Table of Contents

Cover image

Title page

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

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