Artificial intelligence for future generation robotics
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...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands ; Oxford, England ; Cambridge, Massachusetts :
Elsevier
[2021]
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009670618406719 |
Tabla de Contenidos:
- Front Cover
- Artificial Intelligence for Future Generation Robotics
- Copyright Page
- Contents
- List of contributors
- About the editors
- Preface
- 1. 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
- 2. 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
- 3. 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
- 4. 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
- 5. 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
- 6. 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
- 7. 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
- Assembly
- Packaging
- Customer service
- Open source robotics
- 7.3.2 Machine learning applications in robotics
- 7.4 Conclusion
- 7.5 Future scope
- References
- 8. 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
- 9. 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
- 10. 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
- Back Cover.
