Kaustav Mondal
Papers
4
Total Citations
27
H-Index
4
About
Kaustav Mondal is a robotics and control systems researcher whose work centers on the modeling, design, and autonomous control of mobile robotic platforms, with a particular emphasis on non-holonomic differential drive systems. His research addresses fundamental challenges at the intersection of motion planning, dynamic modeling, and advanced control theory, making meaningful contributions to both theoretical understanding and practical implementation. Mondal's most influential work explores the trade-offs inherent in model predictive control (MPC) strategies for trajectory tracking, proposing innovative hierarchical control architectures that pair MPC outer-loops with PI inner-loops to achieve robust performance. His 2019 paper on kinematic versus dynamic model-based MPC has garnered 10 citations, reflecting its relevance to practitioners navigating real-world control design decisions. Complementing this, his 2020 study on stability versus maneuverability — cited 8 times — offers a nuanced analysis of how vehicle design parameters directly influence a robot's agility and stability envelope. Beyond single-robot systems, Mondal has tackled the ambitious challenge of multi-robot coordination through his two-part series on low-cost differential drive vehicles, contributing to the FAME project's vision of cooperative autonomous fleets. Collectively, his body of work provides accessible, practically grounded insights for researchers and engineers advancing autonomous ground vehicle technology.
Research Focus
Key Achievements
Top Papers
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