Fan-Chen Weng

About

Fan-Chen Weng is a leading researcher in intelligent robotics and human-robot interaction (HRI), with a focus on omnidirectional service robots (OSRs) operating in complex, real-world environments. His work bridges control theory, deep learning, and speech processing to make robots more responsive and autonomous. Weng’s most cited paper (2020, 18 citations) introduces a speech improvement-based stratified adaptive finite-time saturation control (SAFTS-C) that enables OSRs to understand voice commands in noisy settings, significantly advancing robust HRI. Another influential work (2020, 13 citations) develops a deep learning framework—integrating Single-Shot Detection, FaceNet, and Kernelized Correlation Filter (SSD-FN-KCF)—for specific human detection and tracking, enhancing a robot’s ability to interact with designated individuals. More recently (2021, 5 citations), Weng tackled the challenge of simultaneous translation and rotation tracking for sharp corners and time-varying terrain using hierarchical adaptive fixed-time saturated control. These contributions demonstrate his commitment to practical, adaptive control solutions that improve robot safety and performance. With a growing citation record, Weng is recognized for pushing the boundaries of autonomous service robotics in dynamic, human-centric settings.

Research Focus

Key Achievements

Top Papers

1. 1
   Experimental Validation of Speech Improvement-Based Stratified Adaptive Finite-Time Saturation Control of Omnidirectional Service Robot

   18 citations · 2020
2. 2
   Interactions Between Specific Human and Omnidirectional Mobile Robot Using Deep Learning Approach: SSD-FN-KCF

   13 citations · 2020
3. 3
   Simultaneous translation and rotation tracking design for sharp corner, obstacle avoidance, and time-varying terrain by hierarchical adaptive fixed-time saturated control

   5 citations · 2021

Key Collaborators

Contact & Links