Papers
19
Total Citations
166
H-Index
7
About
P. Krishnamurthy is a leading researcher at the intersection of safe autonomous navigation, control theory, and adversarial robustness. Their work centers on developing mathematically rigorous frameworks—particularly Control Barrier Functions (CBFs)—to guarantee safety in robotic systems operating in unstructured environments. Krishnamurthy’s most impactful contribution is the introduction of differentiable optimization-based CBFs, enabling real-time collision avoidance and occlusion-free visual servoing, as demonstrated in their highly cited 2023 paper (37 citations) and its 2024 follow-up (21 citations). They have also pioneered novel approaches to sensor fusion, including sliding-window temporal attention architectures for robust UGV navigation (16 citations), and addressed critical vulnerabilities in learning-based autonomy, revealing how adversarial attacks on roadside billboards can dynamically alter autonomous vehicle trajectories (19 citations). With over 146 total citations across their top ten papers, Krishnamurthy’s work spans from humanoid robot navigation in unknown environments to semantic SLAM for indoor localization and real-time anomaly monitoring for assured autonomy. Their research is distinguished by its dual focus on advancing theoretical safety guarantees while addressing practical deployment challenges, making significant contributions to the safe integration of autonomous systems in real-world scenarios.
Research Focus
Key Achievements
Top Papers
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- 7Humanoid robot navigation and obstacle avoidance in unknown environments10 citations · 2013
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