Papers
10
Total Citations
146
H-Index
6
About
Wencen Wu’s research lies at the intersection of multi-robot systems, environmental monitoring, and intelligent control, with a focus on enabling robot swarms to autonomously explore and map unknown, dynamic fields. Her major contributions include developing biologically inspired switching strategies that allow robotic agents to transition from individual to cooperative exploration when searching for local minima in noisy scalar fields—a concept validated in her most-cited work (77 citations). She has also pioneered cooperative mapping of underwater acoustic channels using robot swarms (16 citations), addressing critical challenges in underwater networking. Wu’s work on parameter identification for spatial-temporal varying processes (13 citations) demonstrates how coordinated mobile robots can estimate parameters of complex diffusion fields, with experimental validation using real platforms. More recently, she has integrated reinforcement learning for multi-robot field coverage and dynamic field reconstruction, pushing toward real-time environmental monitoring without human intervention. Her innovative use of recurrent neural networks for level curve tracking without localization (5 citations) showcases her ability to merge machine learning with robotic control. With over 140 total citations, Wu’s research consistently advances autonomous exploration in harsh environments, from underwater acoustics to hazardous terrestrial zones.
Research Focus
Key Achievements
Top Papers
- 1Robust Cooperative Exploration With a Switching Strategy77 citations · 2012
- 2Cooperatively Mapping of the Underwater Acoustic Channel by Robot Swarms16 citations · 2014
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- 4Experimental validation of source seeking with a switching strategy10 citations · 2011
- 5Glider CT: Analysis and Experimental Validation9 citations · 2016
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- 9Constrained fast source seeking using two nonholonomic mobile robots3 citations · 2016
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