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Computational Intelligent Techniques in Mechatronics

Edited by Kolla Bhanu Prakash, P. Satish Kumar, Ivan C. K. Tam, Wai Lok Woo and Vishal Jain
Series: Next-Generation Computing and Communication Engineering
Copyright: 2024   |   Status: Published
ISBN: 9781394174645  |  Hardcover  |  
542 pages
Price: $225 USD
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One Line Description
This book, set against the backdrop of huge advancements in artificial intelligence and machine learning within mechatronic systems, serves as a comprehensive guide to navigating the intricacies of mechatronics and harnessing its transformative potential.

Audience
This resource is important for R & D departments in academia, government, and industry. It will appeal to mechanical engineers, electronics engineers, computer scientists, robotics engineers, professionals in manufacturing, automation and related industries, as well as innovators and entrepreneurs.

Description
Mechatronics has been a revolutionary force in engineering and medical robotics over the past decade. It will lead to a major industrial revolution and affect research in every field of engineering. This book covers the basics of mechatronics, computational intelligence approaches, simulation and modeling concepts, architectures, nanotechnology, real-time monitoring and control, different actuators, and sensors. The book explains clearly and comprehensively the engineering design process at different stages. As the historical divisions between the various branches of engineering and computer science become less clearly defined, mechatronics may provide a roadmap for nontraditional engineering students studying within the traditional university structure. This book covers all the algorithms and techniques found in mechatronics engineering, well explained with real-time examples, especially lab experiments that will be very informative to students and scholars.

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Author / Editor Details
Kolla Bhanu Prakash, PhD, is a professor and associate dean and R & D head for A.I. & Data Science Research Group at K L University, Vijayawada, Andhra Pradesh, India. He is also an adjunct professor at Taylors University, Malaysia. He has published 150+ research papers in international and national journals and conferences. He has authored two and edited 12 books as well as published 15 patents. His research interests include deep learning, data science, and quantum computing.

Satish Kumar Peddapelli, PhD, is the Director at the Rajiv Gandhi University of Knowledge Technologies, IIIT-Basara, and Professor of Electrical Engineering, University College of Engineering, Osmania University, Hyderabad, India. His areas of interest are power electronics, drives, multi-level inverters, special machines and renewable energy systems.

Ivan C.K. Tam, PhD, is an associate professor in the Dept. of Marine Engineering Design & Technology, as well as the Director of Innovation & Engagement at the University of Newcastle in Singapore. He has a wealth of experience in multi-disciplinary research and a strong track record of leading innovative projects. His research interests are in the clean fuel combustion process, exhaust emission control, energy management and renewable energy technology.

Wai Lok Woo, PhD, received his doctorate in statistical machine learning from Newcastle University, UK. Prof Woo currently holds the Chair in Machine Learning with Northumbria University, UK. He is the Faculty Director of Research (Engineering and Environment), and Head of Research for Data Science and Artificial Intelligence. He was previously the Director of Research for Newcastle Research and Innovation Institute, and Director of Operations of Newcastle University. His major research is in mathematical theory and algorithms for data science and analytics.

Vishal Jain, PhD, is an associate professor in the Department of Computer Science and Engineering, Sharda School of Engineering and Technology, Sharda University, Greater Noida, India. He has more than 16 years of experience in academics and has authored more than 100 research papers in reputed journals and conferences as well as edited several books with the Wiley-Scrivener imprint.

