Umesh Kumar Sharma
Papers
5
Total Citations
84
H-Index
4
About
Umesh Kumar Sharma is a leading researcher in the field of cable-driven parallel robots (CDPRs), with a particular focus on their application in large-scale 3D printing and additive manufacturing. His work addresses the fundamental challenges of kineto-static analysis, workspace optimization, and control of these complex, redundant systems. Sharma’s major contributions include pioneering the use of neural networks for both inverse and forward kineto-static solutions of CDPRs, effectively handling the nonlinearities introduced by cable mass and elasticity—a critical step for real-world deployment. His research has garnered significant attention, with his most cited paper, "Inverse and Forward Kineto-Static Solution of a Large-Scale Cable-Driven Parallel Robot using Neural Networks," accumulating 29 citations. He has also made notable advances in workspace analysis, designing large-scale printing robots while considering cable mass and mobile platform orientation (21 citations), and developing optimization techniques for miniaturizing planar cable-driven 3D printers. By integrating machine learning with classical robotics, Sharma is enabling more precise, scalable, and practical cable-driven systems for construction and manufacturing.
Research Focus
Key Achievements
Top Papers
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- 5Miniaturization of a Planar Cable-Driven 3D Printer using Optimization3 citations · 2020