Papers
10
Total Citations
86
H-Index
5
About
Sergey Popov is a researcher specializing in autonomous robotics, deep learning-based anomaly detection, and intelligent transportation systems. His most significant contributions lie at the intersection of neural network architectures and multi-robot system security, where he has pioneered novel approaches to identifying anomalous behavior in complex robotic environments. Popov's most influential work, garnering 20 citations, introduced autoencoder-based preprocessing for detecting anomalous behavior in robot systems, leveraging dimensionality reduction to enhance detection accuracy. Building on this foundation, he developed Siamese autoencoder architectures that preserve distances for anomaly detection in multi-robot systems, accumulating 17 and 11 citations respectively — work that represents a meaningful advance in information security control for sensor-rich robotic platforms. Beyond anomaly detection, Popov has made contributions to intelligent transportation systems through mobile service frameworks for ITS infrastructure (12 citations), multi-criteria path planning algorithms, and coordination strategies for heterogeneous robot groups combining ground rovers and aerial drones. His 2020 work on bricklaying robot trajectory planning demonstrates a practical engineering focus, applying algorithmic innovations to real-world construction automation challenges. With a research portfolio spanning deep learning, swarm robotics, and transportation intelligence, Popov's work offers valuable insights for students and researchers exploring autonomous systems security and multi-agent coordination.
Research Focus
Key Achievements
Top Papers
- 1
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- 3Adage mobile services for ITS infrastructure12 citations · 2013
- 4
- 5Bricklaying robot moving algorithms at a construction site9 citations · 2020
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- 8Multi-Criteria Path Planning Algorithm for a Robot on a Multilayer Map4 citations · 2018
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