Hongzhan Yu

Papers

Total Citations

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About

Hongzhan Yu is a robotics researcher specializing in safe autonomous navigation, with a focus on dynamic obstacle avoidance and human-robot interaction. Their most cited work, "Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance" (2023, 13 citations), addresses a critical bottleneck in scaling robot navigation: the exponential complexity of planning around multiple moving obstacles. Yu introduces a data-driven approach using neural barrier functions that sequentially handle obstacle interactions, enabling real-time, scalable control without requiring analytical models of complex dynamics. This work has significant implications for deploying robots in crowded, unpredictable environments. More recently, Yu's 2025 paper "Safe Human Robot Navigation in Warehouse Scenario" (4 citations) tackles the pressing challenge of integrating autonomous mobile robots (AMRs) into shared industrial spaces. By proposing a novel methodology for ensuring human worker safety, Yu directly addresses the operational and ethical demands of modern logistics. Their research bridges theoretical control theory and practical deployment, making contributions that are both technically rigorous and industrially relevant. Yu’s work is essential reading for researchers in robot safety, motion planning, and human-robot collaboration.