V. Ganapathy

Papers

Total Citations

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About

V. Ganapathy is a robotics researcher whose work sits at the intersection of artificial intelligence and autonomous navigation, with a primary focus on developing intelligent systems for area coverage in complex, unknown environments. His major contributions center on applying deep reinforcement learning to solve the challenging problem of coverage path planning—enabling robots to autonomously and efficiently cover entire spaces without prior knowledge of the environment. Ganapathy’s research addresses the NP-hard nature of coverage path planning by introducing novel approaches such as implicit cellular decomposition and online map-building, allowing robots to adapt their strategies in real-time as they explore. His most cited work, "Deep Reinforcement Learning Based Online Area Covering Autonomous Robot" (2021), has garnered 9 citations, demonstrating its relevance to the field. Alongside related publications on coverage in unknown dynamic environments, his cumulative work provides a robust framework for deploying autonomous robots in residential and commercial settings. Ganapathy’s research is particularly valuable for students and engineers working on practical robotics applications, bridging the gap between theoretical reinforcement learning algorithms and real-world autonomous navigation challenges.