Badong Chen
Papers
13
Total Citations
179
H-Index
7
About
Badong Chen is a leading researcher at the intersection of robotics, neuromusculoskeletal modeling, and adaptive signal processing. His work focuses on enabling intelligent human-robot interaction, with key contributions in pedestrian trajectory prediction, personalized gait generation, and collaborative torque estimation. Chen’s most cited paper, "IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction" (2024, 43 citations), advances autonomous navigation by modeling crowd dynamics to avoid collisions. He also developed a Random Forest-based method for personalized gait trajectory generation (2019, 40 citations), tailoring robotic assistance to individual anthropometric features. In human-robot collaboration, his neuromusculoskeletal model predicts voluntary torques from sEMG signals (2020, 24 citations), enhancing prosthetic and exoskeleton control. Chen’s impact extends to space robotics, where his generalized maximum correntropy Kalman filter improved target tracking in the TianGong-2 space laboratory (2022, 16 citations). He has also contributed to EEG-fMRI fusion for neurorobotics and led emotion recognition algorithms in the World Robot Contest 2021. With over 170 citations across these works, Chen’s research bridges theoretical innovation and real-world robotic applications, from home service robots to space exploration.
Research Focus
Key Achievements
Top Papers
- 1IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction43 citations · 2024
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- 8Robust Motion Averaging under Maximum Correntropy Criterion7 citations · 2021
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- 10Kalman Filtering3 citations · 2023