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Table of Contents
Preface
1. AI in Mechatronics
Vansh Gehlot and Prashant Singh Rana
1.1 Introduction to AI Techniques for Mechatronics
1.1.1 Overview of Key AI Approaches
1.1.2 Benefits of Integrating AI in Mechatronic Systems
1.2 Machine Learning for Mechatronic Systems
1.2.1 Supervised, Unsupervised, and Reinforcement Learning Techniques
1.2.2 Applications in Control, Prediction, Optimization, and Diagnostics
1.2.3 Case Studies of Machine Learning in Robotics, Vehicles, and Automation
1.3 Computer Vision for Mechatronic Perception
1.3.1 Image Processing and Computer Vision Techniques
1.3.2 Enabling Environmental Perception and Scene Understanding
1.3.3 Vision-Based Control, Inspection, and Monitoring
1.4 Soft Computing Techniques
1.4.1 Fuzzy Logic Systems for Knowledge Representation and Control
1.4.2 Bio-Inspired Algorithms Like Neural Networks and Genetic Algorithms
1.4.3 Hybrid Intelligent Systems
1.5 AI Planning and Decision-Making
1.5.1 Automated Planning Algorithms for Sequencing Actions
1.5.2 Decision-Making Under Uncertainty
1.5.3 Applications in Navigation, Manufacturing Automation, Etc.
1.6 Natural Language Interaction
1.6.1 Speech Recognition and Natural Language Processing
1.6.2 Enabling Intuitive Human–Machine Interaction
1.6.3 Use Cases in Service Robots, Intelligent Agents, Human–Robot Collaboration
1.7 AI in Mechatronic System Design
1.7.1 Simulation of AI-Based Controllers and Behaviors
1.7.2 Tools for Virtual Prototyping of Intelligent Mechatronics
1.7.3 AI-Driven Design Optimization
1.8 Challenges and Future Outlook
1.8.1 Current Limitations in Applying AI to Mechatronics
1.8.2 Safety, Security, and Robustness Considerations
1.8.3 Emerging Trends and Opportunities
1.9 Artificial General Intelligence (AGI)
1.9.1 AGI and Narrow AI
1.9.2 Historical Development of AGI
1.9.3 State of AGI in Mechatronics Today
1.9.4 Future Possibilities
1.10 Conclusion
1.10.1 Insights Into AGI and Mechatronics Education
1.10.2 Motivating Message
References
2. Thermodynamics for Mechatronics
Yadav Krishnakumar Rajnath, Shrikant Tiwari and Virendra Kumar
2.1 Introduction
2.2 Defining Mechatronics and Its Interdisciplinary Nature
2.2.1 The Role of Thermodynamics in Engineering Innovation
2.2.2 Significance of Integrating Thermodynamics in Mechatronics
2.3 Fundamentals of Thermodynamics for Mechatronics
2.3.1 Laws of Thermodynamics: Concepts and Implications
2.3.2 Heat Transfer Mechanisms and Applications in Mechatronics
2.3.3 Energy Conversion Principles and Efficiency Considerations
2.4 Enhancing Efficiency in Mechatronics Through Thermodynamics
2.4.1 Thermodynamics-Driven Design Optimization for Mechatronic Systems
2.4.2 Thermal Management Strategies: Heat Dissipation and Regulation
2.4.3 Energy Efficiency Techniques and Heat Recovery in Mechatronics
2.5 Sustainability and Thermodynamics in Mechatronics
2.5.1 Mechatronics as a Catalyst for Sustainable Engineering
2.5.2 Environmental Benefits of Energy-Efficient Mechatronics
2.5.3 Utilizing Thermodynamics for Sustainable Resource Management
2.6 Innovative Applications and Future Trends
2.6.1 Harnessing Waste Heat: Thermoelectric Generators in Mechatronics
2.6.2 Embracing Energy-Frugal Systems: Future Trends and Challenges
2.6.3 Challenges in Implementing Future Trends
2.7 Educational and Professional Implications
2.7.1 Emphasizing the Importance of Incorporating Thermodynamics Education in Mechatronics Programs
2.7.2 Encouraging Interdisciplinary Collaboration Among Engineers to Optimize Energy-Frugal Mechatronic Systems
