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This document discusses the optimization of fuzzy logic controllers for mobile robot path planning using reinforcement learning, specifically Q-learning. It highlights the challenges in designing effective fuzzy controllers and the advantages of using learning algorithms to improve robot navigation and obstacle avoidance. The chapter presents a systematic approach to enhance robot behaviors through optimized fuzzy logic systems, demonstrating significant performance improvements in autonomous path planning
- Author
- rabykv
- Language
- EN