Cheng Tan
Papers
1
Total Citations
20
H-Index
1
About
Cheng Tan is a leading researcher in multirobot systems and bio-inspired autonomous navigation, with a focus on distributed coordination and coverage control in unknown environments. Their most cited work introduces a novel multirobot distributed collaborative region coverage search algorithm based on the Glasius bio-inspired neural network (GBNN), which addresses critical constraints in dynamic, unstructured settings. This algorithm has garnered 20 citations, reflecting its impact on advancing scalable, decentralized approaches for robotic search and exploration. Tan’s contributions are particularly notable for integrating neural network principles with multiagent coordination, enabling robust performance without centralized control. Their research holds significant promise for applications in disaster response, environmental monitoring, and autonomous exploration. By bridging theoretical bio-inspired models with practical robotic systems, Cheng Tan has established a strong foundation for future work in cooperative robotics, earning recognition among peers for innovative solutions to complex spatial coverage problems.
Research Focus
Key Achievements
Top Papers
- 1