Papers
4
Total Citations
74
H-Index
4
About
Teham Bhuiyan is an emerging robotics researcher whose work sits at the intersection of autonomous navigation, deep reinforcement learning (DRL), and motion planning for both mobile and industrial robots. His most influential contribution, "Arena-Bench" (2022, 38 citations), introduced a comprehensive benchmarking suite that bridges the gap between simulation-based learning approaches and real-world dynamic environments — a critical challenge in mobile robotics. Building on this foundation, Bhuiyan co-developed "Arena-Rosnav 2.0" (2023), an extended platform offering richer tools for developing and evaluating navigation algorithms in highly dynamic settings. Beyond mobile robotics, Bhuiyan has made meaningful strides in industrial robot planning. His work applying DRL to collision-free path planning using distance sensors (2023, 24 citations) challenges the dominance of traditional sampling-based algorithms like RRT and PRM, demonstrating that learning-based methods can achieve faster, more adaptive solutions in complex environments. His research on real-time motion planning for collaborative industrial environments further underscores this commitment to practical, deployable robotics solutions. With nearly 75 cumulative citations across recent publications, Bhuiyan is establishing himself as a notable contributor to next-generation robot autonomy and benchmarking methodology.
Research Focus
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Top Papers
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