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

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

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

Original languageEnglish
Title of host publication2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)
Pages240-242
ISBN (electronic)978-1-7281-8414-2
Publication statusPublished - 20 Aug 2020
Peer-reviewedYes

Conference

Title1st IEEE International Conference on Autonomic Computing and Self-Organizing Systems
Abbreviated titleACSOS 2020
Conference number1
Duration17 - 21 August 2020
LocationOnline
CityWashington, DC
CountryUnited States of America

External IDs

Scopus 85092715797
ORCID /0000-0002-3513-6448/work/168720194

Keywords

Keywords

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