Artificial Intelligence for the Early Design Phases of Space Missions

Berquand, Audrey; Murdaca, Francesco; Riccardi, Annalisa; Soares, Tiago; Genere, Sam; Brauer, Norbert; Kumar, K. Udaya

doi:10.1109/aero.2019.8742082

Cited by 12 publications

(8 citation statements)

References 17 publications

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“…However, it can quickly become highly time-consuming to search through the non-neglectable amount of data accumulated over the past decades. As found in [1], experts involved in feasibility studies can spend up to 50% of their work time searching for information. Information Retrieval (IR) methods combined with the recent advances of Natural Language Processing (NLP) and Understanding (NLU), should allow experts to access information more quickly, more efficiently and overcome the current 'IR bottleneck'.…”

Section: Introductionmentioning

confidence: 99%

“…The DEA aims to considerably reduce the time spent to access information on past missions. The high-level architecture of the DEA, requirements and the result of a set of interviews involving ESA experts are presented in previous publications [1], [2]. To our best knowledge, the Daphne virtual assistant, in development at Texas A&M University and presented in [3], is the most similar concept to the DEA.…”

Section: Introductionmentioning

confidence: 99%

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Space mission design ontology: extraction of domain-specific entities and concepts similarity analysis

Berquand

Moshfeghi

Riccardi

2020

AIAA Scitech 2020 Forum

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Expert Systems, computer programs able to capture human expertise and mimic experts' reasoning, can support the design of future space missions by assimilating and facilitating access to accumulated knowledge. To organise these data, the virtual assistant needs to understand the concepts characterising space systems engineering. In other words, it needs an ontology of space systems. Unfortunately, there is currently no official European space systems ontology. Developing an ontology is a lengthy and tedious process, involving several human domain experts, and therefore prone to human error and subjectivity. Could the foundations of an ontology be instead semi-automatically extracted from unstructured data related to space systems engineering? This paper presents an implementation of the first layers of the Ontology Learning Layer Cake, an approach to semi-automatically generate an ontology. Candidate entities and synonyms are extracted from three corpora: a set of 56 feasibility reports provided by the European Space Agency, 40 books on space mission design publicly available and a collection of 273 Wikipedia pages. Lexica of relevant space systems entities are semi-automatically generated based on three different methods: a frequency analysis, a term frequency-inverse document frequency analysis, and a Weirdness Index filtering. The frequency-based lexicon of the combined corpora is then fed to a word embedding method, word2vec, to learn the context of each entity. With a cosine similarity analysis, concepts with similar contexts are matched.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Space mission design ontology: extraction of domain-specific entities and concepts similarity analysis

Berquand

Moshfeghi

Riccardi

2020

AIAA Scitech 2020 Forum

Self Cite

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“…Another recent work is [59], where a process for innovative problem solving is proposed based on the human and computer assisting each other in the identification and characterization of the obscure features of a problem. Finally, ESA's Design Engineering Assistant (DEA) [60] introduces a VA for concurrent engineering processes that helps engineers by providing data aggregation and synthesis capabilities of past unstructured documentation in an automated manner. It also includes a user interface (UI) for fast access to this information.…”

Section: Vas For System Designmentioning

confidence: 99%

Daphne: A Virtual Assistant for Designing Earth Observation Distributed Spacecraft Missions

Martin

Selva

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This article describes Daphne, a virtual assistant for designing Earth observation distributed spacecraft missions. It is, to the best of our knowledge, the first virtual assistant for such application. The article provides a thorough description of Daphne, including its question answering system and the main features we have implemented to help system engineers design distributed spacecraft missions. In addition, the article describes a study performed at NASA's Jet Propulsion Laboratory (JPL) to assess the usefulness of Daphne in this use case. The study was conducted with N = 9 subjects from JPL, who were asked to work on a mission design task with two versions of Daphne, one that was fully featured implementing the cognitive assistance functions, and one that only had the features one would find in a traditional design space exploration tool. After the task, they filled out a standard user experience survey, completed a test to assess how much they learned about the task, and were asked a number of questions in a semi-structured exit interview. Results of the study suggest that Daphne can help improve performance during system design tasks compared to traditional tools, while keeping the system usable. However, the study also raises some concerns with respect to a potential reduction in human learning due to the use of the cognitive assistant. The article ends with a list of suggestions for future development of virtual assistants for space mission design. Index Terms-Distributed spacecraft missions, earth observation, machine learning, mixed initiative, virtual assistant. I. INTRODUCTION M OTIVATED by the challenges of system architecture in general and architecting distributed satellite missions (DSM) in particular, and inspired by the success of commercial virtual assistants (VA), such as Siri, Google Assistant, Alexa, or Mycroft, we have developed Daphne, the first VA-to the best of our knowledge-to support the high-level design of DSM. This article describes how Daphne can be used to design DSM and includes a quantitative validation study performed at NASA JPL where the test subjects had expertise in mission design. The contribution of this article is two fold. First, we introduce Daphne as a complete, open-source package for DSM tradespace analysis that includes the standard VA functionality, such as Manuscript

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“…The space sector is getting interested in AI too, having incorporated techniques and methods in different areas. Natural language processing (Berquand et al 2018), knowledge representation, automated reasoning, computer vision (Jasiobedski et al 2001), trajectory optimization and navigation (Izzo et al 2019), satellite autonomy (Anderson et al 2009), and robotics are some of the fields in space engineering where AI have made interesting contributions.…”

Section: Artificial Intelligence In Engineeringmentioning

confidence: 99%

AI and Space Safety: Collision Risk Assessment

Sánchez

Vasile

Minisci

2020

Handbook of Space Security

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The New Space environment will introduce notorious changes in the space environment during the next decades with the irruption of mega-constellation, the boost of small satellites, and the generalization of low-thrust engines. Current safety strategies and collision avoidance procedures will no longer be capable to deal with the increase on conjunction alerts. Artificial intelligence appears to be the best strategy to cope with this new situation, thanks to its ability to perform faster than physical models and make decisions based on a wider range of parameters than human operators. This results in better performances when more data are available, as the situations will present on the coming years.

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Artificial Intelligence for the Early Design Phases of Space Missions

Cited by 12 publications

References 17 publications

Space mission design ontology: extraction of domain-specific entities and concepts similarity analysis

Space mission design ontology: extraction of domain-specific entities and concepts similarity analysis

Daphne: A Virtual Assistant for Designing Earth Observation Distributed Spacecraft Missions

AI and Space Safety: Collision Risk Assessment

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