Papers
5
Total Citations
38
H-Index
3
About
Wang Chen’s research bridges robotics, computer vision, and deep learning, with a focus on data fusion, 3D reconstruction, and intelligent state detection. In his seminal 2005 work, Chen proposed a two-stage SVM-based data fusion method that achieved accurate robot gripper state estimation by minimizing both empirical and structural risk—a foundational contribution cited 14 times. He later advanced omnidirectional vision, developing a 2-point algorithm for 3D reconstruction of horizontal lines from single omnidirectional images (2010, 11 citations) and a nonsingle viewpoint stereo depth estimation method using space layer labeling (2011, 3 citations), expanding the field-of-view for robot navigation and depth sensing. More recently, Chen applied deep learning to power systems, creating a YOLOv5-based network for detecting switchgear secondary panel states (2021, 8 citations), improving operational safety. His latest work, “iKap: Kinematics-Aware Planning with Imperative Learning” (2025), integrates vision-to-planning systems for efficient, adaptable trajectory generation. With a career spanning two decades, Chen’s work demonstrates a consistent drive to fuse theoretical rigor with practical robotics and industrial applications, earning recognition for both foundational algorithms and cutting-edge AI deployments.
Research Focus
Key Achievements
Top Papers
- 1Study on a SVM-based data fusion method14 citations · 2005
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- 5iKap: Kinematics-Aware Planning with Imperative Learning2 citations · 2025