Artificial intelligence for future generation robotics /

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Shaw, Rabindra Nath, Ghosh, Ankush, Balas, Valentina Emilia, Bianchini, Monica
Formato: Electrónico eBook
Idioma:Inglés
Publicado: San Diego : Elsevier, 2021.
Temas:
Robotics.
Artificial intelligence.
Robotics
Artificial Intelligence
Robotique.
Intelligence artificielle.
artificial intelligence.
Artificial intelligence
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
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

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