Xinzhi Zhou

Sichuan University, Chengdu University of Information Technology

Papers

Total Citations

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About

Xinzhi Zhou is a leading researcher in mobile robotics and intelligent path planning, with a focus on overcoming the limitations of traditional optimization algorithms in complex, dynamic environments. Zhou’s major contributions center on advancing ant colony optimization (ACO) techniques to solve real-world navigation challenges. In their highly cited 2021 work, *Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment* (28 citations), Zhou introduced a novel time taboo strategy that dramatically improves convergence speed and global search capability, enabling robots to navigate unknown, time-varying obstacles effectively. Building on this, Zhou’s *Path Planning of Mobile Robot Based on Adaptive Ant Colony Optimization* (7 citations) further refined the approach by implementing adaptive initial pheromone distribution, addressing persistent issues of slow convergence and poor search performance. These innovations have significant implications for autonomous systems, logistics, and industrial automation, where efficient, real-time navigation is critical. Zhou’s work is recognized for bridging the gap between theoretical optimization and practical deployment, making them a notable figure in the field of intelligent robotics and computational intelligence.