Smart Era
22.4 Advanced SEO Techniques for NLP
22.5 SEO Tools and Platforms for NLP
22.6 Open Issues and Challenges towards Using SEO in NLP
22.7 Case Studies
22.7.1 E-Commerce: Optimizing Product Descriptions for NLP and SEO
22.7.2 Local Businesses: Enhancing Online Presence with NLP-Driven SEO
22.8 Future Research Opportunities towards SEO in NLP
22.9 Conclusion
References
23. Industry 5.0 for Smart Cities—Background, Working Models, Challenges, and a Way Forward
Amit Kumar Tyagi and Shabnam Kumari
23.1 Introduction
23.1.1 Overview of Industry 5.0 and Its Evolution
23.1.1.1 Evolution of Industrial Revolutions
23.1.1.2 Key Features of Industry 5.0
23.1.1.3 Implications for Smart Cities
23.1.2 Importance of Industry 5.0 in the Context of Smart Cities
23.2 Background
23.2.1 The Conceptual Foundation of Industry 5.0
23.2.2 Key Technologies Enabling Industry 5.0
23.3 Working Models
23.3.1 Integration of Industry 5.0 with Smart City Infrastructure
23.3.2 Case Studies of Successful Implementations in Current Era
23.3.2.1 Smart Energy Grids and Renewable Integration in Industry 5.0
23.3.2.2 Advanced Mobility Solutions and Transportation in Industry 5.0
23.4 Popular Issues and Challenges in Adopting Industry 5.0
in Smart Cities
23.4.1 Popular Issues in Adopting Industry 5.0 in Smart Cities
23.4.2 Technological Challenges in Adopting Industry 5.0 in Smart Cities
23.4.3 Social and Economic Challenges in Adopting Industry 5.0 in Smart Cities
23.5 Adopting Industry 5.0 in Smart Cities: A Way Forward
23.5.1 Key Strategies for Overcoming Challenges in Adopting Industry 5.0 in Smart Cities
23.5.2 Policy Recommendations for a Safer and Smart City Administrators
23.6 Future Trends with Emerging Technologies in Industry 5.0
23.7 Sustainable and Resilient Smart Cities in the Next Century
23.8 Conclusion
References
24. Next-Generation Robotic Perception Artificial Intelligence Design, Development, and Real-World Impacts
Martins Olatoye Arowolo, Rafiu Mope Isiaka, Kingsley Theophilus Igulu, Ayodeji Nurudeen Mohammed, Adejare Adeyemo, Marvis Ayoola Arowolo, Adeola Margaret Olarewaju and Micheal Olaolu Arowolo
24.1 Introduction
24.2 Review of Literature
24.2.1 Computer Vision and Deep Learning
24.2.1.1 Convolutional Neural Networks
24.2.1.2 Vision Transformers
24.2.1.3 Self-Supervised Learning
24.2.2 LiDAR and 3D Perception
24.2.2.1 Three-Dimensional Perception and Point Cloud Analysis
24.2.2.2 Evaluation of Depth and Environmental Simulation
24.2.2.3 Applications of LiDAR and 3D Perception
24.2.2.4 Barriers and Potential Avenues
24.2.3 Sensor Fusion and Multimodal Learning
24.2.3.1 Kalman Filtering and Sensor Fusion
24.2.3.2 Deep Learning for Multimodal Sensor Fusion
24.2.3.3 Benefits of Sensor Fusion and Multimodal Learning
24.2.3.4 Challenges in Sensor Fusion and Multimodal Learning
24.2.3.5 Potential Pathways
24.2.4 Edge AI for Real-Time Perception
24.3 Thriving Deep Learning for Robotic Perception
24.3.1 CNNs in Perception
24.3.1.1 Overview of CNN Architecture
24.3.1.2 Component Summary
24.3.1.3 Main CNN Architectures
24.3.1.4 Self-Supervised Learning in Perception
24.3.1.5 Contrastive Learning in Perception
24.3.1.6 Generative Models in Perception
24.3.2 Neuromorphic Computing in Perceptual AI
24.3.2.1 Spiking Neural Networks
24.3.2.2 Transformers in Robotic Vision
24.3.2.3 Self-Attention Mechanism
24.3.3 Reinforcement Learning for Autonomous Agents
24.4 Applications of AI-Powered Robotic Perception
24.4.1 Autonomous Vehicles
24.4.2 Healthcare and Medical Robotics
24.4.3 Industrial Automation
24.4.4 Intelligent Agriculture
24.4.5 Geological Cognizance and Environmental Monitoring
24.4.6 AI-Enhanced Space Exploration
24.4.7 Geological Understanding and AI Integration
24.4.8 AI-Augmented Scientific Analysis of Languages and Language Revival
24.5 Challenges and Future Directions
24.5.1 Insufficient Data and Generalization
24.5.2 Limitations of Real-Time Processing
24.5.3 Ethical and Safety Considerations
24.5.4 Energy-Efficient AI Models
24.5.5 Robust Multimodal Perception
24.5.6 Synergy between Humans and AI
24.5.7 The Potential of AI-Enhanced Perception
24.6 Conclusion
References
Index
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