Xinwei Lv
Papers
2
Total Citations
7
H-Index
2
About
Xinwei Lv is a researcher focused on advancing mobile robot navigation and positioning systems within smart indoor environments. Their work centers on developing hybrid computational frameworks that leverage big data and machine learning to enhance the safety and robustness of robot navigation. Lv’s major contributions include pioneering the use of the Apache Spark platform for big data-driven indoor position forecasting, as well as proposing novel hybrid models like EWT-MOFEPSO-MRMRMI-ORELM, which integrate signal processing, feature selection, and optimized extreme learning machines. These innovations address critical challenges in predicting mobile robot trajectories, enabling more reliable autonomous transportation in complex indoor settings. Though their most cited papers have accumulated modest citation counts (5 and 2 citations respectively), the work represents foundational steps in applying advanced computing paradigms to real-world robotics. Lv’s research is particularly notable for bridging the gap between theoretical hybrid algorithms and practical deployment in smart environments, offering scalable solutions for industries relying on automated guided vehicles and service robots. This emerging body of work signals a promising trajectory in intelligent robotics and data-driven automation.
Research Focus
Key Achievements
Top Papers
- 1
- 2