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Introduction

Recently, there has been a rapid advancement in Artificial Intelligence (AI) and robotics technologies, leading to the development of autonomous robots that can perform various tasks without human intervention. These autonomous robots can operate in dynamic and unpredictable environments, making real-time decisions based on the data they perceive. One of the key enabling technologies for autonomous robots is Edge AI, which allows them to process data locally on the device rather than relying on cloud computing. This thesis aims to explore the application of Edge AI in autonomous robots and evaluate its effectiveness in improving their performance and autonomy.

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

Chapter Two: Literature Review
1. Introduction to Edge AI
2. Autonomous robots and their applications
3. Edge computing and its relevance to AI
4. Challenges in implementing Edge AI for autonomous robots
5. Previous studies on Edge AI for autonomous robots
6. State-of-the-art technologies in Edge AI for autonomous robots
7. Potential future developments in Edge AI for autonomous robots
8. Comparison of Edge AI versus cloud-based AI for autonomous robots
9. Ethical considerations in the use of Edge AI for autonomous robots
10. Conclusion

Chapter Three: Research Methodology
1. Research design
2. Data collection methods
3. Data analysis techniques
4. Study population and sample size
5. Data validity and reliability
6. Research instruments
7. Ethical considerations
8. Research limitations

Chapter Four: Findings
1. Overview of the research findings
2. Analysis of the data collected
3. Evaluation of the effectiveness of Edge AI in autonomous robots
4. Comparison of Edge AI with cloud-based AI for autonomous robots
5. Impact of Edge AI on the performance of autonomous robots
6. Challenges faced in implementing Edge AI for autonomous robots
7. Future implications of Edge AI in autonomous robots
8. Recommendations for future research

Chapter Five: Conclusion and Summary
1. Summary of the research findings
2. Conclusions drawn from the study
3. Implications of the findings
4. Contribution to the field of Edge AI for autonomous robots
5. Recommendations for practitioners and policymakers
6. Suggestions for future research

Thesis Overview

The development of autonomous robots has gained widespread attention in recent years, with applications ranging from industrial automation to healthcare and defense. One of the key challenges in enabling autonomous robots to operate effectively in dynamic environments is the ability to process data in real-time and make decisions autonomously. This is where Edge AI comes in, providing the capability to process data locally on the device rather than relying on cloud computing.

This thesis explores the application of Edge AI in autonomous robots and evaluates its effectiveness in improving their performance and autonomy. The thesis begins with an introduction to the topic, providing background information on Edge AI and autonomous robots, as well as outlining the problem statement, objectives, and scope of the study.

The literature review in Chapter Two provides an overview of previous studies on Edge AI for autonomous robots, as well as current technologies and challenges in the field. Chapter Three outlines the research methodology, including the research design, data collection methods, and ethical considerations.

Chapter Four presents the findings of the research, analyzing the effectiveness of Edge AI in autonomous robots and comparing it with cloud-based AI. The discussion covers the impact of Edge AI on the performance of autonomous robots, challenges faced in implementation, and future implications of the technology.

Finally, Chapter Five concludes the thesis, summarizing the research findings, drawing conclusions, and providing recommendations for practitioners and policymakers. The thesis aims to make a significant contribution to the field of Edge AI for autonomous robots and suggests future research directions in this area.

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