Papers
9
Total Citations
326
H-Index
7
About
Ruobing Wang is a versatile robotics researcher whose work spans human-robot collaboration, cable-driven parallel robots, autonomous underwater systems, and motion planning. His most influential contribution, "Towards Proactive Human–Robot Collaboration: A Foreseeable Cognitive Manufacturing Paradigm" (2021, 190 citations), established a forward-thinking framework for intelligent manufacturing in which robots anticipate and adapt to human intent—a landmark reference in cognitive robotics. Wang has made substantial advances in cable-driven parallel robots (CDPRs), developing novel suspended and reconfigurable architectures capable of Schönflies motions, alongside sophisticated motion planning algorithms that incorporate jerk-limited, time-optimal, and model predictive control strategies. His work on underwater robotics, particularly the SVAM-Net framework for salient object detection (41 citations), demonstrates his breadth across domains, applying deep learning to real-world autonomous perception challenges. Additional contributions to bipedal locomotion and path-constrained motion planning for manipulators underscore his command of both classical dynamics and modern machine learning approaches. With over 300 cumulative citations, Wang's research consistently bridges theoretical rigor with practical robotics applications, making his work an essential reference for engineers and researchers advancing intelligent, adaptive robotic systems.
Research Focus
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Top Papers
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- 9Jerk-Limited Online Trajectory Scaling for Cable-Driven Parallel Robots2 citations · 2025