Longlong Zhao

Chongqing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Longlong Zhao is a researcher specializing in robotics and intelligent optimization algorithms, with a particular focus on path planning for autonomous systems. His major contributions lie in enhancing the efficiency and robustness of robot navigation through hybrid algorithmic approaches. Zhao’s most-cited work, "3D path planning for a robot based on improved ant colony algorithm" (2020, 23 citations), addresses critical challenges in three-dimensional environments by refining the basic ant colony optimization method. Building on this, his paper "Path Planning for Robot Based on IACO-SFLA Hybrid Algorithm" (2020, 7 citations) introduces a novel fusion of the improved ant colony algorithm (IACO) with the shuffled frog leaping algorithm (SFLA), effectively overcoming issues of slow convergence, low efficiency, and susceptibility to local optima. This hybrid approach demonstrates Zhao’s innovative thinking in combining metaheuristic techniques to solve complex real-world problems. His work is particularly valuable for students and researchers in robotics, artificial intelligence, and optimization, offering practical solutions for autonomous navigation in challenging terrains. With a growing citation record, Zhao continues to contribute to advancing intelligent robotic systems.