K Rahul Sharma
Papers
9
Total Citations
63
H-Index
6
About
K. Rahul Sharma is a robotics and control systems researcher whose work spans mobile robotics, sensor fusion, and predictive control. With a career built around solving fundamental challenges in autonomous systems, Sharma has made notable contributions to how robots perceive, navigate, and control themselves in complex environments. His early work focused on sensor fusion techniques, most prominently demonstrated in his 2014 paper applying Kalman filters to predict robot orientation and obstacle proximity using multiple infrared sensors — his most cited work with 12 citations. This research addressed a critical bottleneck in reliable robot localization, offering a practical multi-sensor approach that influenced subsequent work in the field. Sharma's research then evolved toward advanced control strategies, particularly Model Predictive Control (MPC) for mobile robots. His investigations into trajectory tracking for non-holonomic and differentially steered robots — examining both kinematic and dynamic models — have collectively garnered over 28 citations, establishing him as a consistent voice in predictive robotics control. His extension of MPC to the classic rotary inverted pendulum problem further demonstrated his breadth across control applications. Complementing this, Sharma contributed to multi-robot exploration and path planning, including frontier-based area coverage and dynamic environment navigation using A* algorithms. Across roughly a dozen publications, his cumulative work reflects a researcher dedicated to bridging theoretical control methods with practical robotic implementation.
Research Focus
Key Achievements
Top Papers
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- 4Frontier Based Multi Robot Area Exploration Using Prioritized Routing8 citations · 2016
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- 8Model Predictive Control for rotary inverted pendulum using LabVIEW3 citations · 2019
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