Papers
7
Total Citations
36
H-Index
4
About
Inseok Hwang is a robotics and autonomous systems researcher whose work spans state estimation, multi-robot coordination, UAV navigation, and safety-critical control. His research addresses fundamental challenges in making autonomous systems reliable, efficient, and secure across a range of complex environments. Hwang's early foundational contributions focused on hybrid systems estimation, with his State-Dependent-Transition Hybrid Estimation algorithm providing principled methods for tracking both continuous and discrete system states — work that has garnered 11 citations and remains relevant to complex dynamical systems. His 2017 paper on Invariant Kalman filtering for optical flow-based visual odometry in UAVs — his most-cited work with 12 citations — advanced robust, GPS-independent navigation for unmanned aerial vehicles. Complementing this, his 2023 open-source Gazebo plugin for GNSS multipath emulation offers the research community practical simulation tools for urban autonomous navigation. Beyond navigation, Hwang has explored bio-inspired robotics through evolutionary optimization of flapping-wing vehicles, reinforcement learning safety verification, and distributed multi-objective path planning for robot networks. His recent work on anchor-free integrity monitoring against cyberattacks reflects a growing focus on resilient multi-robot systems. Collectively, his research portfolio demonstrates a sustained commitment to bridging rigorous theoretical frameworks with real-world autonomous systems applications.
Research Focus
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Top Papers
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- 6Safety Verification of Model Based Reinforcement Learning Controllers2 citations · 2020
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