Papers
2
Total Citations
215
H-Index
2
About
Guo Xie is a leading researcher in intelligent robotics and autonomous systems, with a focus on motion prediction and cooperative localization. His work bridges deep learning and sensor fusion to solve critical challenges in real-time robotic perception. His highly cited 2020 paper, “Motion Trajectory Prediction Based on a CNN-LSTM Sequential Model” (189 citations), introduced a novel hybrid architecture that significantly improved the accuracy of forecasting pedestrian and vehicle trajectories—a foundational contribution to safe autonomous navigation. Building on this, Xie addressed the practical limitations of multi-robot coordination in his 2021 study, “Multimobile Robot Cooperative Localization Using Ultrawideband Sensor and GPU Acceleration” (26 citations). Here, he tackled the persistent problem of non-line-of-sight (NLOS) errors in complex indoor environments by fusing ultrawideband (UWB) sensor data with GPU-accelerated processing, achieving both high precision and real-time performance. This work has direct implications for warehouse automation, search-and-rescue missions, and industrial swarm robotics. Xie’s research is distinguished by its clear focus on deployable solutions, combining theoretical rigor with hardware-aware optimization. His contributions continue to shape how robots perceive, predict, and collaborate in dynamic, uncertain spaces.
Research Focus
Key Achievements
Top Papers
- 1Motion trajectory prediction based on a CNN-LSTM sequential model189 citations · 2020
- 2