Papers
2
Total Citations
10
H-Index
2
About
J Vaishnavi is a robotics researcher whose work focuses on solving the critical challenge of inverse kinematics (IK) for robotic manipulators—a fundamental problem in automation, manufacturing, and surgical robotics. Her research bridges conventional meta-heuristic techniques and modern deep learning approaches to compute precise joint angles for desired end-effector positions. Her most cited paper, "Inverse Kinematics Solution for 5-DoF Robotic Manipulator using Meta-heuristic Techniques" (2021, 7 citations), addresses the complexity of IK solutions for five-degree-of-freedom manipulators, a key component in industrial automation. Building on this, her 2022 work "Deep Learning Framework for Inverse Kinematics Mapping for a 5 DoF Robotic Manipulator" (3 citations) introduces neural network-based control methods to enhance manipulator accuracy and safety. Together, these contributions demonstrate a systematic progression from optimization-based to learning-based approaches in robotic control. Vaishnavi's work is particularly notable for its practical relevance: by improving IK solution efficiency, her research directly supports safer, more reliable automation in manufacturing, surgery, and transport applications. Her dual focus on meta-heuristic and deep learning frameworks positions her as a researcher advancing the frontier of intelligent robotic manipulation.
Research Focus
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Top Papers
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