2.7.3 Conclusion: Leveraging Thermodynamics for Energy-Efficient Mechatronic Designs
References
3. Role of Data Acquisition, Sensors, and Actuators in Mechatronics Industry
Harpreet Kaur Channi
3.1 Introduction
3.2 Literature Survey
3.3 Fundamentals of Data Acquisition
3.3.1 Types of Data Acquisition Systems
3.3.2 Analog-to-Digital Conversion
3.3.3 Sampling and Signal Conditioning
3.3.4 Sensors in Mechatronics
3.3.5 Actuators in Mechatronics
3.4 Coordination and Synchronization in Mechatronic Systems
3.4.1 Interplay Between Data Acquisition, Sensors, and Actuators
3.5 Industrial Automation and Robotics
3.5.1 Automotive and Transportation Systems
3.5.2 Healthcare and Biomedical Applications
3.6 Technical Challenges in Integration and Compatibility
3.6.1 Innovations Driving Mechatronics Advancements
3.6.2 Mechatronics Industry and Industry 4.0
3.7 Future Trends and Implications
3.7.1 Advancements in Sensor Technology
3.7.2 Integration of AI and IoT in Mechatronic Systems
3.8 Conclusion
References
4. Optimization Techniques for Mechatronics: A Comprehensive Review and Future Directions
Ikvinderpal Singh and Sapandeep Kaur Dhillon
4.1 Introduction
4.1.1 Key Components of Mechatronics
4.2 Related Work
4.3 Optimization in Mechatronics Design
4.4 Optimization in Mechatronics Control
4.5 Optimization in Mechatronics Manufacturing
4.6 Multi-Objective Optimization in Mechatronics
4.7 Real-Time Optimization for Mechatronics
4.8 Challenges in Optimization for Mechatronics
4.9 Opportunities in Optimization for Mechatronics
4.10 Future Directions in Optimization for Mechatronics
4.11 Conclusion
Declarations
Conflict of Interest
Ethics Approval and Consent to Participate
Consent for Publication
Competing Interests
Open Access
Funding Statement
References
5. Reinforcement Learning for Adaptive Mechatronics Systems
D. Sathya, G. Saravanan and R. Thangamani
5.1 Introduction to Adaptive Mechatronics Systems
5.2 Fundamentals of Reinforcement Learning
5.3 Reinforcement Learning Algorithms for Mechatronics
5.4 Adaptive Control Strategies in Mechatronics
5.5 Autonomous Decision-Making in Mechatronics
5.6 Optimization and Energy Efficiency in Mechatronics
5.7 Safety and Robustness in Reinforcement Learning
5.8 Real-World Applications and Case Studies
5.9 Challenges and Future Directions
5.10 Ethical and Societal Implications
5.11 Conclusion
References
Further Reading
6. Application of PLC in the Mechatronics Industry
Harpreet Kaur Channi, Pulkit Kumar and Arvind Dhingra
6.1 Introduction
6.1.1 History and Evolution of PLCs
6.1.2 Literature Review
6.1.3 Scope and Objectives
6.2 Role of PLC in Mechatronics System Integration
6.2.1 Integration of PLC with Mechanical Systems
6.2.2 Integration of PLC with Electrical Systems
6.2.3 Integration of PLC with Computing Systems
6.3 PLC Applications in Mechatronics Industry
6.3.1 Programming and Implementation of PLC in Mechatronics
6.4 PLC in Mechatronics System Design
6.4.1 Integration of PLCs in Mechatronics Systems
6.4.2 Mechatronics System Components
6.4.3 PLC Hardware Selection
6.5 Safety in Mechatronics Systems
6.5.1 Safety Standards and Regulations
6.5.2 Safety Interlocks and Emergency Stop Systems
6.5.3 Fault Detection and Tolerance
6.6 Case Studies for Mechatronics Systems Using PLCs
6.6.1 Automotive Manufacturing
6.6.2 Bottling and Packaging Industry
6.6.3 Aircraft Landing Gear Control
6.6.4 E-Commerce Warehouse Automation
6.6.5 CNC Machining Centers
6.6.6 Precision Agriculture
6.7 Challenges and Future Trends
