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About

D. Tamilselvi is a robotics researcher whose work focuses on intelligent navigation for mobile robots in indoor environments. Her core contributions lie in applying computational intelligence and reinforcement learning techniques to solve the fundamental challenge of autonomous path planning and obstacle avoidance. She pioneered the use of **Fuzzy logic controllers** for sensor-based navigation, enabling robots to make reliable decisions under uncertainty—a method that remains her most influential work with 35 citations. Tamilselvi further advanced the field by introducing **Q-learning** for optimal path selection in grid environments, allowing robots to learn navigation policies without prior environmental knowledge. Her research also explores **Genetic Algorithms** for finding optimal routes and **Dynamic Programming** for real-time collision avoidance with moving obstacles. By systematically addressing navigation challenges from static to dynamic environments, Tamilselvi has contributed foundational frameworks that bridge classical control theory with modern machine learning approaches. Her work continues to inform researchers developing autonomous systems for warehouses, hospitals, and other indoor settings where reliable robot navigation is critical.