Papers
1
Total Citations
2
H-Index
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About
Xing Yao is a robotics researcher whose work centers on intelligent path planning for robotic manipulators, with a particular focus on overcoming the limitations of sampling-based algorithms. Yao’s most notable contribution addresses the well-known shortcomings of the Rapidly-exploring Random Tree (RRT) algorithm—namely, non-smooth paths, low search efficiency, and poor adaptability in dynamic environments. In their 2025 paper, Yao proposed an improved RRT framework that enhances obstacle avoidance for robotic arms, offering a more efficient and adaptable solution for real-world automation tasks. While the work is still early in its citation lifecycle, it represents a meaningful step toward practical, robust motion planning in cluttered or changing workspaces. Yao’s research sits at the intersection of robotics, control theory, and computational geometry, with implications for manufacturing, logistics, and autonomous systems. As the field increasingly demands real-time, safe, and smooth robot motion, Yao’s contributions are poised to gain traction among engineers and researchers developing next-generation robotic systems.
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