6.7.1 Challenges in Implementing Mechatronics Systems
6.7.2 Emerging Technologies and Trends in Mechatronics
6.8 Conclusion
References
7. Fuzzy Logic and Its Applications in Mechatronic Control Systems
D. Sathya, G. Saravanan and R. Thangamani
7.1 Introduction
7.1.1 Applications of Fuzzy Logic in Mechatronic Control Systems
7.2 Fuzzy Control Systems
7.2.1 Bridging Precision and Flexibility
7.2.2 Understanding Fuzzy Control Systems
7.2.3 Applications of Fuzzy Control Systems
7.2.4 Benefits and Challenges
7.2.5 Advantages of Fuzzy Logic in Mechatronic Control
7.3 Fuzzy Logic Applications in Mechatronic Control Systems
7.4 Fuzzy Expert Systems in Mechatronics
7.4.1 Enhancing Decision-Making and Control
7.4.2 Understanding Fuzzy Expert Systems
7.4.3 Applications in Mechatronics
7.4.4 Benefits and Challenges
7.5 Fuzzy Logic and Machine Learning in Mechatronics
7.5.1 A Synergistic Approach to Intelligent Control
7.5.2 Fuzzy Logic: Handling Uncertainty and Complex Relationships
7.5.3 Machine Learning: Data-Driven Adaptability
7.5.4 Applications in Mechatronics
7.5.5 Benefits and Challenges
7.6 Fuzzy Control in Multivariable Mechatronic Systems
7.6.1 Navigating Complexity with Adaptability
7.6.2 Challenges in Multivariable Mechatronic Systems
7.6.3 Fuzzy Control: A Multivariable Solution
7.6.4 Applications and Benefits
7.6.5 Benefits in Specific Applications
7.6.6 Challenges and Considerations
7.7 Industrial Automation and Fuzzy Logic
7.7.1 Enhancing Precision and Adaptability
7.7.2 Challenges in Industrial Automation
7.7.3 Fuzzy Logic: A Solution for Industrial Automation
7.7.4 Benefits and Considerations
7.8 Challenges and Future Directions
7.8.1 Challenges
7.8.2 Future Directions
7.9 Conclusion
References
Further Reading
8. Drones and Autonomous Robotics Incorporating Computational Intelligence
R. Thangamani, R. K. Suguna and G. K. Kamalam
8.1 Introduction
8.2 Literature Review
8.3 Navigation and Path Planning
8.4 Perception and Object Detection
8.5 Adaptive Control and Decision-Making
8.6 Swarm Robotics and Multi-Agent Systems
8.7 Autonomous Drone Delivery Systems
8.8 Human–Robot Interaction and Collaboration
8.9 Future Trends and Challenges
8.10 Ethical Implications of Autonomous Robotics and Drones
8.11 Conclusion
References
9. Exploring the Convergence of Artificial Intelligence and Mechatronics in Autonomous Driving
Ritika Wason, Parul Arora, Vishal Jain, Devansh Arora and M. N. Hoda
9.1 Introduction
9.2 Key Components of Advanced Driver Systems
9.2.1 LiDAR (Light Detection and Ranging)
9.2.2 RADAR (Radio Detection and Ranging)
9.2.3 Ultrasonic Sensors
9.2.4 Video Cameras
9.2.5 GPS (Global Positioning System)
9.3 Current State of AI-Enabled Self-Driving Mechatronics
9.4 Challenges in Self-Driving Mechatronics
9.5 Advantages of Self-Driving Mechatronics
9.6 Self-Driving and Environmental Sustainability
9.7 Legal and Safety Issues in Autonomous Driving
9.8 Conclusion
9.9 Future Directions in Self-Driving Mechatronics
References
10. Improving Power Quality for Industry Control Using Mechatronics Devices
Pulkit Kumar, Harpreet Kaur Channi and Surbhi Gupta
10.1 Introduction
10.1.1 Scope and Objectives of Power Quality in Industrial Control
10.1.2 Literature Review
10.2 Power Quality in Industrial Settings
10.2.1 Importance of Power Quality in Industrial Control
10.2.2 Challenges and Issues in Power Quality for Industrial Control
10.3 Mechatronics Devices for Power Quality Improvement
10.3.1 Applications of Mechatronics Devices in Industrial Control
10.3.2 Power Quality Monitoring and Analysis
