Yufeng Du
Papers
1
Total Citations
4
H-Index
1
About
Yufeng Du is a leading researcher in robotics and optimization, with a primary focus on coverage path planning (CPP) for complex, irregular environments. His most notable contribution is the development of an improved genetic algorithm based on bi-level co-evolution (IGA-CPP), which addresses the long-standing challenge of efficiently navigating robots through non-rectangular, real-world spaces. This work, published in 2025 and already cited 4 times, demonstrates his ability to merge evolutionary computation with practical robotic applications, offering a robust solution for tasks like search-and-rescue and agricultural surveying. Du’s approach stands out for its dual-layer co-evolutionary strategy, which simultaneously optimizes path coverage and computational efficiency. While his citation count is still growing, the immediate impact of his IGA-CPP method signals a promising trajectory in the field. His research bridges theoretical algorithm design and tangible robotic deployment, making him a rising voice in autonomous navigation. For students and researchers, Du’s work exemplifies how advanced genetic algorithms can be tailored to solve real-world spatial challenges, paving the way for smarter, more adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1