Mingyong Liu
Papers
6
Total Citations
88
H-Index
6
About
Mingyong Liu is a leading researcher at the intersection of swarm robotics, underwater autonomous systems, and multi-agent game theory. His work fundamentally advances how robots coordinate in complex, dynamic environments—from collaborative trapping strategies using pattern formation to defense-intrusion games where robotic defenders synchronize intercept maneuvers. Liu’s most impactful contribution lies in revolutionizing underwater perception: he developed the YOLOv3-Marine and YOLOv3-UW algorithms, which dramatically improve the speed and accuracy of detecting and classifying small, dense underwater targets for intelligent robots. These innovations directly address critical challenges in marine exploration and defense, achieving high detection rates in cluttered scenes. His research on natural landmark extraction from 2D laser data also provides foundational methods for mobile robot navigation. With his top-cited paper on multi-target trapping garnering 31 citations and his YOLOv3-based work accumulating over 24 citations, Liu’s influence is steadily growing. His studies on alternative escape behaviors in robotics further showcase his ability to model biologically inspired decision-making. For students and researchers, Liu’s work offers a compelling blueprint for integrating perception, control, and strategy in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Multi-target trapping with swarm robots based on pattern formation31 citations · 2018
- 2
- 3
- 4Stay-eat or run-away: Two alternative escape behaviors11 citations · 2019
- 5Underwater Dense Targets Detection and Classification based on YOLOv39 citations · 2019
- 6