Sudip Chandra Gupta
Papers
2
Total Citations
3
H-Index
1
About
Sudip Chandra Gupta is a robotics and control systems researcher whose work focuses on intelligent mobile robot navigation and embedded control architectures. His key research areas include computer vision for human-robot interaction, nonlinear control design, and real-time embedded systems. In his most cited work, Gupta proposed a novel convolutional neural network (CNN)-based tracking system for human-following mobile robots, integrating Mask R-CNN and YOLOv2 architectures with a Linear Quadratic Gaussian (LQG) control framework to address persistent challenges in visual object tracking such as environmental and object clutter. His second major contribution presents a comprehensive comparative analysis of linear PID and nonlinear LQR controllers for skid-steer mobile robots, with a focus on practical embedded implementation. Though early in his citation impact, Gupta’s work bridges advanced deep learning perception with robust control theory, offering practical pathways for deploying autonomous robots in real-world settings. His research is particularly relevant for students and engineers interested in the intersection of computer vision, control systems, and embedded hardware for mobile robotics.
Research Focus
Key Achievements
Top Papers
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