RoboMAX: Robotic Mission Adaptation eXemplars
Paper i proceeding, 2021
Emerging and future applications of robotic systems pose unique self-adaptation challenges. To support the research needed to address these challenges, we provide an extensible repository of robotic mission adaptation exemplars. Co-designed with robotic application stakeholders including researchers, developers, operators, and end-users, our repository captures key sources of uncertainty, adaptation concerns, and other distinguishing characteristics of such applications. An online form enables external parties to supply new exemplars for curation and inclusion into the repository. We envisage that our RoboMAX repository will enable the development, evaluation, and comparison of self-adaptation approaches for the robotic systems domain.
Författare
Mehrnoosh Askarpour
McMaster University
Christos Tsigkanos
Technische Universität Wien
Claudio Menghi
Université du Luxembourg
Radu Calinescu
University of York
Patrizio Pelliccione
Gran Sasso Science Institute (GSSI)
Göteborgs universitet
Sergio García Gonzalo
Göteborgs universitet
Ricardo Diniz Caldas
Cyber Physical Systems
Tim J. Von Oertzen
Johannes Kepler Universität Linz (JKU)
M. Wimmer
Johannes Kepler Universität Linz (JKU)
Luca Berardinelli
Johannes Kepler Universität Linz (JKU)
Matteo Rossi
Politecnico di Milano
Marcello M. Bersani
Politecnico di Milano
Gabriel S. Rodrigues
Universidade de Brasilia
Proceedings - 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021
245-251 9462005
9781665402897 (ISBN)
2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021
Virtual, Online, ,
Virtual, Online, ,
Ämneskategorier
Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning
Robotteknik och automation
Datavetenskap (datalogi)
DOI
10.1109/SEAMS51251.2021.00040