Yonglei Liu
Papers
3
Total Citations
83
H-Index
3
About
Yonglei Liu is a leading researcher in mobile robotics, specializing in path planning algorithms for complex and dynamic environments. His work addresses critical limitations in traditional approaches—such as slow convergence, poor path quality, and susceptibility to local optima—by developing novel, adaptive solutions. Liu’s most influential contribution is the adaptive genetic algorithm based on collision detection (AGACD), which significantly improves path quality and convergence speed for mobile robots, earning 47 citations. He further advanced the field with the CERRT algorithm, a variant of rapidly-exploring random tree (RRT) designed for complex environments, achieving 24 citations for its enhanced environmental sensitivity and efficiency. Additionally, his improved ant colony algorithm tackles challenges in dynamic and unknown settings, garnering 12 citations. Collectively, Liu’s work has been cited over 80 times, reflecting its impact on both theoretical and practical robotics. His algorithms are widely applicable in autonomous navigation, search-and-rescue, and industrial automation, making him a key figure in advancing intelligent, real-world robotic path planning.
Research Focus
Key Achievements
Top Papers
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