A Deep Domain-Specific Model Framework for Self-Reproducing Robotic Control Systems
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-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 language | English |
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Title of host publication | 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) |
Pages | 240-242 |
ISBN (electronic) | 978-1-7281-8414-2 |
Publication status | Published - 20 Aug 2020 |
Peer-reviewed | Yes |
Conference
Title | 1st IEEE International Conference on Autonomic Computing and Self-Organizing Systems |
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Abbreviated title | ACSOS 2020 |
Conference number | 1 |
Duration | 17 - 21 August 2020 |
Location | Online |
City | Washington, DC |
Country | United States of America |
External IDs
Scopus | 85092715797 |
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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