Artificial 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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. About the editors - p. xiii 8. Preface - p. xv 9. Chapter One. Robotic process automation with increasing productivity and improving product quality using artificial intelligence and machine learning - p. 1 10. Chapter Two. Inverse kinematics analysis of 7-degree of freedom welding and drilling robot using artificial intelligence techniques - p. 15 11. Chapter Three. Vibration-based diagnosis of defect embedded in inner raceway of ball bearing using 1D convolutional neural network - p. 25 12. Chapter Four. Single shot detection for detecting real-time flying objects for unmanned aerial vehicle - p. 37 13. Chapter Five. Depression detection for elderly people using AI robotic systems leveraging the Nelder–Mead Method - p. 55 14. Chapter Six. Data heterogeneity mitigation in healthcare robotic systems leveraging the Nelder–Mead method - p. 71 15. Chapter Seven. Advance machine learning and artificial intelligence applications in service robot - p. 83 16. Chapter Eight. Integrated deep learning for self-driving robotic cars - p. 93 17. Chapter Nine. Lyft 3D object detection for autonomous vehicles - p. 119 18. Chapter Ten. Recent trends in pedestrian detection for robotic vision using deep learning techniques - p. 137 19. Index - p. 159
Artificial 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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xi 7. About the editors - p. xiii 8. Preface - p. xv 9. Chapter One. Robotic process automation with increasing productivity and improving product quality using artificial intelligence and machine learning - p. 1 10. Chapter Two. Inverse kinematics analysis of 7-degree of freedom welding and drilling robot using artificial intelligence techniques - p. 15 11. Chapter Three. Vibration-based diagnosis of defect embedded in inner raceway of ball bearing using 1D convolutional neural network - p. 25 12. Chapter Four. Single shot detection for detecting real-time flying objects for unmanned aerial vehicle - p. 37 13. Chapter Five. Depression detection for elderly people using AI robotic systems leveraging the Nelder–Mead Method - p. 55 14. Chapter Six. Data heterogeneity mitigation in healthcare robotic systems leveraging the Nelder–Mead method - p. 71 15. Chapter Seven. Advance machine learning and artificial intelligence applications in service robot - p. 83 16. Chapter Eight. Integrated deep learning for self-driving robotic cars - p. 93 17. Chapter Nine. Lyft 3D object detection for autonomous vehicles - p. 119 18. Chapter Ten. Recent trends in pedestrian detection for robotic vision using deep learning techniques - p. 137 19. Index - p. 159