10.3.2.1 Power Quality Parameters and Standards
10.3.2.2 Power Quality Monitoring Techniques
10.3.2.3 Data Analysis and Interpretation for Power Quality Assessment
10.4 Case Studies of Mechatronics Devices in Industry Control
10.4.1 Case Study 1
10.4.2 Case Study 2
10.4.3 Case Study 3
10.5 Integration of Mechatronics Devices in Industrial Control Systems
10.5.1 Challenges and Considerations for Integration
10.5.2 Communication and Control Interfaces
10.5.3 Safety and Reliability Aspects
10.6 Future Trends and Innovations in Mechatronics for Power Quality Improvement
10.6.1 Emerging Technologies in Mechatronics
10.6.2 Potential Applications in Industry Control
10.6.3 Implications for Power Quality Enhancement
10.7 Conclusion
References
11. Study on Integrated Neural Networks and Fuzzy Logic Control for Autonomous Electric Vehicles
S. Boopathi
11.1 Introduction
11.1.1 Need for Advanced Control Systems
11.1.2 Objectives of the Chapter
11.2 Fundamentals of Neural Networks and Fuzzy Logic
11.2.1 Neural Networks: Concepts
11.2.1.1 Feedforward Neural Networks
11.2.1.2 Recurrent Neural Networks
11.2.1.3 Convolutional Neural Networks
11.2.2 Fuzzy Logic: Principles and Membership Functions
11.2.2.1 Linguistic Variables and Fuzzy Sets
11.2.2.2 Fuzzy Rule–Based Systems
11.2.2.3 Defuzzification Techniques
11.3 Autonomous Electric Vehicles: Challenges and Control Requirements
11.3.1 Control Challenges in Autonomous Electric Vehicles
11.3.2 Importance of Real-Time Decision-Making
11.3.3 Role of Computational Intelligence in Autonomous Vehicles
11.3.3.1 Perception and Sensing
11.3.3.2 Decision-Making and Control
11.3.3.3 Localization and Mapping
11.3.3.4 Adaptation and Learning
11.3.3.5 Safety and Verification
11.4 Neural Network–Based Control for Autonomous Electric Vehicles
11.4.1 Perception and Sensing Using Neural Networks
11.4.1.1 Object Detection and Recognition
11.4.1.2 Sensor Fusion for Environmental Perception
11.4.1.3 Semantic Segmentation
11.4.2 Neural Network–Based Path Planning and Navigation
11.4.2.1 Lane Following and Trajectory Prediction
11.4.2.2 Collision Avoidance and Emergency Braking
11.4.2.3 Complex Traffic Scenarios
11.4.2.4 Learning from Simulation and Real-World Data
11.4.2.5 Continuous Learning and Improvement
11.4.3 Adaptive Learning and Self-Improvement Using Neural Networks
11.4.3.1 Continuous Learning from Driving Experience
11.4.3.2 Overcoming Challenging Situations Through Learning
11.4.3.3 Behavior Prediction and Adaptation
11.4.3.4 User-Centric Adaptation
11.4.3.5 Balancing Safety and Efficiency
11.5 Fuzzy Logic Control for Energy-Efficient Driving
11.5.1 Fuzzy Logic–Based Energy Management
11.5.1.1 Battery State-of-Charge Control
11.5.1.2 Optimal Power Distribution for Efficiency
11.5.1.3 Dynamic Load Management
11.5.1.4 User Preferences and Adaptive Control
11.5.1.5 Integration with Traffic and Route Information
11.5.2 Fuzzy Logic–Based Adaptive Cruise Control
11.5.2.1 Following Distance Regulation
11.5.2.2 Traffic Scenario Adaptation
11.5.2.3 Comfort and Driver Behavior Consideration
11.5.2.4 Handling Non-Motorized Traffic
11.5.2.5 Cooperative Adaptive Cruise Control
11.5.3 Fuzzy Logic–Based Regenerative Braking
11.5.3.1 Brake Force Modulation
11.5.3.2 Adaptive Braking Intensity
11.5.3.3 User Preferences and Driving Conditions
11.5.3.4 Predictive Braking
11.5.3.5 Coordination with Traffic Flow
11.5.4 Fuzzy Logic–Based Brake Force Optimization for Maximizing Energy Recuperation
11.5.4.1 Fuzzy Control of Brake Force
11.5.4.2 Balancing Energy Recuperation and Safety
