Papers
2
Total Citations
7
H-Index
1
About
Xiangpeng Xu’s research centers on autonomous systems, multi-agent coordination, and vision-based guidance, with a particular focus on unmanned aerial vehicles (UAVs) and spacecraft. His work addresses critical challenges in motion tracking and pose estimation under constrained or extreme environments. In his most cited paper, “3-D motion trajectory measurement for the target through a multi-UAVs system” (2022, 6 citations), Xu developed a novel framework for accurately reconstructing three-dimensional trajectories using distributed UAV networks—a key contribution to cooperative surveillance and target tracking. More recently, his 2024 paper on “Quadrilateral Pose Estimation for Constrained Spacecraft Guidance and Control Using Deep Learning–Based Keypoint Filtering” introduces an innovative deep learning approach to filter keypoints for precise spacecraft pose estimation, a notoriously difficult problem in the harsh conditions of space. This work bridges computer vision and aerospace control, offering new pathways for autonomous docking and debris removal. Though early in his career, Xu’s research demonstrates strong potential for impact in both aerial robotics and space systems, with his methods poised to enable more robust, real-time decision-making in safety-critical applications.
Research Focus
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Top Papers
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