
Artificial Intelligence for Future Generation Robotics
- 1st Edition - June 19, 2021
- Editors: Rabindra Nath Shaw, Ankush Ghosh, Valentina Emilia Balas, Monica Bianchini
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 8 5 4 9 8 - 6
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 8 5 7 9 9 - 4
Artificial Intelligence for Future Generation Robotics offers a vision for potential future robotics applications for AI technologies. Each chapter includes theory and mathemati… Read more

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Request a sales quoteArtificial Intelligence for Future Generation Robotics offers a vision for potential future robotics applications for AI technologies. Each chapter includes theory and mathematics to stimulate novel research directions based on the state-of-the-art in AI and smart robotics. Organized by application into ten chapters, this book offers a practical tool for researchers and engineers looking for new avenues and use-cases that combine AI with smart robotics. As we witness exponential growth in automation and the rapid advancement of underpinning technologies, such as ubiquitous computing, sensing, intelligent data processing, mobile computing and context aware applications, this book is an ideal resource for future innovation.
- Brings AI and smart robotics into imaginative, technically-informed dialogue
- Integrates fundamentals with real-world applications
- Presents potential applications for AI in smart robotics by use-case
- Gives detailed theory and mathematical calculations for each application
- Stimulates new thinking and research in applying AI to robotics
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- About the editors
- Preface
- Chapter One. Robotic process automation with increasing productivity and improving product quality using artificial intelligence and machine learning
- Abstract
- 1.1 Introduction
- 1.2 Related work
- 1.3 Proposed work
- 1.4 Proposed model
- 1.5 Manufacturing systems
- 1.6 Results analysis
- 1.7 Conclusions and future work
- References
- Chapter Two. Inverse kinematics analysis of 7-degree of freedom welding and drilling robot using artificial intelligence techniques
- Abstract
- 2.1 Introduction
- 2.2 Literature review
- 2.3 Modeling and design
- 2.4 Results and discussions
- 2.5 Conclusions and future work
- References
- Chapter Three. Vibration-based diagnosis of defect embedded in inner raceway of ball bearing using 1D convolutional neural network
- Abstract
- 3.1 Introduction
- 3.2 2D CNN—a brief introduction
- 3.3 1D convolutional neural network
- 3.4 Statistical parameters for feature extraction
- 3.5 Dataset used
- 3.6 Results
- 3.7 Conclusion
- References
- Chapter Four. Single shot detection for detecting real-time flying objects for unmanned aerial vehicle
- Abstract
- 4.1 Introduction
- 4.2 Related work
- 4.3 Methodology
- 4.4 Results and discussions
- 4.5 Conclusion
- References
- Chapter Five. Depression detection for elderly people using AI robotic systems leveraging the Nelder–Mead Method
- Abstract
- 5.1 Introduction
- 5.2 Background
- 5.3 Related work
- 5.4 Elderly people detect depression signs and symptoms
- 5.5 Proposed methodology
- 5.6 Result analysis
- References
- Chapter Six. Data heterogeneity mitigation in healthcare robotic systems leveraging the Nelder–Mead method
- Abstract
- 6.1 Introduction
- 6.2 Data heterogeneity mitigation
- 6.3 LSTM-based classification of data
- 6.4 Experiments and results
- 6.5 Conclusion and future work
- Acknowledgment
- References
- Chapter Seven. Advance machine learning and artificial intelligence applications in service robot
- Abstract
- 7.1 Introduction
- 7.2 Literature reviews
- 7.3 Uses of artificial intelligence and machine learning in robotics
- 7.4 Conclusion
- 7.5 Future scope
- References
- Chapter Eight. Integrated deep learning for self-driving robotic cars
- Abstract
- 8.1 Introduction
- 8.2 Self-driving program model
- 8.3 Self-driving algorithm
- 8.4 Deep reinforcement learning
- 8.5 Conclusion
- References
- Further reading
- Chapter Nine. Lyft 3D object detection for autonomous vehicles
- Abstract
- 9.1 Introduction
- 9.2 Related work
- 9.3 Dataset distribution
- 9.4 Methodology
- 9.5 Result
- 9.6 Conclusions
- References
- Chapter Ten. Recent trends in pedestrian detection for robotic vision using deep learning techniques
- Abstract
- 10.1 Introduction
- 10.2 Datasets and artificial intelligence enabled platforms
- 10.3 AI-based robotic vision
- 10.4 Applications of robotic vision toward pedestrian detection
- 10.5 Major challenges in pedestrian detection
- 10.6 Advanced AI algorithms for robotic vision
- 10.7 Discussion
- 10.8 Conclusions
- References
- Further reading
- Index
- No. of pages: 178
- Language: English
- Edition: 1
- Published: June 19, 2021
- Imprint: Elsevier
- Paperback ISBN: 9780323854986
- eBook ISBN: 9780323857994
RS
Rabindra Nath Shaw
AG
Ankush Ghosh
VE
Valentina Emilia Balas
MB