Papers
2
Total Citations
31
H-Index
2
About
Ding-Sheng Wang is a robotics researcher specializing in human-robot interaction (HRI), omnidirectional mobile robot control, and deep learning-based perception systems. His work addresses critical challenges in enabling robots to operate safely and effectively in dynamic, noisy environments alongside humans. Wang’s most cited paper (18 citations) introduces a speech improvement-based stratified adaptive finite-time saturation control (SIB-SAFTSC) for omnidirectional service robots, a method that enhances voice command recognition in noisy settings while ensuring stable, finite-time robot motion. His second major contribution (13 citations) presents a novel deep learning framework—integrating Single-Shot Detection (SSD), FaceNet, and Kernelized Correlation Filter (KCF)—for detecting and tracking specific humans during HRI tasks. This work advances the reliability of person-following and interaction initiation in service robotics. Wang’s research bridges theoretical control design and practical perception, with direct applications in assistive and service robotics. His contributions are recognized for improving robot autonomy and safety in real-world human environments, laying groundwork for more intuitive and robust robotic companions.
Research Focus
Key Achievements
Top Papers
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