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Introduction

Autonomous Underwater Vehicles (AUVs) have gained significant interest in recent years due to their ability to operate independently, without the need for human intervention. With advancements in Artificial Intelligence (AI), AUVs can now perform complex tasks underwater, such as underwater mapping, environmental monitoring, and search and rescue operations. This thesis explores the application of AI in AUVs, focusing on the design, implementation, and performance of intelligent systems for autonomous underwater navigation and operation.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of the study
1.3 Problem Statement
1.4 Objective of the study
1.5 Limitation of the study
1.6 Scope of the study
1.7 Significance of the study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of AUVs
2.2 Evolution of AI in AUVs
2.3 Applications of AI in AUVs
2.4 Navigation and Control of AUVs
2.5 Communication Systems for AUVs
2.6 Sensor Fusion in AUVs
2.7 Machine Learning in AUVs
2.8 Deep Learning in AUVs
2.9 Challenges and Limitations in AI for AUVs
2.10 Future Trends in AI for AUVs

Chapter 3: System Design and Methodology
3.1 System Architecture of AI-Based AUV
3.2 Selection of Sensors for AUV
3.3 Data Acquisition and Processing
3.4 AI Algorithms for Navigation and Control
3.5 Communication Protocols for AUVs
3.6 Testing and Validation Methods
3.7 Integration of Hardware and Software
3.8 Performance Evaluation Metrics

Chapter 4: System Implementation
4.1 Hardware Components for AI-Based AUV
4.2 Software Development for AUV Control
4.3 Integration of AI Algorithms
4.4 Calibration and Testing Procedures
4.5 Data Logging and Analysis
4.6 Troubleshooting and Debugging
4.7 Performance Optimization Techniques
4.8 Simulation and Field Testing

Chapter 5: Conclusion and Summary
5.1 Summary of the Study
5.2 Contributions and Findings
5.3 Implications for Future Research
5.4 Recommendations for Industry Applications
5.5 Conclusion

Thesis Overview on AI in Autonomous Underwater Vehicles

Autonomous Underwater Vehicles (AUVs) have become indispensable tools in various underwater applications, ranging from scientific research to military operations. With the integration of Artificial Intelligence (AI) technologies, AUVs have the potential to revolutionize the way underwater tasks are performed. This thesis explores the design, implementation, and evaluation of AI-based systems for autonomous underwater navigation and operation.

Chapter 1 provides an introduction to the study, outlining the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms related to AI in AUVs. Chapter 2 presents a comprehensive literature review, covering the evolution of AI in AUVs, applications, navigation and control, communication systems, sensor fusion, machine learning, deep learning, challenges, and future trends.

Chapter 3 focuses on the system design and methodology, detailing the system architecture, sensor selection, data acquisition, AI algorithms, communication protocols, testing, validation, and performance evaluation metrics. Chapter 4 delves into the system implementation, discussing hardware components, software development, integration of AI algorithms, calibration, testing, data logging, troubleshooting, performance optimization, and simulation.

Chapter 5 concludes the thesis, summarizing the study findings, contributions, implications for future research, recommendations for industry applications, and a final conclusion on the impact of AI in AUVs. Through this thesis, the aim is to provide valuable insights into the potential of AI in enhancing the efficiency, autonomy, and capabilities of Autonomous Underwater Vehicles for a wide range of underwater tasks.

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