Qiang Liang

Papers

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About

Qiang Liang is a robotics researcher whose work focuses on intelligent automation, path planning, and machine vision systems. His research addresses critical challenges in industrial robotics, particularly in improving the efficiency and adaptability of automated systems. Liang's most cited paper, "Path Planning for Yarn Changing Robots Based on NRBO and Dynamic Obstacle Avoidance Strategy" (2024, 5 citations), introduces a novel fusion algorithm that overcomes the limitations of traditional bionic algorithms—such as inefficient search processes and poor dynamic obstacle avoidance—by combining the Newton-Raphson-based optimizer with the dynamic window approach. This work has significant implications for textile manufacturing automation. In his earlier study, "Design and research of automatic plug-in system based on machine vision" (2017, 2 citations), Liang developed a SCARA robot platform integrating monocular vision for automatic identification and positioning, transforming coordinate data from CCD cameras into actionable robotic commands. Through these contributions, Liang demonstrates a commitment to advancing robotic autonomy and precision in real-world industrial applications, laying groundwork for smarter, more responsive manufacturing systems.