Zhongya Wang
Papers
6
Total Citations
65
H-Index
5
About
Zhongya Wang is a leading researcher in multi-robot systems, with a focus on bio-inspired optimization, cooperative control, and task allocation. Her work addresses fundamental challenges in coordinating multiple autonomous agents, from path planning and target hunting to nanorobot swarms for medical applications. Wang’s most cited paper (20 citations) introduces a novel multi-objective artificial bee colony algorithm for multi-robot path planning, optimizing foraging mechanisms and crowding distance calculations. She has also pioneered the application of game theory to multi-robot target hunting (15 citations), establishing coordinated hunting models with obstacle avoidance and search strategies. Her market-based task allocation algorithm (12 citations) improves task evaluation by integrating distance fitness and time urgency. Notably, Wang has extended multi-robot cooperation principles to nanomedicine, developing a Quorum Sensing algorithm that controls nanorobot population and drug concentration in cancer areas (7 citations). Her work consistently demonstrates how nature-inspired algorithms—from bee colonies to bacterial communication—can solve complex coordination problems across scales, from macroscopic robot teams to microscopic medical nanorobots.
Research Focus
Key Achievements
Top Papers
- 1
- 2Coordinated multi-robot target hunting based on extended cooperative game15 citations · 2015
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- 6Multi-robot target hunting based on dynamic adjustment auction algorithm5 citations · 2016