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Engineering Proceedings
Volume 133
Issue 1
10.3390/engproc2026133152
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Proceeding Paper

Artificial Intelligence for Planetary Exploration: Lessons Learned from a Decade of Analog Field Tests †

by
Steffen Planthaber
1,*,
Udo Frese
1,2,
Wiebke Brinkmann
1,*,
Raúl Domínguez
1,
Melvin Laux
1,2,
Mehmed Yüksel
1,
Andreas Bresser
1 and
Frank Kirchner
1,2
1
German Research Center for Artificial Intelligence GmbH (DFKI), 28359 Bremen, Germany
2
Faculty 3-Mathematics and Computer Science, University of Bremen, 28359 Bremen, Germany
*
Authors to whom correspondence should be addressed.
†
Presented at the 15th EASN International Conference, Madrid, Spain, 14–17 October 2025.
Eng. Proc. 2026, 133(1), 152; https://doi.org/10.3390/engproc2026133152
Published: 15 May 2026
(This article belongs to the Proceedings of The 15th EASN International Conference on “Innovation in Aviation & Space Towards Sustainability Today & Tomorrow”)
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Abstract

Celestial bodies in the solar system have long been of particular interest in space science. Some questions, e.g., those concerning the origin of life, require on-site landing and exploration. Due to signal delay, some degree of autonomy provided by artificial intelligence (AI) is needed. Motivated by planetary exploration missions, the German Research Center for Artificial Intelligence (DFKI) has developed methods for (semi-)autonomous control of vehicles and robots on extraterrestrial bodies. To validate the software, we conduct extensive field tests in terrestrial analog environments. Field tests can be seen as an intermediate step between development and laboratory testing and real-world deployment in an extraterrestrial environment. This paper describes the challenges of testing AI and robotic systems in analog environments, with a focus on the additional dependencies that arise during the preparation and execution of such field tests. The robots and software tested in these field tests are based on more than a decade of development across various projects, covering a wide range of AI systems and applications, including geometric planning, probabilistic perception, deep learning, and robot construction for open challenges in planetary exploration.
Keywords: artificial intelligence; space robotics; field tests artificial intelligence; space robotics; field tests

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MDPI and ACS Style

Planthaber, S.; Frese, U.; Brinkmann, W.; Domínguez, R.; Laux, M.; Yüksel, M.; Bresser, A.; Kirchner, F. Artificial Intelligence for Planetary Exploration: Lessons Learned from a Decade of Analog Field Tests. Eng. Proc. 2026, 133, 152. https://doi.org/10.3390/engproc2026133152

AMA Style

Planthaber S, Frese U, Brinkmann W, Domínguez R, Laux M, Yüksel M, Bresser A, Kirchner F. Artificial Intelligence for Planetary Exploration: Lessons Learned from a Decade of Analog Field Tests. Engineering Proceedings. 2026; 133(1):152. https://doi.org/10.3390/engproc2026133152

Chicago/Turabian Style

Planthaber, Steffen, Udo Frese, Wiebke Brinkmann, Raúl Domínguez, Melvin Laux, Mehmed Yüksel, Andreas Bresser, and Frank Kirchner. 2026. "Artificial Intelligence for Planetary Exploration: Lessons Learned from a Decade of Analog Field Tests" Engineering Proceedings 133, no. 1: 152. https://doi.org/10.3390/engproc2026133152

APA Style

Planthaber, S., Frese, U., Brinkmann, W., Domínguez, R., Laux, M., Yüksel, M., Bresser, A., & Kirchner, F. (2026). Artificial Intelligence for Planetary Exploration: Lessons Learned from a Decade of Analog Field Tests. Engineering Proceedings, 133(1), 152. https://doi.org/10.3390/engproc2026133152

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