Vivekanand C Roodagi

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About

Vivekanand C Roodagi’s research lies at the intersection of artificial intelligence and autonomous systems, with a primary focus on indoor navigation and deep reinforcement learning. His most-cited work, “Indoor Navigation with Deep Reinforcement Learning” (2020, 11 citations), addresses a critical challenge: enabling reliable navigation within buildings where GPS signals fail. By applying deep reinforcement learning algorithms, Roodagi’s research advances solutions for robotics, drones, and gaming applications, offering more adaptive and efficient path-planning in complex indoor environments. His contributions are particularly notable for bridging the gap between theoretical reinforcement learning and practical deployment, demonstrating how agents can learn optimal navigation policies through trial and error in simulated or real-world spaces. While his citation count reflects a focused, emerging impact, Roodagi’s work is foundational for researchers exploring autonomous indoor mobility, especially in settings requiring real-time decision-making without external positioning systems. His findings have implications for warehouse automation, assistive robotics, and augmented reality, positioning him as a promising voice in the growing field of intelligent navigation.