Chuyi Song

Inner Mongolia University for Nationalities

Papers

Total Citations

H-Index

About

Chuyi Song is a researcher in robotics and computational intelligence, with a primary focus on autonomous navigation and optimization algorithms. Their most cited work, "Robotic Path Planning Based on Improved Ant Colony Algorithm" (2019, 6 citations), introduces a novel enhancement to the classic ant colony optimization (ACO) method, specifically tailored for robotic path planning in complex environments. By refining the pheromone update mechanism and heuristic function, Song’s algorithm significantly improves convergence speed and path optimality, addressing key limitations in traditional ACO approaches. This contribution has practical implications for mobile robots, autonomous vehicles, and industrial automation, where efficient and collision-free navigation is critical. While their citation count is modest, the work demonstrates a strong foundation in bio-inspired computing and its application to real-world robotics challenges. Song’s research bridges theoretical algorithm design and practical implementation, offering a scalable solution for dynamic and obstacle-rich settings. Their work is particularly valuable for students and researchers exploring swarm intelligence, path planning, and the intersection of nature-inspired algorithms with robotic systems.