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About

Sourabh Bhattacharya is a robotics and autonomous systems researcher whose work spans motion planning, visual control, multi-robot coordination, and precision agriculture. Early in his career, he made foundational contributions to visibility-constrained navigation, developing optimal path planning strategies for differential-drive robots operating under limited field-of-view sensing—work that garnered over 114 citations and laid the groundwork for practical autonomous navigation. He further advanced the field through homography-based visual servoing, enabling nonholonomic robots to navigate efficiently using only camera imagery, accumulating nearly 100 citations across related publications. His research extended into pursuit-evasion problems, swarm intelligence-driven source seeking, and game-theoretic approaches to multi-robot resource allocation, including charging station assignment strategies. More recently, Bhattacharya has pivoted toward agricultural robotics, designing multi-robot phenotyping systems for row crop data collection and applying deep learning to soybean yield estimation through multiview image fusion—work that has quickly attracted significant attention in the plant breeding community. Across his career, his publications reflect a consistent thread: bridging theoretical rigor in robot motion and control with impactful real-world deployment, making him a notable contributor to both foundational robotics and its emerging agricultural applications.