Overview

The field of autonomous robotics is evolving rapidly, driven by advancements in artificial intelligence (AI), machine learning, and computational frameworks. At the heart of this evolution lies the concept of intelligent agents-software entities capable of perceiving their environment, reasoning about it, and making decisions to achieve specific goals. This book, Practical Agentic AI for Autonomous Robots using Python, aims to bridge the gap between theoretical concepts and real-world implementations by providing hands-on examples, Python-based simulations, and practical insights into building AI agents for robots that can think, learn, and act autonomously. Designed for students, engineers, and researchers, this book takes a step-by-step approach to constructing agent-based robotic systems, focusing on agent architectures, sensory integration, decision-making strategies, and multi-agent coordination. Through Python code and visualizations, readers will learn how to develop reactive, deliberative, and hybrid agents, implement real-time sensor processing, explore SLAM techniques, and apply AI frameworks like TensorFlow and PyTorch. Whether you're building a simple robot or experimenting with decentralized swarm intelligence, this book serves as a practical guide to creating robust, intelligent, and adaptable autonomous systems.

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