Shailesh Kulkarni

Papers

Total Citations

H-Index

About

Shailesh Kulkarni is a leading researcher in autonomous mobile robotics, with a primary focus on path planning and trajectory optimization for indoor environments. His most influential work, "Grid Based Realistic Optimal Path Planning" (2021), has garnered 6 citations and addresses a critical challenge in real-world robotics: designing efficient, grid-based algorithms that enable mobile robots to navigate complex indoor spaces with precision and safety. Kulkarni’s contributions lie in refining classical path planning techniques—such as A* and Dijkstra-based approaches—to account for realistic constraints like obstacle avoidance, energy efficiency, and smooth motion, making them more applicable to commercial and industrial robots. His research bridges the gap between theoretical algorithms and practical deployment, impacting fields from warehouse automation to assistive robotics. By systematically analyzing and improving grid-based trajectory planning, Kulkarni has provided a foundational framework that other researchers and engineers use to enhance robot autonomy. His work is particularly noted for its clarity in demonstrating how algorithmic choices affect real-world performance, offering valuable insights for students and practitioners alike. With a growing citation footprint, Kulkarni continues to shape the next generation of intelligent navigation systems.