Ajinkya Joglekar
Papers
1
Total Citations
6
H-Index
1
About
Ajinkya Joglekar is an emerging researcher specializing in autonomous systems, robotics, and intelligent control, with a particular focus on off-road vehicle autonomy and reinforcement learning-based control frameworks. His most notable work addresses one of the pressing challenges in autonomous vehicle research: adapting classical path-tracking algorithms for complex, unstructured off-road environments. In his 2022 paper, "Deep Reinforcement Learning Based Adaptation of Pure-Pursuit Path-Tracking Control for Skid-Steered Vehicles," Joglekar pioneered the integration of deep reinforcement learning with the widely-used pure-pursuit controller, enabling skid-steered vehicles to navigate challenging terrain with greater precision and adaptability. This contribution is significant because it bridges the gap between conventional geometric control methods — traditionally designed for structured road environments — and the dynamic demands of off-road autonomy. The work has garnered 6 citations since its publication, reflecting growing interest from the robotics and autonomous systems community. Joglekar's research represents a timely and practical advancement for applications in agriculture, defense, and search-and-rescue robotics, where reliable autonomous navigation in unstructured environments is increasingly critical.
Research Focus
Key Achievements
Top Papers
- 1