Senthil Hariharan Arul
Papers
6
Total Citations
19
H-Index
3
About
Senthil Hariharan Arul is a robotics researcher specializing in autonomous navigation, multi-agent coordination, and human-robot interaction. His work addresses the fundamental challenge of enabling robots to move safely and efficiently through complex, dynamic environments—from cluttered indoor spaces to unstructured outdoor scenes. Arul’s most impactful contribution is DS-MPEPC, a model predictive control algorithm that achieves safe, deadlock-free robot navigation in crowded settings, earning 5 citations since 2023. He further advances multi-robot systems with a selective communication framework for reinforcement learning-based coordination, and with CGLR, a decentralized replanning method using congestion metrics for dense multi-agent navigation. In the realm of human-guided autonomy, Arul developed BehAV, which leverages Vision Language Models to interpret natural language instructions for outdoor robot navigation, and VLPG-Nav, a visual language approach for object-centric indoor navigation. His work on unconstrained model predictive control under uncertainty provides a probabilistic framework for robust navigation. Collectively, Arul’s research bridges perception, planning, and human-robot communication, pushing the frontier of autonomous navigation in real-world settings.
Research Focus
Key Achievements
Top Papers
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