Papers
24
Total Citations
372
H-Index
11
About
Xianjia Yu is a leading researcher at the intersection of distributed robotics, autonomous systems, and edge-cloud computing. His work focuses on enabling secure, decentralized, and lifelong learning for multi-robot teams operating in GNSS-denied environments. Yu’s major contributions include pioneering the integration of federated learning and blockchain with robotic systems, as demonstrated in his highly cited paper “Federated Learning in Robotic and Autonomous Systems” (69 citations), which addresses real-time collaboration and low-latency offloading. He has also advanced cooperative localization for UAVs using UWB and LiDAR-based sensing, with papers like “Cooperative UWB-Based Localization for Outdoors Positioning and Navigation of UAVs” (34 citations) and “UAV Tracking with Lidar as a Camera Sensor in GNSS-Denied Environments” (30 citations). Yu’s impact is evident from his cumulative citation count exceeding 300, and his work on benchmarking multi-modal LiDAR SLAM (29 citations) provides critical ground truth for autonomous navigation. Notably, his research on lifelong federated learning for autonomous mobile robots (21 citations) and distributed ROS 2 systems (23 citations) has shaped modern architectures for edge-to-cloud communication. Yu’s contributions are vital for students and researchers exploring scalable, resilient autonomy in real-world deployments.
Research Focus
Key Achievements
Top Papers
- 1Federated Learning in Robotic and Autonomous Systems69 citations · 2021
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- 4UAV Tracking with Lidar as a Camera Sensor in GNSS-Denied Environments30 citations · 2023
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