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AI in Mechatronics and Robotics Engineering [#1015409]
AI in Mechatronics and Robotics Engineering by Ajit Singh
English | July 17, 2025 | ISBN: N/A | ASIN: B0FHZPKYHV | 212 pages | EPUB | 0.28 Mb
"AI in Mechatronics and Robotics Engineering" is a comprehensive and modern textbook meticulously crafted for undergraduate (B.Tech) and postgraduate (M.Tech) students of Mechatronics, Robotics, Mechanical, Electrical, and Computer Science Engineering. It serves as a definitive guide to understanding and applying the principles of Artificial Intelligence in the design, control, and operation of modern mechatronic and robotic systems.
Key Features of This Book:
1. Practical-First Approach: Every chapter is anchored with simple, easy-to-understand practical examples and code snippets (in Python) to bridge the theory-practice divide.
2. Lucid and Accessible Language: Complex topics like neural networks, sensor fusion, and reinforcement learning are broken down into simple, digestible explanations, making them accessible to students from various engineering backgrounds.
3. Modular 10-Chapter Structure: The book is organized into a logical sequence of 10 chapters, making it ideal for a one-semester course. It progresses from fundamentals to advanced topics, ensuring a smooth learning curve.
4. Integrated Capstone Project: A dedicated final chapter guides students step-by-step through the process of building a real-world AI-powered mechatronic system, reinforcing concepts from across the book.
5. Updated and Relevant Content: Covers the latest advancements and industry-relevant topics, including Deep Learning, Reinforcement Learning, Computer Vision, SLAM, Human-Robot Interaction, and the ethics of AI.
6. Focus on Essential Tools: Introduces students to industry-standard tools and libraries like Python, TensorFlow, PyTorch, and OpenCV within the context of practical robotics problems.
7. Emphasis on Ethics and Safety: A dedicated chapter prepares students to be responsible engineers by addressing the critical issues of AI bias, safety standards, and ethical considerations in robotics.
My core philosophy is "simplicity in concept, depth in application." I believe that the most complex AI algorithms can be introduced through simple, intuitive examples. You won't find pages of dense mathematical proofs without context. Instead, you'll find a logical progression that starts with a real-world problem-like how a robot can distinguish between a fruit and a vegetable-and then introduces the AI tools, such as Convolutional Neural Networks (CNNs), required to solve it. We provide code snippets and pseudo-code that are easy to follow, encouraging students to get their hands dirty and experiment.
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