Artificial Intelligence for Future Generation Robotics
- Length: 178 pages
- Edition: 1
- Language: English
- Publisher: Elsevier
- Publication Date: 2021-07-21
- ISBN-10: 0323854982
- ISBN-13: 9780323854986
- Sales Rank: #0 (See Top 100 Books)
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.
Artificial Intelligence for Future Generation Robotics Copyright Contents List of contributors About the editors Preface one Robotic process automation with increasing productivity and improving product quality using artificial intelligence and... 1.1 Introduction 1.2 Related work 1.3 Proposed work 1.4 Proposed model 1.4.1 System component 1.4.2 Effective collaboration 1.5 Manufacturing systems 1.6 Results analysis 1.7 Conclusions and future work References two Inverse kinematics analysis of 7-degree of freedom welding and drilling robot using artificial intelligence techniques 2.1 Introduction 2.2 Literature review 2.3 Modeling and design 2.3.1 Fitness function 2.3.2 Particle swarm optimization 2.3.3 Firefly algorithm 2.3.4 Proposed algorithm 2.4 Results and discussions 2.5 Conclusions and future work References three Vibration-based diagnosis of defect embedded in inner raceway of ball bearing using 1D convolutional neural network 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 four Single shot detection for detecting real-time flying objects for unmanned aerial vehicle 4.1 Introduction 4.2 Related work 4.2.1 Appearance-based methods 4.2.2 Motion-based methods 4.2.3 Hybrid methods 4.2.4 Single-step detectors 4.2.5 Two-step detectors/region-based detectors 4.3 Methodology 4.3.1 Model training 4.3.2 Evaluation metric 4.4 Results and discussions 4.4.1 For real-time flying objects from video 4.5 Conclusion References Five Depression detection for elderly people using AI robotic systems leveraging the Nelder–Mead Method 5.1 Introduction 5.2 Background 5.3 Related work 5.4 Elderly people detect depression signs and symptoms 5.4.1 Causes of depression in older adults 5.4.2 Medical conditions that can cause elderly depression 5.4.3 Elderly depression as side effect of medication 5.4.4 Self-help for elderly depression 5.5 Proposed methodology 5.5.1 Proposed algorithm 5.5.2 Persistent monitoring for depression detection 5.5.3 Emergency monitoring 5.5.4 Personalized monitoring 5.5.5 Feature extraction 5.6 Result analysis References six Data heterogeneity mitigation in healthcare robotic systems leveraging the Nelder–Mead method 6.1 Introduction 6.1.1 Related work 6.1.2 Contributions 6.2 Data heterogeneity mitigation 6.2.1 Data preprocessing 6.2.2 Nelder–Mead method for mitigating data heterogeneity 6.3 LSTM-based classification of data 6.4 Experiments and results 6.4.1 Data heterogeneity mitigation using Nelder–Mead method 6.4.2 LSTM-based classification of data 6.5 Conclusion and future work Acknowledgment References SEVEN Advance machine learning and artificial intelligence applications in service robot 7.1 Introduction 7.2 Literature reviews 7.2.1 Home service robot 7.3 Uses of artificial intelligence and machine learning in robotics 7.3.1 Artificial intelligence applications in robotics [6] Assembly [7] Packaging [7] Customer service [7] Open source robotics [7] 7.3.2 Machine learning applications in robotics [10] 7.4 Conclusion 7.5 Future scope References Eight Integrated deep learning for self-driving robotic cars 8.1 Introduction 8.2 Self-driving program model 8.2.1 Human driving cycle Perception Scene generation Planning Action 8.2.2 Integration of supervised learning and reinforcement learning Supervised learning Reinforcement learning 8.3 Self-driving algorithm 8.3.1 Fundamental driving functions White lane detection Signals 8.3.2 Signals Traffic signs Laneless driving 8.3.3 Hazards YOLO and detection of objects Collision avoidance Estimation of risk level for self-driving 8.3.4 Warning systems Driver monitoring Pedestrian hazard detection Sidewalk cyclists’ detection 8.4 Deep reinforcement learning 8.4.1 Deep Q learning Learning rate Discount factor 8.4.2 Deep Q Network 8.4.3 Deep Q Network experimental results 8.4.4 Verification using robocar 8.5 Conclusion References Further reading NINE Lyft 3D object detection for autonomous vehicles 9.1 Introduction 9.2 Related work 9.2.1 Perception datasets 9.3 Dataset distribution 9.4 Methodology 9.4.1 Models 9.5 Result 9.6 Conclusions References TEN Recent trends in pedestrian detection for robotic vision using deep learning techniques 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 10.4.1 Smart homes and cities 10.4.2 Autonomous driving 10.4.3 Tracking 10.4.4 Reidentification 10.4.5 Anomaly detection 10.5 Major challenges in pedestrian detection 10.5.1 Illumination conditions 10.5.2 Instance size 10.5.3 Occlusion 10.5.4 Scene specific data 10.6 Advanced AI algorithms for robotic vision 10.7 Discussion 10.8 Conclusions References Further reading Index
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