Yue Cao

Guangdong University of Petrochemical Technology

Papers

Total Citations

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About

Yue Cao is a researcher whose work lies at the intersection of robotics, artificial intelligence, and optimization algorithms, with a particular focus on autonomous navigation and path planning. Their most notable contribution is the development of a robot path-planning method based on an improved genetic algorithm, a 2024 paper that has already garnered 4 citations. This work addresses a critical challenge in robotics: enabling efficient, collision-free movement in complex environments. By refining traditional genetic algorithms—enhancing convergence speed and solution quality—Cao has provided a practical framework for real-time robotic navigation, with implications for industrial automation, autonomous vehicles, and service robotics. The approach balances exploration and exploitation, reducing computational overhead while maintaining robustness. Though early in their career, Cao’s research demonstrates a clear impact, offering a scalable solution that bridges theoretical optimization and applied robotics. Their work is particularly relevant for students and researchers seeking to understand how evolutionary algorithms can be tailored for dynamic, real-world tasks. As the field of autonomous systems grows, Cao’s contributions represent a stepping stone toward more adaptive and intelligent robotic behavior.