Sarvesh Prajapati
Papers
3
Total Citations
13
H-Index
3
About
Sarvesh Prajapati is a roboticist advancing safe, high-speed autonomy for mobile robots operating on challenging off-road and unstructured terrains. His research centers on motion planning and control under uncertainty, with a particular focus on skid-steer wheeled mobile robots (SSWMRs) and legged systems. Prajapati’s key contributions include developing a probabilistic motion model that uses Gaussian Process Regression and Sigma-Point Transforms to capture the complex skidding and slipping dynamics of SSWMRs during aggressive maneuvers—a critical step toward reliable off-road navigation. He has also pioneered a chance-constrained convex Model Predictive Control (MPC) framework for quadrupedal locomotion, enabling robust performance under parametric and additive uncertainties without extensive manual tuning. Additionally, his data-driven sampling-based stochastic MPC integrates GP-enhanced dynamic models to improve navigation safety and efficiency. With over a dozen citations across his most-cited works published in 2024–2025, Prajapati’s research directly addresses the gap between theoretical control methods and real-world robotic deployment, making him a rising voice in robust, uncertainty-aware autonomy for field robotics.
Research Focus
Key Achievements
Top Papers
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