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

• 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 the product life cycle, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. mechanisms that leverage sensing Industrial Internet of Things (IIoT) devices to share data for the construction of DTs are susceptible to adverse selection problems.
  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)
• A Blueprint Architecture of Compound AI Systems for Enterprise [18.109450556443782]
  We introduce a blueprint architecture for compound AI systems to operate in enterprise settings cost-effectively and feasibly. Our proposed architecture aims for seamless integration with existing compute and data infrastructure, with stream'' serving as the key orchestration concept.
  arXiv  Detail & Related papers  (2024-06-02T01:16:32Z)
• 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)
• Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles [52.79089414630366]
  We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL) We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
  arXiv  Detail & Related papers  (2020-07-27T12:38:37Z)
• 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)
• Many-Objective Software Remodularization using NSGA-III [17.487053547108516]
  We propose a novel many-objective search-based approach using NSGA-III. The process aims at finding the optimal remodularization solutions that improve the structure of packages, minimize the number of changes, preserve semantics coherence, and re-use the history of changes.
  arXiv  Detail & Related papers  (2020-05-13T18:34:15Z)

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.