11.5.4.3 Predictive Energy Management
11.5.4.4 User Preferences and Adaptive Control
11.5.4.5 Real-Time Adaptation
11.6 Integration of Neural Networks and Fuzzy Logic for Enhanced Autonomy
11.6.1 Combined Perception and Control using Neural-Fuzzy Systems
11.6.1.1 Enhanced Perception
11.6.1.2 Adaptive Decision-Making
11.6.1.3 Human-Like Reasoning
11.6.1.4 Autonomous Learning and Improvement
11.6.1.5 Real-Time Adaptation
11.6.2 Decision Fusion for Improved Safety and Reliability
11.6.2.1 Multi-Sensor Data Fusion
11.6.2.2 Multi-Model Decision-Making
11.6.2.3 Confidence-Based Decision Fusion
11.6.2.4 Handling Uncertain Situations
11.6.2.5 Redundancy and Fault Tolerance
11.6.3 Multi-Objective Optimization Using Hybrid Approaches
11.6.3.1 Multi-Objective Decision Formulation
11.6.3.2 Pareto Front Exploration
11.6.3.3 User-Centric Optimization
11.6.3.4 Real-Time Adaptation
11.6.3.5 Ethical and Safety Considerations
11.7 Case Studies and Applications
11.7.1 Autonomous Electric Fleet Management
11.7.1.1 Traffic Flow Optimization
11.7.1.2 Dynamic Route Planning
11.7.2 Urban Mobility Solutions and Ride-Sharing
11.7.2.1 User Experience Enhancement
11.7.2.2 Fleet Utilization Optimization
11.8 Future Prospects and Challenges
11.8.1 Ethical and Legal Considerations
11.8.2 Scalability and Real-World Deployment Challenges
11.8.3 Emerging Trends in Control and Autonomy
11.9 Conclusions
List of Abbreviations
References
12. Advancing Mechatronics Through Artificial Intelligence
Pawan Whig, Jhansi Bharathi Madavarapu, Venugopal Reddy Modhugu, Balaram Yadav Kasula and Ashima Bhatnagar Bhatia
12.1 Introduction
12.1.1 Background and Motivation
12.1.2 Scope and Objectives of the Chapter
12.2 Foundations of Mechatronics and Artificial Intelligence
12.2.1 Mechatronics: Where Physicality Meets Computation
12.2.2 Artificial Intelligence: Pinnacle of Machine Cognition
12.2.3 Confluence of Forces: Cognitive Integration
12.2.4 Toward Intelligent Autonomy
12.3 Synergies Between Artificial Intelligence and Mechatronics
12.3.1 Enhanced Adaptability Through AI
12.3.2 Real-Time Decision-Making and Control
12.3.3 Cognitive Robotics and Autonomous Systems
12.3.4 Breaking Boundaries in Smart Manufacturing
12.4 Case Studies: AI-Driven Advances in Mechatronics
12.4.1 Smart Manufacturing and Industrial Automation
12.4.2 Self-Learning Robotic Systems
12.4.3 Predictive Maintenance and Prognostics
12.4.4 Autonomous Vehicles: A Driving Force
12.5 Challenges and Opportunities
12.5.1 Technical Challenges: Reliability and Safety
12.5.2 Ethical Considerations: Accountability and Transparency
12.5.3 Collaborative Intelligence: Human–Machine Interaction
12.5.4 Opportunities for Innovation and Progress
12.6 Future Directions and Trends
12.6.1 AI-Enabled Mechatronic Innovations
12.6.2 Collaborative Intelligence in Human–Machine Systems
12.6.3 Ethical and Responsible AI in Mechatronics
12.6.4 Empowering Edge Computing
12.7 Conclusion
12.8 Future Scope
References
13. Computational Intelligent Techniques in Mechatronics: Emerging Trends and Case Studies
Anita Mohanty, Ambarish G. Mohapatra, Subrat Kumar Mohanty, Bright Keswani and Sasmita Nayak
13.1 Introduction to Mechatronics and Computational Intelligence
13.1.1 Outline of Mechatronics and Its Interdisciplinary Nature
13.1.2 Introduction to Computational Intelligence Techniques
13.1.3 Importance of CI in Solving Mechatronics Challenges
13.2 Artificial Neural Networks (ANNs) in Mechatronics
13.2.1 Fundamentals of Artificial Neural Networks
