Maithilee Nagesh Kulkarni

Papers

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About

Maithilee Nagesh Kulkarni’s research lies at the intersection of robotics, kinematics, and human-robot interaction, with a focus on making industrial automation more intuitive and accessible. Her most influential work, “Analysis of the inverse kinematics for 5 DOF robot arm using D-H parameters” (2017, 26 citations), introduces a novel algorithm that applies the Denavit-Hartenberg convention and a brute-force iterative method to solve the complex non-linear equations governing a five-degree-of-freedom robotic arm. This contribution provides a practical, accurate framework for motion planning in robotic manipulators. In a complementary vein, her paper “Gesture based control of IRB1520ID using Microsoft’s Kinect” (2017, 7 citations) pioneers a real-time, vision-based control system that translates human skeletal data into commands for an ABB industrial robot. By processing Kinect sensor frames in MATLAB, Kulkarni’s work demonstrates how natural gestures can replace traditional programming interfaces, lowering the barrier for non-expert operators. Together, these contributions showcase her ability to bridge theoretical kinematics with applied human-robot collaboration, offering tangible solutions for safer, more flexible manufacturing environments. Her research continues to inspire students and engineers exploring intuitive robot control and advanced kinematic modeling.