Qinqin Fan
Papers
3
Total Citations
37
H-Index
3
About
Qinqin Fan is a rising leader in intelligent robotics and multi-agent systems, with a focused expertise in multi-robot task allocation (MRTA) and crowd evacuation in complex environments. Her most impactful work introduces a novel multimodal multi-objective evolutionary algorithm, enhanced by deep reinforcement learning, to solve the challenging problem of distributing tasks among multiple robots in dynamic, real-world settings. This research, published in 2023 and 2024, has already garnered over 30 citations, reflecting its immediate relevance to both academia and industry. Fan’s contributions are particularly notable for bridging optimization theory with practical robotic coordination, enabling systems to adapt to environmental constraints and decision-maker preferences. She has also pioneered an unmanned system-guided crowd evacuation method for large-scale, complex urban environments, addressing critical challenges in disaster response and public safety. By integrating evolutionary computation with reinforcement learning, Fan is shaping the next generation of autonomous, cooperative robotic systems. Her work stands at the intersection of artificial intelligence, operational research, and emergency management, promising significant advances in how robots collaborate under uncertainty.
Research Focus
Key Achievements
Top Papers
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