A Deep Domain-Specific Model Framework for Self-Reproducing Robotic Control Systems
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
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
Originalsprache | Englisch |
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Titel | 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) |
Seiten | 240-242 |
ISBN (elektronisch) | 978-1-7281-8414-2 |
Publikationsstatus | Veröffentlicht - 20 Aug. 2020 |
Peer-Review-Status | Ja |
Konferenz
Titel | 1st IEEE International Conference on Autonomic Computing and Self-Organizing Systems |
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Kurztitel | ACSOS 2020 |
Veranstaltungsnummer | 1 |
Dauer | 17 - 21 August 2020 |
Ort | Online |
Stadt | Washington, DC |
Land | USA/Vereinigte Staaten |
Externe IDs
Scopus | 85092715797 |
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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