Papers
7
Total Citations
65
H-Index
5
About
Jishnu Keshavan is a researcher whose work spans autonomous systems, bioinspired perception, neural control architectures, and data-driven robotics. He first gained recognition for his pioneering work on bioinspired approaches to small-object detection and avoidance, drawing on biological vision principles to address fundamental limitations of machine-vision systems in resource-constrained autonomous vehicles — work that has accumulated over 35 citations across two related publications from 2018 and 2019. His research has since expanded into sophisticated control theory, where he developed novel zeroing neural network frameworks for solving time-varying underdetermined systems with prescribed performance constraints, earning 12 citations for that 2022 contribution. More recently, Keshavan has tackled the challenge of nonlinear robotic control through adaptive Koopman operator-theoretic embeddings, enabling robust linear control techniques to be applied to complex dynamical systems. His 2024 contributions further demonstrate his breadth, including approximation-free tracking control for redundant manipulators and a collision-cone formulation for control barrier functions applicable to both ground and aerial vehicles. With a growing body of work totaling over 65 citations, Keshavan represents an emerging voice bridging bioinspired perception, neural computation, and rigorous control theory in modern robotics.
Research Focus
Key Achievements
Top Papers
- 1Bioinspired Approaches for Autonomous Small-Object Detection and Avoidance27 citations · 2019
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- 4Autonomous Bio-Inspired Small-Object Detection and Avoidance8 citations · 2018
- 5
- 6A Collision Cone Approach for Control Barrier Functions4 citations · 2024
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