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

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Contributors

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

Title2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion ACSOS
Subtitle(Online)
Conference number
Duration17 - 21 August 2020
Location
CityWashington, DC
CountryUnited States of America

External IDs

Scopus 85092715797

Keywords

Keywords

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