Papers
4
Total Citations
49
H-Index
3
About
Subhranil Das is a rising researcher in autonomous mobile robotics, with a focused expertise in machine learning-driven path planning and collision avoidance. His most impactful work, "A Machine Learning approach for collision avoidance and path planning of mobile robot under dense and cluttered environments" (2022, 41 citations), establishes a core contribution: developing intelligent navigation systems that enable robots to operate safely in complex, obstacle-rich settings. This paper, his most cited, addresses a fundamental challenge in the field—balancing real-time decision-making with safety in unpredictable environments. Das further explores state estimation techniques for collision avoidance (2023, 3 citations) and has advanced the field with his latest work on adaptive stochastic gradient descent combined with least angle regression for enhanced navigation (2025, 1 citation). His research trajectory shows a clear progression from foundational path-tracing implementations (2021) to sophisticated, learning-based navigation models. With a growing citation footprint, Das is establishing himself as a contributor to the next generation of autonomous systems, particularly for industrial and manufacturing applications where reliable, intelligent navigation is critical.
Research Focus
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Top Papers
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