Bandita Das

Papers

Total Citations

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About

Bandita Das is a researcher in robotics and computer vision, with a focus on vision-guided automation and industrial assembly systems. Her work centers on developing and comparing algorithms that enable robots to accurately identify and manipulate parts in manufacturing environments. Her most-cited paper, "Comparison of Edge Detection Algorithm for Part Identification in a Vision Guided Robotic Assembly System" (2014), has garnered 5 citations, establishing a foundational contribution to the field of robotic part localization. This study systematically evaluates edge detection techniques—critical for object recognition in automated assembly—providing practical insights for optimizing vision-based robotic guidance. While her citation count reflects a focused, early-career impact, Das’s work addresses a key challenge in Industry 4.0: bridging the gap between computer vision algorithms and real-time robotic control. Her research is particularly valuable for students and engineers seeking to understand how algorithmic choices affect system reliability in automated manufacturing. By emphasizing comparative analysis, Das contributes to the broader goal of making robotic assembly more adaptive and efficient, laying groundwork for future advancements in intelligent manufacturing systems.