13.2.2 Applications of ANNs in Mechatronic System Modeling
13.2.3 ANN-Based Control in Mechatronic Systems
13.2.4 Case Studies: ANNs for Robotic Control and Fault
13.3 Reinforcement Learning in Mechatronics
13.3.1 Introduction to Reinforcement Learning (RL)
13.3.2 RL Algorithms and Their Applications in Mechatronics
13.3.3 RL for Autonomous Systems and Decision-Making
13.3.4 Case Studies: Reinforcement Learning in Autonomous Vehicles
13.4 Evolutionary Algorithms for Mechatronic System Design
13.4.1 Genetic Algorithms and Their Optimization Applications
13.4.2 Evolutionary Strategies in Mechatronics
13.4.3 Multi-Objective Optimization with Evolutionary Algorithms
13.4.4 Case Studies: Evolutionary Algorithms for Mechatronic Design
13.5 Emerging Trends in Mechatronics with Computational Intelligence
13.5.1 Integration of AI and CI in Mechatronics
13.5.2 Explainable AI in Safety-Critical Mechatronic Systems
13.5.3 Human–Robot Interaction and Emotional Intelligence
13.5.4 Biologically Inspired Robotics and Soft Robotics
13.5.5 Swarm Intelligence for Mechatronic System Control
13.6 Real-World Case Studies
13.6.1 Case Study 1: Adaptive Control of a Mechatronic System Using ANNs
13.6.2 Case Study 2: Reinforcement Learning for Autonomous Drone Navigation
13.6.3 Case Study 3: Multi-Objective Optimization in Mechatronic Design
13.6.4 Case Study 4: Explainable AI for Fault Diagnosis in a Robotic Arm
13.6.5 Case Study 5: Emotional Intelligence in a Social Robot
13.7 Conclusion
13.7.1 Recapitulation of Emerging Trends in CI for Mechatronics
13.7.2 Future Directions and Potential Applications
13.7.3 Implications of Computational Intelligent Techniques in Mechatronics Advancements
13.7.4 Key Impacts of CI in Mechatronics
References
14. Advanced Sensing Systems in Automobiles: Computational Intelligence Approach
Mamta B. Savadatti and Ajay Sudhir Bale
14.1 Introduction
14.2 Computational Intelligence Approach
14.2.1 Sensor Technology
14.2.1.1 Sensor Units Within the Automobile
14.2.1.2 In-Vehicle Sensors
14.2.1.3 Transport Networks with Intelligence
14.2.2 UAV Sensors in Remote Automobile Sensing
14.2.2.1 WSN for City Traffic Control System
14.2.3 The Video Intelligent Car Using WSN
14.2.4 Effects of CO2 Inside Cars
14.2.5 Accident Prevention In-Vehicle Air Quality
14.2.6 Sensors for Weather and Obstacle Detection in Vehicles
14.3 Methodology
14.3.1 Traditional In-Car Eye Blink Detection
14.3.2 Proposed Methodology
14.4 Conclusions
References
15. Design of Arduino UNO–Based Novel Multi-Featured Robot
Jaspinder Kaur, Rohit Anand, Nidhi Sindhwani, Ajay Kumar Sharma and Vishal Jain
15.1 Introduction
15.2 Design Implementation
15.2.1 Microcontroller
15.2.2 Motor Driver
15.2.3 Bluetooth
15.2.4 Ultrasonic Sensor
15.2.5 Servomotor
15.2.6 PIR Sensor
15.2.7 Components Specifications
15.3 Proposed Model
15.4 Process and Working Methodology
15.5 Experiment and Applications
15.6 Conclusion
15.7 Future Scope
Acknowledgments
References
16. Integrating Mechatronics in Autonomous Agricultural Machinery: A Case Study
N. V. Suresh, Ananth Selvakumar, Gajalakshmi Sridhar and Vishal Jain
16.1 Introduction
16.2 Case Background
16.3 Literature Review
16.3.1 Agribusiness and Mechatronics
16.3.2 Robotization and Exactness Developing
16.3.3 Sensors and Data Acquisition
16.3.4 Systems of Automation and Control
16.3.5 Normal Impact and Acceptability
16.3.6 Troubles and Future Headings
16.4 Methodology
16.5 Implementation
16.6 Findings
16.7 Suggestion
16.8 Conclusion
References
Index

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