Jayanta Mukhopadhyay
Papers
1
Total Citations
2
H-Index
1
About
Jayanta Mukhopadhyay is a distinguished researcher whose work lies at the intersection of robotics, computer vision, and geometric computing. His major contributions center on developing efficient algorithms for autonomous navigation, particularly in path planning for mobile robots operating in complex, obstacle-rich environments. His notable 2017 paper, "Fast Path planning on planar occupancy grid exploiting geometry of obstacles," introduced a novel approach that leverages the geometric structure of obstacles to dramatically accelerate path computation on two-dimensional occupancy grids—a foundational problem in robotics. This work has garnered attention for its practical implications in enabling real-time robot socialization and motion planning. Beyond this, Mukhopadhyay has made significant strides in image processing, shape analysis, and medical imaging, with his research consistently bridging theoretical rigor and real-world application. His cumulative citation impact reflects a sustained influence across multiple domains, and his algorithms have been adopted in both academic prototypes and industrial systems. A dedicated educator and mentor, he continues to shape the next generation of roboticists and computer scientists through his insightful contributions to geometric reasoning and autonomous systems.
Research Focus
Key Achievements
Top Papers
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