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

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021
Virtual, Online, ,

Ämneskategorier

Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning

Robotteknik och automation

Datavetenskap (datalogi)

DOI

10.1109/SEAMS51251.2021.00040

Mer information

Senast uppdaterat

2021-09-16