Titas Bera
Papers
3
Total Citations
16
H-Index
3
About
Titas Bera is a researcher whose work lies at the intersection of probabilistic robotics and motion planning, with a particular focus on sampling-based algorithms for autonomous systems. His research addresses the fundamental challenge of navigating robots—especially unmanned aerial vehicles (UAVs)—through complex, constricted environments where narrow passages and obstacles pose significant difficulties. Bera’s major contributions include the development of an efficient random walk strategy for sampling-based motion planners and a Lévy flight-based probabilistic approach tailored for UAVs navigating through tunnels, windows, or mazes. His most cited work, "Analysis of Obstacle based Probabilistic RoadMap Method using Geometric Probability" (2014, 9 citations), provides a rigorous geometric probability framework for understanding how obstacles influence roadmap connectivity. With a cumulative citation count of 16 across his key papers, Bera’s research has advanced the theoretical underpinnings of motion planning while offering practical solutions for aerial inspection robotics in confined spaces. His innovative use of Lévy flights—a search pattern inspired by natural phenomena—demonstrates a creative approach to overcoming the narrow passage problem, making his work valuable for both roboticists and students exploring autonomous navigation in challenging environments.
Research Focus
Key Achievements
Top Papers
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