Papers
5
Total Citations
33
H-Index
3
About
Yongxing Hao’s research spans the frontiers of multi-robot systems, autonomous agriculture, and environmental robotics, with a focus on real-time trajectory planning and intelligent perception. His most influential work, “Differential flatness-based trajectory planning for multiple unmanned aerial vehicles using mixed-integer linear programming” (2005, 16 citations), pioneered a method for generating fuel-optimal, collision-free trajectories for UAV swarms in dynamic environments—a foundational contribution to cooperative control. More recently, his 2023 study “Underwater Waste Recognition and Localization Based on Improved YOLOv5” (8 citations) addresses the urgent challenge of plastic pollution by enabling robots to accurately detect and locate underwater debris, advancing autonomous cleanup technologies. Earlier work includes developing a robotic simulation for autonomous small grain harvesting systems (2003, 4 citations), demonstrating the potential of multi-vehicle coordination in precision agriculture, and designing attitude algorithms for portable mobile robots using quaternion-based updates (2007, 3 citations). Hao also proposed a practical framework for formation planning of multiple unmanned ground vehicles (2004, 2 citations), leveraging group flatness to simplify trajectory generation. His contributions bridge theoretical control methods with real-world applications in aerial, ground, and underwater robotics, making him a versatile figure in autonomous systems research.
Research Focus
Key Achievements
Top Papers
- 1
- 2Underwater Waste Recognition and Localization Based on Improved YOLOv58 citations · 2023
- 3
- 4Attitude Algorithm of Portable Mobile Robot3 citations · 2007
- 5