Sashwata Banerjee
Papers
2
Total Citations
11
H-Index
2
About
Sashwata Banerjee is a robotics researcher whose work bridges deep learning and intelligent control for robotic manipulation. His primary research areas include computer vision-based object detection, robotic grasping and sorting, and fuzzy logic-based kinematic modeling. Banerjee’s most cited work, “AlexNet based Real-Time Detection and Segregation of Household Objects using Scorbot” (2020, 9 citations), introduces a practical system that combines a monocular camera with an AlexNet convolutional neural network to classify household objects before guiding a Scorbot-ER 5 Plus robotic arm in pick-and-place tasks—demonstrating a cost-effective, real-time approach to automated sorting. In a complementary study, “Fuzzy Membership Functions in ANFIS for Kinematic Modeling of 3R Manipulator” (2022, 2 citations), he applies adaptive neuro-fuzzy inference systems to model the complex kinematics of a three-revolute-joint manipulator, enhancing precision in trajectory planning. Banerjee’s contributions are notable for integrating classical control theory with modern deep learning, offering scalable solutions for domestic robotics and industrial automation. His work has been cited in subsequent research on intelligent grasping and fuzzy robotics, marking him as an emerging voice in applied robotic intelligence.
Research Focus
Key Achievements
Top Papers
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