Jonathan Lwowski
Papers
7
Total Citations
150
H-Index
6
About
Jonathan Lwowski is a robotics and artificial intelligence researcher whose work spans autonomous systems, computer vision, and multi-robot coordination. His research consistently bridges theoretical innovation with practical applications, addressing challenges in areas such as object tracking, swarm robotics, pedestrian detection, and indoor localization. Lwowski's most cited work, "Improved Deep Neural Network Object Tracking System for Applications in Home Robotics" (2018, 43 citations), demonstrates his early commitment to leveraging deep learning for real-world robotic deployment. His complementary research on pedestrian detection using convolutional neural networks (29 citations) has relevance across autonomous driving, surveillance, and search-and-rescue domains. Notably, his bird flocking-inspired formation control algorithm for unmanned aerial vehicles (30 citations) introduced a biologically motivated approach to coordinating UAV swarms with guaranteed image overlap — a creative fusion of nature and engineering. Lwowski has also contributed meaningfully to indoor localization, evaluating the precision of HTC Vive Tracker technology for GPS-denied environments (24 citations), and to task allocation strategies for heterogeneous robotic swarms operating on cloud networks. Across more than 150 cumulative citations, his body of work reflects a productive and versatile research career at the intersection of intelligent robotics and autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 4HTC Vive Tracker: Accuracy for Indoor Localization24 citations · 2020
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- 7Stereo Camera Based Formation Control for Unmanned Aerial Vehicles3 citations · 2018