Papers
3
Total Citations
69
H-Index
3
About
Yifan Tang’s research lies at the intersection of mobile robotics, sensor networks, and computer vision, with a focus on deploying and localizing autonomous systems under real-world constraints. His early work pioneered strategies for deploying navigationally-challenged mobile sensor nodes—robots that can move and communicate but lack localization or obstacle avoidance capabilities. In his 2004 paper “Heterogeneous mobile sensor net deployment using robot herding and line-of-sight formations” (54 citations), Tang introduced a “helper” robot approach to guide simpler nodes into formation, enabling sensor network deployment in indoor environments. This was extended in “Planning mobile sensor net deployment for navigationally-challenged sensor nodes” (11 citations), which developed novel planning algorithms for large-scale, cost-constrained systems. More recently, Tang has advanced robot relocalization with FusedNet (2024, 4 citations), an end-to-end network that fuses global and local image features via cross-attention to achieve robust localization in dynamic, large-scale scenes using only a monocular camera. His work bridges foundational deployment challenges with modern deep learning solutions, demonstrating a sustained impact on practical, resource-limited robotics.
Research Focus
Key Achievements
Top Papers
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