Zhipeng Chen

Shenzhen University

Papers

6

Total Citations

109

H-Index

4

About

Zhipeng Chen is a researcher whose work sits at the intersection of robotics, precision measurement, and infrastructure monitoring. His research focuses on three interconnected domains: autonomous robotic inspection systems, inertial navigation and localization technologies, and structural health monitoring for civil infrastructure. Chen has made notable contributions to sewer inspection technology, with his work on defect instance segmentation and 3D reconstruction for floating capsule robots garnering 48 citations since 2022, establishing him as an emerging voice in intelligent pipeline inspection. His parallel investigations into earth-rockfill dam deformation monitoring — using high-precision flexible pipeline measurement systems — reflect a sophisticated understanding of geotechnical instrumentation, earning 30 citations for addressing the critical challenge of monitoring ultra-high rockfill dams exceeding 300 meters. Beyond infrastructure, Chen has advanced indoor robot localization by developing tight-integration frameworks combining Wi-Fi RTT, encoder data, and inertial navigation systems on affordable smartphone platforms, accumulating 24 citations. His additional work on concrete floor flatness inspection using wheeled robots further demonstrates his versatility in applying robotic sensing to construction quality assessment. Across these contributions, Chen consistently addresses real-world engineering limitations — sparse sampling, low efficiency, and measurement accuracy — making his research practically impactful for infrastructure safety and smart construction communities.

Research Focus

Key Achievements

4
H-Index
6
Papers
109
Total Citations
18
Avg Citations/Paper
🏆 Most Cited Paper
Sewer defect instance segmentation, localization, and 3D reconstruction for sewer floating capsule robots
48 citations · 2022
📈 Most Prolific Year: 2022 (4 Papers)
🤝 Key Collaborators: 23
🏛 Institutions: Shenzhen University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago