A Deep Domain-Specific Model Framework for Self-Reproducing Robotic Control Systems

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Abstract

As robots play more critical roles in diverse and complex scenarios in the real world, monomorphic robots are limited to repeating and rather simple tasks. How to achieve a robust, flexible, and scalable multi-robot system becomes essential research. Model-driven software development (MDSD) provides a sturdy methodology for robotic programming using multilevel domain-specific languages (DSLs). These DSLs lay a solid foundation for the design, integration, and extensibility of robotic applications. In this paper, we propose a deep domain-specific model framework for the self-reproducing robotic control system to escort reliable, versatile tasks of heterogeneous robots.

Details

OriginalspracheEnglisch
Titel2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)
Seiten240-242
ISBN (elektronisch)978-1-7281-8414-2
PublikationsstatusVeröffentlicht - 20 Aug. 2020
Peer-Review-StatusJa

Konferenz

Titel1st IEEE International Conference on Autonomic Computing and Self-Organizing Systems
KurztitelACSOS 2020
Veranstaltungsnummer1
Dauer17 - 21 August 2020
OrtOnline
StadtWashington, DC
LandUSA/Vereinigte Staaten

Externe IDs

Scopus 85092715797
ORCID /0000-0002-3513-6448/work/168720194
ORCID /0000-0001-5357-2748/work/175220748

Schlagworte

Schlagwörter

  • MSDS, Deep Model, Self-reproducing robots control systems, Robot kinematics, Robot sensing systems, DSL, Context modeling, Computational modeling, software