Mengqing Fan
Papers
1
Total Citations
9
H-Index
1
About
Mengqing Fan is a researcher in robotics and artificial intelligence, with a primary focus on multi-robot systems and path planning in complex environments. Their most notable contribution is the development of a genetic multi-robot path planning (GMPP) algorithm, which intelligently coordinates multiple robots navigating through obstacle-rich spaces. This work, published in 2021, addresses critical challenges such as path length optimization and safety feasibility in large-scale settings, offering a robust solution for real-world applications like warehouse automation and search-and-rescue operations. The paper has garnered 9 citations, reflecting its relevance in the growing field of swarm robotics. Fan’s research bridges theoretical optimization techniques—specifically genetic algorithms—with practical implementation, demonstrating how evolutionary computation can enhance multi-agent coordination. By tackling the trade-off between efficiency and safety, their work provides a foundation for future advancements in autonomous navigation. Fan’s contributions are particularly valuable for students and engineers seeking to understand scalable, decentralized approaches to robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1