Xianghua Liao

Guangxi University of Science and Technology

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About

Xianghua Liao is a robotics and agricultural automation researcher whose work bridges intelligent control systems and computer vision for practical applications. Her primary research areas include quadruped robot locomotion, trajectory planning, and fruit detection for agricultural robotics. Liao’s most impactful contribution is her development of a bionic impact-free foot-end trajectory generation algorithm for quadruped robots, which optimizes gait planning by combining workspace analysis with joint constraints. This work, published in 2022 and cited 4 times, addresses a fundamental challenge in legged robotics: achieving smooth, energy-efficient movement. In parallel, Liao has advanced agricultural automation through her 2023 study on orange detection using an improved Faster R-CNN algorithm. By integrating feature data analysis with deep learning, she enhanced the ability of picking robots to accurately identify ripe oranges in complex natural environments, a critical step toward reducing manual labor during harvest seasons. Though early in her career, Liao’s dual focus on robotic locomotion and vision-based fruit detection demonstrates a practical, interdisciplinary approach to solving real-world problems in both industrial and agricultural settings.