Jiquan Yang
Papers
2
Total Citations
18
H-Index
2
About
Jiquan Yang is a researcher focused on advancing autonomous navigation and intelligent robotic control. His work centers on developing efficient path planning algorithms for robots operating without prior global maps, addressing a critical challenge in real-world, dynamic environments. His most cited paper, "Convolutionally evaluated gradient first search path planning algorithm without prior global maps" (2021, 13 citations), introduces a novel approach that enables robots to navigate unknown spaces by combining convolutional evaluation with gradient-based search, significantly improving computational efficiency and adaptability. Additionally, Yang has contributed to industrial robotics through his work on "Industry robotic motion and pose recognition method based on camera pose estimation and neural network" (2021, 5 citations), where he integrates camera pose estimation with neural networks to accurately monitor and validate robot motion and posture in manufacturing settings. This work is vital for ensuring operational correctness and safety in automated production lines. With a growing citation impact, Yang’s research bridges theoretical algorithm design and practical robotic applications, offering valuable solutions for autonomous systems and smart manufacturing. His contributions are particularly relevant for students and researchers exploring real-time navigation and vision-based robotic control.
Research Focus
Key Achievements
Top Papers
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- 2