G. Nirmala

Papers

Total Citations

H-Index

About

G. Nirmala’s research lies at the intersection of artificial intelligence and autonomous robotics, with a primary focus on mobile robot localization, navigation, and path planning. Her work addresses the fundamental challenge of enabling robots to operate intelligently in unknown, dynamic environments without prior knowledge. A key contribution is her application of reinforcement learning—specifically Q-learning—to guide mobile robots in indoor settings, as demonstrated in her 2011 paper on optimal path selection in grid environments. She further advanced this approach with an intellectual navigation method that requires only a learning signal, reducing the need for exhaustive environmental modeling. Her 2017 survey on mobile robot localization and navigation in AI, which has garnered 12 citations, provides a comprehensive overview of the field’s potential and challenges. While her citation counts reflect a focused, emerging impact, her work is foundational for researchers exploring learning-based navigation systems. Nirmala’s contributions are particularly notable for bridging theoretical reinforcement learning with practical robotic autonomy, offering scalable solutions for real-world deployment in unstructured spaces.