Artificial Intelligence for Future Generation Robotics

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