Papers
2
Total Citations
15
H-Index
2
About
Yuchen Tang is a researcher advancing the fields of path planning and human-robot collaboration. Their key contributions lie in developing faster, more efficient algorithms for autonomous navigation and enhancing safety in shared workspaces. Tang’s most cited work, “Late line‐of‐sight check and partially updating for faster any‐angle path planning on grid maps” (2019, 10 citations), introduces a novel optimization to classic path planning methods like Theta* and Lazy Theta*. By deferring line-of-sight checks and partially updating search data, this technique significantly accelerates any-angle path planning on grid maps—a critical improvement for real-time applications in computer games and robotics. Tang also addresses the pressing safety challenges of human-machine collaboration in their 2022 paper, “A Human-Machine Safety Distance Detection Method Based on Computer Vision” (5 citations). This work proposes a vision-based system to dynamically detect and maintain safe distances between humans and robots, a vital step toward practical and secure collaborative production environments. Through these contributions, Tang is helping to make autonomous systems both faster and safer.
Research Focus
Key Achievements
Top Papers
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