Papers
2
Total Citations
16
H-Index
2
About
Xingyu Yao is a rising researcher in robotics and agricultural automation, with a focus on intelligent manipulation and perception in unstructured environments. Their work bridges optimization algorithms and computer vision to enhance robotic efficiency and autonomy. Yao’s most cited paper introduces a novel trajectory planning method for a casting sorting robotic arm, leveraging a nature-inspired Genghis Khan shark optimized algorithm combined with segmented interpolation polynomials. This approach achieves time-optimal, smooth trajectories, addressing critical challenges in industrial automation. With 12 citations, this work highlights Yao’s contribution to heuristic optimization for robotic motion. Another notable study tackles real-time detection and instance segmentation of strawberries in unstructured settings, a key enabler for harvesting robots. By overcoming challenges like variable lighting and occlusions, Yao advances precision agriculture. Though early in their career, Yao’s research demonstrates significant impact, with citation counts reflecting growing recognition. Their work exemplifies the integration of bio-inspired algorithms and deep learning for practical robotics, promising future contributions to autonomous systems in both industrial and agricultural domains.
Research Focus
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Top Papers
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