Papers
7
Total Citations
89
H-Index
3
About
Lianghong Wu is a robotics researcher whose work spans neural network optimization, swarm robotics, and autonomous navigation. His most impactful contribution is the development of a predefined fixed-time convergence Zeroing Neural Network (ZNN) model, which introduced a power piecewise activation function for solving time-varying quadratic programming problems—a paper that has garnered 63 citations. This work has direct applications in dual-arm manipulator cooperative trajectory tracking, demonstrating his ability to bridge theoretical advances with practical robotic systems. In underwater robotics, Wu proposed a lightweight target detection algorithm combining dynamic sampling transformers with knowledge distillation optimization, addressing the challenge of accurate perception in low-quality optical environments. His research on swarm robotics includes self-organizing hunting strategies in unknown environments with dynamic obstacles, as well as multi-target search algorithms using triangular cone formations in complex nonconvex obstacle spaces. More recently, Wu has contributed to SLAM performance analysis across campus environments and developed risk-aware navigation systems that integrate semantic costmaps from RGBD sensors. His work on improved adaptive Monte Carlo localization, incorporating virtual motion models with NDT and EKF, further advances robot localization reliability. With publications spanning 2015 to 2025, Wu continues to push boundaries in intelligent robotic perception, coordination, and control.
Research Focus
Key Achievements
Top Papers
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- 4Visual and LiDAR SLAM Performance in Campus Scenes3 citations · 2024
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