Papers
3
Total Citations
69
H-Index
3
About
Jiawei Tang is a robotics researcher whose work sits at the intersection of motion planning, control theory, and multi-robot systems. His research addresses fundamental challenges in autonomous robot navigation and coordination, with a particular focus on developing mathematically rigorous and practically effective solutions for complex robotic tasks. Tang's most celebrated contribution is his development of Geometric Model Predictive Control (GMPC), a trajectory tracking framework that respects the manifold structure of robot configuration spaces — specifically continuous transformation groups — rather than naively treating robot states as elements of ordinary vector spaces. This geometrically principled approach has garnered 32 citations since its 2024 publication, reflecting its immediate impact on the robotics control community. Equally notable is his APF-CPP framework, which leverages artificial potential fields to enable efficient multi-robot collaborative coverage path planning, accumulating 28 citations in 2024 alone. His earlier work on relative state formation theory for warehouse multi-robot systems demonstrated his sustained interest in coordinated autonomous systems for real-world logistics applications. Collectively, Tang's research pushes the boundaries of how robots perceive, plan, and cooperate in structured environments, offering contributions that are both theoretically elegant and practically deployable — making his work essential reading for students exploring autonomous robotics and multi-agent systems.
Research Focus
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Top Papers
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