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About

Dr. S. Mercy Shalinie is a leading researcher in the field of mobile robotics and artificial intelligence, with a primary focus on autonomous navigation and path planning in complex indoor environments. Her work is foundational in applying reinforcement learning and evolutionary algorithms to enable robots to navigate without prior environmental knowledge. In her highly cited 2011 paper, she pioneered the use of Q-learning for optimal path selection in a 10×10 grid environment, allowing mobile robots to calculate Q-values based on current and future discounted rewards for collision-free movement. Her 2012 study further advanced this domain by employing Genetic Algorithms for optimal path selection, demonstrating robust performance in static indoor settings. Earlier, in 2009, she introduced a Dynamic Programming approach for real-time navigation with moving obstacles, providing a global optimal solution for collision-free robot path planning. With over 22 citations across her key works, Dr. Shalinie’s contributions have significantly influenced the development of intelligent, adaptive navigation systems. Her research remains essential reading for students and engineers working on autonomous robotics, reinforcement learning applications, and real-time path optimization in dynamic environments.