Skills Composition Framework for Reconfigurable Cyber-Physical Production Modules
- URL: http://arxiv.org/abs/2405.13604v1
- Date: Wed, 22 May 2024 12:56:05 GMT
- Title: Skills Composition Framework for Reconfigurable Cyber-Physical Production Modules
- Authors: Aleksandr Sidorenko, Achim Wagner, Martin Ruskowski,
- Abstract summary: This paper proposes a framework for skills' composition and execution in skill-based reconfigurable cyber-physical production modules.
It is based on distributed Behavior trees (BTs) and provides good integration between low-level devices' specific code and AI-based task-oriented frameworks.
- Score: 44.99833362998488
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: While the benefits of reconfigurable manufacturing systems (RMS) are well-known, there are still challenges to their development, including, among others, a modular software architecture that enables rapid reconfiguration without much reprogramming effort. Skill-based engineering improves software modularity and increases the reconfiguration potential of RMS. Nevertheless, a skills' composition framework with a focus on frequent and rapid software changes is still missing. The Behavior trees (BTs) framework is a novel approach, which enables intuitive design of modular hierarchical control structures. BTs have been mostly explored from the AI and robotics perspectives, and little work has been done in investigating their potential for composing skills in the manufacturing domain. This paper proposes a framework for skills' composition and execution in skill-based reconfigurable cyber-physical production modules (RCPPMs). It is based on distributed BTs and provides good integration between low-level devices' specific code and AI-based task-oriented frameworks. We have implemented the provided models for the IEC 61499-based distributed automation controllers to show the instantiation of the proposed framework with the specific industrial technology and enable its evaluation by the automation community.
Related papers
- An Interpretable Automated Mechanism Design Framework with Large Language Models [26.89126917895188]
Mechanism has long been a cornerstone of economic theory, with traditional approaches relying on mathematical derivations.
Recent automated approaches, including differentiable economics with neural networks, have emerged for designing payments and allocations.
We introduce a novel framework that reformulates mechanism design as a code generation task.
arXiv Detail & Related papers (2025-02-16T12:33:03Z) - Specifications: The missing link to making the development of LLM systems an engineering discipline [65.10077876035417]
We discuss the progress the field has made so far-through advances like structured outputs, process supervision, and test-time compute.
We outline several future directions for research to enable the development of modular and reliable LLM-based systems.
arXiv Detail & Related papers (2024-11-25T07:48:31Z) - Creating Scalable AGI: the Open General Intelligence Framework [0.0]
Open General Intelligence (OGI) is a novel systems architecture that serves as a macro design reference for Artificial General Intelligence (AGI)
OGI adopts a modular approach to the design of intelligent systems, based on the premise that cognition must occur across multiple specialized modules that can seamlessly operate as a single system.
The OGI framework aims to overcome the challenges observed in today's intelligent systems, paving the way for more holistic and context-aware problem-solving capabilities.
arXiv Detail & Related papers (2024-11-24T13:17:53Z) - Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.
By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.
GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
arXiv Detail & Related papers (2024-08-02T10:47:10Z) - RO-SVD: A Reconfigurable Hardware Copyright Protection Framework for AIGC Applications [7.368978400783039]
We propose a blockchain-based copyright traceability framework for AI content.
Our framework can be easily constructed on existing AI-accelerated devices.
This is the first practical hardware study discussing and implementing copyright traceability specifically for AI-generated content.
arXiv Detail & Related papers (2024-06-17T13:38:57Z) - Towards Using Behavior Trees in Industrial Automation Controllers [41.94295877935867]
The Industry 4.0 paradigm manifests the shift towards mass customization and cyber-physical production systems.
There is a lack of PLC software flexibility and integration between low-level programs and high-level task-oriented control frameworks.
This paper proposes an approach for improving the industrial control software design by integrating Behavior Trees into PLC programs.
arXiv Detail & Related papers (2024-04-22T09:47:36Z) - CARLOS: An Open, Modular, and Scalable Simulation Framework for the Development and Testing of Software for C-ITS [0.0]
We propose CARLOS - an open, modular, and scalable simulation framework for the development and testing of software in C-ITS.
We provide core building blocks for this framework and explain how it can be used and extended by the community.
In our paper, we motivate the architecture by describing important design principles and showcasing three major use cases.
arXiv Detail & Related papers (2024-04-02T10:48:36Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
We have developed a proven systems engineering approach for machine learning development and deployment.
Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
arXiv Detail & Related papers (2021-01-11T15:54:48Z) - Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components.
There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed.
We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
arXiv Detail & Related papers (2020-07-13T20:30:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.