Xinkai Wu
Papers
5
Total Citations
44
H-Index
3
About
Xinkai Wu is a robotics researcher specializing in autonomous navigation and localization for service robots operating in human-centered environments. His work addresses critical challenges in enabling robots to function reliably in dynamic, featureless, and complex indoor settings. Wu’s major contributions include developing an Enhanced Adaptive Monte Carlo Localization (AMCL) algorithm that significantly improves robot positioning accuracy and robustness when traditional methods fail in environments lacking distinct features—a common problem in real-world deployments. He has also pioneered hierarchical knowledge graph approaches for remote object navigation, allowing robots to leverage contextual understanding to locate and navigate toward specific objects. Additionally, his two-stage path planning method for target searching ensures complete visual coverage, which is vital for applications like warehouse inspection and elderly care surveillance. With over 44 citations across his most-cited works, Wu’s research has practical implications for automated mobile robots (AMRs) in logistics, domestic assistance, and healthcare. His work on knowledge-enhanced scene embedding further advances object-oriented navigation, bridging the gap between raw sensor data and semantic understanding. Wu’s contributions are helping to make service robots more autonomous, reliable, and capable in the unpredictable environments where they are most needed.
Research Focus
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Top Papers
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