Papers
5
Total Citations
86
H-Index
4
About
Anup Parikh’s research lies at the intersection of robotics, control theory, and autonomous perception, with a focus on enabling reliable target tracking and visual servo control under challenging, real-world conditions. His major contributions address a fundamental problem in vision-based robotics: maintaining accurate state estimates when visual measurements are intermittent or lost entirely. In his most-cited work (2018, 36 citations), Parikh introduced a motion model learning approach that allows a camera-equipped robot to continue tracking a moving target even during occlusions or when the target leaves the field of view. He extended this line of inquiry with a switched systems framework (2017, 27 citations) that provides formal dwell-time guarantees for convergence of pose estimates. Earlier, his work on unified tracking and regulation visual servo control for wheeled mobile robots (2013, 16 citations) demonstrated a novel method for handling both trajectory following and fixed-position regulation using a single monocular camera. More recently, Parikh has advanced autonomous physical security systems and rapid semantic mapping for high-level robot autonomy. His work is notable for bridging rigorous control-theoretic analysis with practical robotic applications, making his methods both provably stable and deployable in complex environments.
Research Focus
Key Achievements
Top Papers
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- 4Autonomous Detection and Assessment with Moving Sensors4 citations · 2020
- 5Rapid Autonomous Semantic Mapping3 citations · 2020