Papers
1
Total Citations
3
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1
About
Rahul Pol is a robotics researcher focused on advancing autonomous mobile robot navigation, particularly in indoor environments. His key contributions lie in developing realistic and optimal path planning algorithms that bridge the gap between theoretical models and real-world constraints. His most cited work, "Socio-realistic optimal path planning for indoor realtime autonomous mobile robot navigation" (2019), introduces the Realistic and Optimal Path Planning Algorithm (ROPPA), which improves system performance by coordinating multiple navigation modules concurrently. This research addresses critical challenges in autonomous navigation, such as dynamic obstacles and real-time decision-making, with 3 citations reflecting its foundational role in the field. Pol’s work emphasizes socio-realistic considerations—accounting for human-robot interaction and environmental unpredictability—making his algorithms more practical for deployment in crowded indoor spaces like hospitals or warehouses. His contributions are particularly valuable for students and researchers exploring efficient, real-world robotic navigation systems, as they offer a framework for integrating modular coordination with optimal path selection.
Research Focus
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Top Papers
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