Micro AI and Fuzzy Logic for Robotics and Expert Systems

Traditional control systems often struggle with the ambiguity and limited processing power of real-world robotics. By combining micro AI, expert systems, and fuzzy logic, you can build smart devices that make human-like decisions on low-power hardware. This course guides you through the foundational principles of rule-based systems and fuzzy inference without requiring advanced hardware. You will discover how to translate imprecise sensor data into precise control actions. What you'll learn: Understand the core concepts of expert systems and knowledge representation; Master fuzzy logic mathematics including fuzzification, membership functions, and defuzzification; Design rule-based inference engines optimized for microcontrollers and resource-constrained environments; Implement fuzzy logic controllers for autonomous navigation and obstacle avoidance; Apply modern micro AI techniques to optimize system performance and decision-making. We begin with essential terminology, historical context, and basic logic structures before moving into practical algorithmic design and control loop implementation. This course is designed for beginners, hobbyists, and aspiring robotics engineers who want to understand intelligent control from the ground up, with no prior AI experience required. Start building smarter, more adaptive micro-robotic systems today.