Papers

2

Total Citations

4

H-Index

2

About

Zida Wu is a robotics researcher focused on advancing autonomous systems and service robotics, with key contributions in state estimation and intelligent navigation. Wu’s work on joint state and input estimation using a recursive Kalman filter, published in 2022, tackles the challenge of unknown disturbances in autonomous agents, enhancing robot performance in unpredictable environments—a critical step for real-world deployment. His 2023 paper on a service robotic system for Internet Data Centers (IDCs) introduces a fully-automatic robot with a smooth chassis for heavy server handling, addressing labor-intensive tasks and reducing maintenance costs. Though early in his career, with each paper garnering 2 citations, Wu’s research bridges theoretical estimation methods and practical applications in high-stakes settings like data centers. His work demonstrates a commitment to solving pressing industrial challenges through innovative design and control, positioning him as an emerging voice in robotics for automation and operational efficiency.

Research Focus

Key Achievements

2
H-Index
2
Papers
4
Total Citations
2
Avg Citations/Paper
🏆 Most Cited Paper
Joint State and Input Estimation of Agent Based on Recursive Kalman Filter Given Prior Knowledge
2 citations · 2022
📈 Most Prolific Year: 2022 (1 Papers)
🤝 Key Collaborators: 9
🏛 Institutions: University of California, Los Angeles, Tencent (China)

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago