Next-Generation Intelligent Systems

Robotics, Internet of Things, and Smart Manufacturing
1. Auflage 2027
ISBN: 978-1-041-34618-0
Verlag: Taylor & Francis

Buch, Englisch, 392 Seiten, Format (B × H): 156 mm x 234 mm

This book showcases how robots are becoming smarter and more capable of working alongside humans. It covers exciting developments in self-driving cars, robotic assistants, and industrial automation, showing how artificial intelligence is transforming industries like healthcare, manufacturing, and transportation.

• Provides a thorough exploration of intelligent robotics, covering fundamental principles like kinematics, dynamics, and control systems, alongside artificial intelligence-driven advancements such as machine learning, deep learning, and reinforcement learning.

• Includes detailed case studies demonstrating how artificial intelligence-driven robots enhance efficiency, automation, and human-robot collaboration across various industries, bridging research with practical implementation.

• Explores real-world applications of intelligent robotics in healthcare (robot-assisted surgery, rehabilitation robots), autonomous vehicles, smart manufacturing, and service robotics.

• Bridges traditional robotics with modern artificial intelligence techniques, equipping readers with the knowledge to develop adaptive and autonomous robotic systems.

• Covers robotic software tools such as ROS (Robot Operating System), simulation platforms, and real-world datasets, enabling researchers and professionals to develop, test, and deploy intelligent robotic systems effectively.

The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, robotics, autonomous systems, mechanical engineering, artificial intelligence, and machine learning.

Zielgruppe

Academic, Postgraduate, and Undergraduate Advanced

Autoren/Hrsg.

Bongale, Arunkumar M

Majeed, Anwar P.P. Abdul

Shukla, Manoj Kumar

Sayyad, Javed

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik

Inhaltsverzeichnis

0. Front Matter.  1. Next-Gen Robotics: Principles, Evolution, Development, and Enabling Innovations.  2. Multi-Sensor Fusion–Based Perception Framework for Medical Image Analysis Using Computer Vision.  3. Deep Learning for Smart Manufacturing: Advances in Object Recognition and Scene Understanding.  4. Ethics, Safety, and Social Impact of Intelligent Systems.  5. Simultaneous Localization and Mapping (SLAM): Algorithms, Challenges, and Future Directions.  6. Bio-Inspired Control Architectures for Adaptive Robotic Systems.  7. Human–Robot Interaction and Collaborative Robotics.  8. Sensor-Based Strategies for Multi-Robot Coordination.  9. Smart Fields, Smarter Harvests: AI and Robotics in Sustainable Agriculture.  10. Autonomous Systems: Mobile Robots, Unmanned Aerial Vehicles (UAVs), and Intelligent Autonomous Vehicles.  11. Internet of Things (IoT) and Smart Manufacturing.  12. Human-Centric Intelligent Systems in Smart Manufacturing: Integrating Robotics, Internet of Things (IoT), and Artificial Intelligence (AI) for Industry 5.0.  13. IoT-Based Intelligent Systems for Remote Electricity Consumption Monitoring and Analysis.  14. Communicative Governance of Digital Twins: Media Narratives, Trust, and Public Legitimation in Smart Manufacturing.  15. Physics-Guided Neural Dynamics for Self-Healing and Energy-Efficient Autonomous Mobile Robots in Industrial Environments.  16. The Internet of Things, Smart Manufacturing, and Digital Twins for Plastic Waste Management: Global Case Studies and Emerging Practices.  17. A Novel Atangana–Baleanu Fractional Derivative-Based Convolutional Mask for Medical Image Edge Enhancement.  18. Deep Reinforcement Learning for Energy-Efficient Flight in Bio-Mimetic Ornithopters: A Sim-to-Real Approach Using Proximal Policy Optimization.  19. Review of Tiny ML Techniques for Low-Power Embedded IoT Systems.

Über Autor(innen)

Arunkumar M. Bongale is Professor and Head of the Department of Robotics & Automation at Symbiosis Institute of Technology, Pune, India. With an M. Tech and PhD in Mechanical Engineering, he brings over eighteen years of experience in robotics, smart manufacturing, materials engineering, sensors, mobile robots, ROS, navigation, and SLAM. He has authored over 65 research papers and holds three patents.

Anwar P.P. Abdul Majeed is an Associate Professor at the School of Engineering and Technology with experience in transnational education. He is a Chartered Engineer with IMechE, a member of IET, and a Senior Member of IEEE. With over 200 publications and editorial roles in prestigious journals and book series, he is committed to advancing knowledge in the application of machine learning and deep learning in different domains. He is also actively engaged in professional societies, alumni networks and contributes to the scientific community through his involvement in the Young Scientists Network of the Academy of Sciences Malaysia.

Manoj Kumar Shukla is currently working as an Assistant Professor at Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed) University, Pune, India. He completed M. Tech and PhD in Electrical Engineering from the National Institute of Technology, Hamirpur (Himachal Pradesh), India. His research interests mainly include control systems, robotics, artificial intelligence, smart sensors and automation. He has 10 years of teaching and research experience. He has published research papers in various reputed journals and conferences.

Javed Sayyad is an Assistant Professor in the Department of Robotics and Automation Engineering at Symbiosis Institute of Technology (SIT), Pune, India. With over 10 years of experience in electrical engineering, his research focuses on Solar Energy, Control Systems of Robotics, AI/ML, IoT, and Embedded Systems. He has published 40 papers and holds three patents. His expertise includes sensor integration, data analysis, and microcontroller-based system design.