Qiye Yang
Papers
5
Total Citations
75
H-Index
5
About
Qiye Yang is a researcher advancing the safety and precision of teleoperation systems, with a focus on collision avoidance, tremor suppression, and force estimation. Their work addresses critical challenges in human-robot interaction, particularly for applications requiring remote manipulation in hazardous or delicate environments. Yang’s most cited paper, “Collision risk assessment and automatic obstacle avoidance strategy for teleoperation robots” (26 citations), introduces a framework for real-time hazard detection and autonomous path correction, enhancing operational safety. Another key contribution, “Broad learning extreme learning machine for forecasting and eliminating tremors in teleoperation” (24 citations), leverages broad learning to filter involuntary hand tremors, improving control accuracy. Yang has also developed sensorless force observers using adaptive sparse general regression neural networks (11 citations) and multilayer depth extreme learning machines (5 citations), enabling precise force feedback without physical sensors. Their innovative tremor-filtering model, the Three-domain Wavelet Least Square Support Vector Machine (9 citations), further demonstrates expertise in signal processing for teleoperation. With a growing citation impact, Yang’s work is pivotal for advancing teleoperation in surgery, space exploration, and remote manufacturing.
Research Focus
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