Neel Puri
Papers
2
Total Citations
5
H-Index
2
About
Neel Puri is an emerging researcher specializing in robotics, with a particular focus on cable-driven parallel robots (CDPRs) and state estimation techniques. His work centers on solving one of the fundamental challenges in CDPR operation: accurately determining the pose — position and orientation — of a robot's end-effector in real time. Puri has made notable contributions by developing sophisticated estimation frameworks that combine classical filtering approaches with nonlinear optimization. His 2022 paper introduced the application of Kalman filtering alongside forward kinematics error covariance bounds for CDPR pose estimation, garnering 3 citations, while his 2023 follow-up work proposed a novel coupled framework integrating an Extended Kalman Filter (EKF) with a nonlinear least-squares forward kinematics algorithm, utilizing end-effector-mounted accelerometer and sensor fusion data to enhance accuracy. Together, these papers have accumulated 5 citations, reflecting growing interest in his methodologies within the robotics community. Though early in his research career, Puri's contributions address practical challenges in CDPR deployment across applications such as construction, rehabilitation, and large-scale manipulation, positioning him as a promising voice in the field of parallel robotics and estimation theory.
Research Focus
Key Achievements
Top Papers
- 1
- 2