Papers
2
Total Citations
18
H-Index
2
About
Youngbo Shim is a robotics researcher whose work centers on mobile robot navigation, obstacle detection, and autonomous collision avoidance systems. With a focus on developing practical, real-world solutions for robotic mobility, Shim has made meaningful contributions to the field of sensor-based navigation over the course of his career. His most recognized work, "Vision-Based Obstacle Detection and Avoidance: Application to Robust Indoor Navigation of Mobile Robots" (2008), has garnered 12 citations and introduced an innovative approach combining stereo camera projective invariants with two-dimensional scanning sensor data, enabling robots to build more informative environmental maps for safer indoor navigation. This sensor fusion methodology represented a significant step toward robust, practical obstacle detection systems. His later research, "Range Sensor-Based Efficient Obstacle Avoidance through Selective Decision-Making" (2018), with 6 citations, addressed persistent limitations in conventional avoidance algorithms — particularly instability in narrow passages and undesirable zig-zag motion patterns — by introducing a selective, strategy-driven decision-making framework. Shim's body of work reflects a consistent drive to bridge the gap between theoretical robotics and real-world deployment, making his research particularly valuable for engineers and students working on autonomous navigation systems.
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Top Papers
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