Meiyi Li
Papers
2
Total Citations
8
H-Index
2
About
Meiyi Li’s research lies at the intersection of computational intelligence, robotics, and bio-inspired algorithms, with a particular focus on adaptive navigation and path planning for mobile robots operating in dynamic and partially unknown environments. Her most influential work introduces a hybrid immune evolutionary computation framework that integrates principles from immunology and clonal selection theory. This approach, detailed in her 2005 paper (5 citations), addresses the concurrent mapping and localization problem by leveraging the adaptive and memory-driven properties of artificial immune systems. In a complementary study (3 citations), Li pioneers an immune-evolutionary planning algorithm enhanced with instance learning, which allows robots to reuse successful path fragments from past experiences. By combining evolutionary mechanisms with immune operators, her method efficiently generates near-optimal global paths, even when environmental conditions change. This work demonstrates how past knowledge can be systematically encoded and exploited to improve real-time decision-making. Li’s contributions are notable for bridging biological inspiration with practical robotics, offering a robust alternative to traditional planning methods. Her research remains a valuable reference for scholars exploring immune-based computation, evolutionary robotics, and adaptive autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 2