Yanming Quan

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About

Yanming Quan is a researcher specializing in robotics, autonomous navigation, and multi-agent coordination, with a focus on improving the efficiency and stability of robotic systems in complex, real-world environments. His work addresses critical challenges in motion control and collaborative scheduling, particularly for automated guided vehicles (AGVs) and warehouse robotics. Quan’s most cited paper, “AGV Motion Balance and Motion Regulation Under Complex Conditions” (2022, 11 citations), introduces novel control strategies to maintain stability and precise movement in challenging terrains, a key contribution to industrial automation. He also developed an improved fruit fly optimization algorithm for object pose estimation in accommodation spaces (2018, 5 citations), demonstrating innovative bio-inspired approaches to perception. Additionally, his simulation of a multi-robot cooperative scheduling system based on ROS (2020) provides a practical framework for managing warehouse fleets using 3D modeling and virtual coordination, directly addressing real-world logistics needs. With a growing citation record, Quan’s work bridges theoretical optimization and applied robotics, offering valuable insights for students and researchers in autonomous systems, swarm robotics, and industrial automation.