Papers
2
Total Citations
4
H-Index
1
About
Qing An is a robotics researcher whose work focuses on advancing autonomous manipulation and environmental perception in complex, real-world settings. A key contribution is the development of a deep reinforcement learning-based sorting operation method for manipulators, specifically designed for the hazardous task of radioactive waste sorting. This work directly addresses critical challenges in unstructured and locally radioactive environments, aiming to overcome the low efficiency, high difficulty, and poor autonomous control of traditional remote operations. In a related vein, An has also pioneered a novel approach to Simultaneous Localization and Mapping (SLAM) by introducing a method for generating dynamic SLAM dense point cloud maps. By fusing semantic information with Bayesian moving probability, this technique overcomes the limitations of static-environment assumptions, significantly improving localization accuracy and mapping consistency in dynamic settings. While early in their career, with these foundational papers already garnering citations, An’s research is poised to have a substantial impact on the future of autonomous robotics in challenging industrial and field applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2