Unlike conventional software, robotic software suffers from a lack of methods and processes that could systematize and facilitate development. Thus, the application of software engineering techniques is at the heart of current issues in robotics. The work presented in this paper aims to facilitate the development of robotic software and to facilitate communication between experts in the field through the use of software engineering techniques and methods. It proposes RsaML (Robotic Software Architecture Modeling Language), a Domain Specific Modeling Language (DSML) dedicated to robotics, which takes into account the different categories of robotic software architectures and makes it possible to describe the latter independently from the implementation platform. The conceptual model defining the terminology and the hierarchy of concepts used for the description and representation of robotic software architectures in RsaML are presented in this article. RsaML is defined through a meta-model which represents the abstract syntax of the language. The real-time properties of robotic software architectures are identified and included in the meta-model. The use of RsaML is illustrated through several experimental scenarios of the language: the definition of a robotic system and the description of its software architecture, the verification of the semantics of a robotic software architecture, and the modeling of a robotic system whose software architecture does not belong to the usual categories. The support tool used for implementations and experimentation is Eclipse Modeling Framework (EMF). The results of experimentation showed good working of the proposed solution and made it possible to validate the main concepts of the RsaML language.

For materials engineers, it is very important to select the best material to make an object. The criteria used for such selection may be classified in several categories, including the two following: criteria related to the durability of materials against degradations caused by environments and those expressing their impacts on the environment throughout their whole life cycle. Selecting a material is therefore a multi-criteria decision problem. Due to the diversity of criteria, material analysis is carried out by design offices using systems based on human knowledge when available and, otherwise, trial and error. Currently, the large amount of materials available and the number of properties make it necessary to investigate the use of generative AI for the selection of materials. However, the information provided by the available systems is insufficient to make the appropriate decision. Traditional operational research systems based on mathematical models, such as Formal Concept Analysis (FCA), provide a support for understanding the logic used to reach the solutions. They offer decision-makers a greater possibility of arguing their choice. Our work tackles this material selection decision problem using FCA. The results obtained after applying the FCA ConExp tool are analyzed in relation to the results obtained by the AI systems ChatGPT and the Fictiv-AI material selection assistant.