Jinhu Dong
Papers
5
Total Citations
82
H-Index
3
About
Jinhu Dong is a robotics researcher whose work lies at the intersection of autonomous navigation, motion planning, and robot perception, with a particular focus on enabling intelligent systems to operate efficiently in complex, real-world environments. His most influential contribution is a multiobjective scheduling strategy that integrates genetic algorithms with time-enhanced A* planning, achieving 59 citations for its practical solution to coordinating multiple parking robots in high-density unmanned lots—a critical challenge in modern urban infrastructure. Dong has also advanced robot learning by combining human demonstrations with nonlinear model predictive control, allowing mobile robots to acquire new skills through natural interaction. His recent work on MS-SLAM introduces a memory-efficient visual SLAM system that uses sliding window map sparsification to manage large-scale environments without sacrificing accuracy, while his fast online omnidirectional quadrupedal jumping framework pushes the boundaries of legged robot agility through virtual-model control and minimum jerk trajectory generation. Additionally, Dong has developed an efficient line-based registration algorithm for robot perception in large-scale structural scenes, addressing persistent challenges in point cloud registration. With over 80 total citations and a consistent focus on practical, scalable solutions, Dong’s research bridges the gap between theoretical robotics and real-world deployment, making him a notable contributor to autonomous systems engineering.
Research Focus
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Top Papers
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