Papers
1
Total Citations
16
H-Index
1
About
Yibo Min is a researcher whose work lies at the intersection of robotics, swarm intelligence, and optimization algorithms. His most notable contribution is the development of an improved ant colony optimization (ACO) algorithm for dynamic path planning of mobile robots, published in 2019. This work addresses a critical limitation of traditional ACO—poor solution quality in changing environments—by fusing genetic operators into the algorithm. The innovation expands the search space and enhances adaptability, enabling robots to navigate complex, dynamic settings more efficiently. With 16 citations, this paper has become a reference point for researchers tackling real-time path planning challenges. Min’s approach demonstrates a practical synergy between evolutionary computation and bio-inspired heuristics, offering a robust framework for autonomous navigation. His research is particularly valuable for students and engineers working on mobile robotics, logistics automation, and intelligent transportation systems. By bridging theoretical algorithm design with applied robotics, Yibo Min contributes to the growing field of adaptive, nature-inspired solutions for dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1