Anway Shirgaonkar
Papers
1
Total Citations
3
H-Index
1
About
Anway Shirgaonkar is a robotics researcher whose work centers on advancing autonomous navigation for mobile robots, particularly in challenging, unstructured environments. His primary contributions lie at the intersection of stochastic model predictive control (MPC) and data-driven learning, addressing the critical challenge of modeling complex, nonlinear tire-terrain interactions. In his most cited work, "Data-Driven Sampling Based Stochastic MPC for Skid-Steer Mobile Robot Navigation" (2025, 3 citations), Shirgaonkar introduces a novel framework that enhances a dynamic unicycle model with Gaussian Process (GP) regression. This approach effectively captures the elusive dynamics of skid-steer robots during high-speed maneuvers, where traditional models fail. By integrating real-time data with probabilistic control, his method improves both safety and performance in uncertain terrains. Though early in his career, these contributions are already shaping the future of robust, adaptive robotic navigation. Shirgaonkar’s work is particularly notable for bridging theoretical control methods with practical, data-efficient solutions—a key step toward deploying autonomous robots in real-world, off-road settings.
Research Focus
Key Achievements
Top Papers
- 1