Papers
6
Total Citations
139
H-Index
5
About
Zhengqiu Zhu is a researcher whose work spans autonomous source searching, intelligent transportation systems, and human-AI collaborative sensing — fields sitting at the intersection of robotics, artificial intelligence, and smart infrastructure. His most recognized contributions address the challenging problem of locating unknown diffusive sources in complex, obstructed environments, developing sophisticated algorithms that combine entropy-based navigation, cognitive strategies, and path planning to guide autonomous searchers through real-world constraints such as road networks and forbidden zones. His landmark papers on Entrotaxis-Jump and related source-searching frameworks have collectively garnered over 100 citations, demonstrating meaningful uptake within the robotics and environmental monitoring communities. More recently, Zhu has broadened his research scope to encompass Crowd Sensing Intelligence for next-generation intelligent transportation systems, exploring how Cyber-Physical-Social Systems can enable smarter urban mobility. His 2024 work on conversational crowdsensing reflects a forward-looking vision, integrating large language models and parallel intelligence into industrial sensing paradigms aligned with Industry 5.0. Across his career, Zhu consistently bridges theoretical algorithm design with practical, socially embedded applications, making his research particularly relevant to students and practitioners working on autonomous systems, smart cities, and human-machine teaming.
Research Focus
Key Achievements
Top Papers
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- 4Crowd Sensing Intelligence for ITS: Participants, Methods, and Stages19 citations · 2023
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