Yinghui Xu
Papers
2
Total Citations
12
H-Index
2
About
Yinghui Xu is a researcher focused on multi-agent systems and intelligent automation, with key contributions in path planning and resource management for robotics and logistics. Their work on "Multi-agent Path Planning with Non-constant Velocity Motion" (2019, 9 citations) addresses a critical gap in the field by moving beyond simplified discretized models to incorporate realistic, variable-velocity motion for each agent—a significant step toward practical deployment in domains like warehouse robotics and transportation. This paper has been cited for its novel approach to balancing computational tractability with physical fidelity. Xu also explores energy efficiency in automated systems, as demonstrated in "Battery Management for Automated Warehouses via Deep Reinforcement Learning" (2020, 3 citations), where they apply reinforcement learning to optimize battery usage in large-scale logistics operations. This work highlights their versatility in bridging algorithmic innovation with real-world constraints. While still early in their career, Xu’s research is steadily gaining recognition for its practical relevance and technical depth, making them a promising voice in the evolution of autonomous multi-agent systems.
Research Focus
Key Achievements
Top Papers
- 1Multi-agent Path Planning with Non-constant Velocity Motion9 citations · 2019
- 2