Papers
2
Total Citations
4
H-Index
1
About
Luxi Yang’s research lies at the intersection of robotics, perception, and human-machine interaction, with a focus on improving autonomy and usability in complex environments. In their foundational work, “A PHD-SLAM Method for Mixed Birth Map Information Based on Amplitude Information” (2021, 3 citations), Yang tackled a critical challenge in simultaneous localization and mapping (SLAM) for mobile robots operating in cluttered, feature-rich settings such as indoor spaces or underwater domains. By integrating amplitude information into the probability hypothesis density (PHD) filter, this method significantly enhances localization accuracy in environments dense with noise and diverse map features—a key step toward robust autonomous navigation. More recently, Yang has expanded into the clinical domain with “Exploring the dynamics of user experience and interaction in XR-enhanced robotic surgery: a systematic review” (2024, 1 citation). This work systematically examines how extended reality (XR) interfaces can augment robotic-assisted surgery (RAS), addressing the pressing need for intuitive, precise control in image-guided procedures. Through this review, Yang highlights the critical role of user experience in next-generation surgical systems. With contributions spanning both algorithmic innovation and human-centered design, Luxi Yang is shaping the future of intelligent, interactive robotics.
Research Focus
Key Achievements
Top Papers
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