Papers

3

Total Citations

17

H-Index

2

About

Cornelia Altenbuchner is a researcher specializing in spacecraft autonomy, fault diagnosis, and bioinspired robotics. Her work focuses on enabling self-sufficient robotic spacecraft to assess their own hardware health, a critical capability for long-duration missions. In her most cited paper (2021, 8 citations), she reports on the integration of a Model-Based Fault Diagnosis (MBFD) system into CubeSat flight software, using real flight data to validate onboard health estimation. This contribution directly supports autonomous decision-making for future deep-space missions. Earlier, she contributed to NASA’s Asteroid Redirect Mission (ARM), analyzing the dynamic response of a robotic manipulator-based capture system for retrieving boulders from asteroids (2015, 7 citations). Her research also extends to bioinspired flight, where she validated a flexible multi-body structural dynamics model of an ornithopter through free-flight testing (2013, 2 citations). By bridging model-based diagnostics, robotic capture dynamics, and avian-inspired aerial robotics, Altenbuchner’s work advances both autonomous spacecraft operations and the design of small, agile aerial robots for civilian and military applications.

Research Focus

Key Achievements

2
H-Index
3
Papers
17
Total Citations
6
Avg Citations/Paper
🏆 Most Cited Paper
On-Board Model Based Fault Diagnosis for CubeSat Attitude Control Subsystem: Flight Data Results
8 citations · 2021
📈 Most Prolific Year: 2021 (1 Papers)
🤝 Key Collaborators: 12
🏛 Institutions: Jet Propulsion Laboratory, National Institute of Aerospace, University of Maryland, College Park

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago