Tongtong Chen
Papers
2
Total Citations
10
H-Index
2
About
Tongtong Chen’s research advances the frontiers of lunar exploration through a unique fusion of radio astronomy, planetary robotics, and in-situ resource utilization (ISRU). Her most cited work, “Gaussian-process-regression-based periodical variation analysis of the lunar surface temperature with the ESA-Dresden radio telescope” (2020, 7 citations), demonstrates a sophisticated application of machine learning to interpret thermal data from the Moon, providing critical insights for future habitat design and instrument deployment. In parallel, her paper “Beacon-based Localization of the Robot in a Lunar Analog Environment” (2020, 3 citations) tackles a fundamental challenge in autonomous planetary operations: enabling robots to navigate and coordinate in GPS-denied, low-visibility settings. This work directly supports the European Space Agency’s vision for sustainable lunar exploration, where robots like MANTIS and IBIS must autonomously transport materials for ISRU. By bridging data-driven modeling with practical robotic localization, Chen’s contributions are laying the groundwork for the next generation of self-sufficient lunar missions. Her research is particularly valuable for students and engineers working at the intersection of planetary science, robotics, and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1
- 2Beacon-based Localization of the Robot in a Lunar Analog Environment3 citations · 2020