Max Robotics — Global Service Robot Directory + US Certification

About

Gaurav Kewlani’s research lies at the intersection of mobile robotics, stochastic dynamics, and path planning under uncertainty. His work is distinguished by a rigorous, mathematically grounded approach to enabling robots to navigate unstructured and unpredictable environments. His most cited paper, “Stochastic mobility-based path planning in uncertain environments” (2009, 61 citations), introduces a framework for generating feasible, online trajectories that account for both dynamic constraints and environmental unpredictability—a critical capability for autonomous field robots. Kewlani further advanced this domain by pioneering the use of stochastic response surfaces and multi-element generalized polynomial chaos methods to statistically predict robot mobility and analyze dynamics under terrain and parameter uncertainty. These contributions, detailed in papers with 26 and 19 citations respectively, provide tools for robots to rapidly and accurately assess their own motion characteristics even when environmental information is imprecise. His work has been instrumental in shifting mobile robot planning from deterministic assumptions to robust, probabilistic frameworks, directly impacting the safety and efficiency of autonomous systems operating in real-world conditions.