arXiv subjects

Daniel Dittler

Publications and source records attributed to Daniel Dittler.

11 recordsLinked to original sources

arXiv2025

A Concept for Autonomous Problem-Solving in Intralogistics Scenarios

Achieving greater autonomy in automation systems is crucial for handling unforeseen situations effectively. However, this remains challenging due to technological limitations and the complexity of real-world environments. This paper examines the need for increased autonomy, defines the problem, and outlines key enabling technologies. A structured concept is proposed, consisting of three main steps: context enrichment, situation analysis, and generation of solution strategies. By following this approach, automation systems can make more independent decisions, reducing the need for human intervention. Additionally, possible realizations of the concept are discussed, especially the use of Large Language Models. While certain tasks may still require human assistance, the proposed approach significantly enhances the autonomy of automation systems, enabling more adaptive and intelligent problem-solving capabilities.

cs.CE↗

arXiv2025

Model-Based Control for Power-to-X Platforms: Knowledge Integration for Digital Twins

Offshore Power-to-X platforms enable flexible conversion of renewable energy, but place high demands on adaptive process control due to volatile operating conditions. To face this challenge, using Digital Twins in Power-to-X platforms is a promising approach. Comprehensive knowledge integration in Digital Twins requires the combination of heterogeneous models and a structured representation of model information. The proposed approach uses a standardized description of behavior models, semantic technologies and a graph-based model understanding to enable automatic adaption and selection of suitable models. It is implemented using a graph-based knowledge representation with Neo4j, automatic data extraction from Asset Administration Shells and port matching to ensure compatible model configurations.

cs.CE↗

arXiv2025

Communication Architecture for Autonomous Power-to-X Platforms: Enhancing Inspection and Operation With Legged Robots and 5G

Inspection and maintenance of offshore platforms are associated with high costs, primarily due to the significant personnel requirements and challenging operational conditions. This paper first presents a classification of Power to X platforms. Building upon this foundation, a communication architecture is proposed to enable monitoring, control, and teleoperation for a Power to X platform. To reduce the demand for human labor, a robotic system is integrated to autonomously perform inspection and maintenance tasks. The implementation utilizes a quadruped robot. Remote monitoring, control, and teleoperation of the robot are analyzed within the context of a 5G standalone network. As part of the evaluation, aspects such as availability and latency are recorded, compared, and critically assessed.

cs.CE↗

arXiv2025

An Expert Survey on Models and Digital Twins

Digital Twins (DTs) are becoming increasingly vital for future industrial applications, enhancing monitoring, control, and optimization of physical assets. This enhancement is made possible by integrating various Digital Models (DMs) within DTs, which must interoperate to represent different system aspects and fulfill diverse application purposes. However, industry perspectives on the challenges and research needs for integrating these models are rarely obtained. Thus, this study conducts an expert survey across multiple application domains to identify and analyze the challenges in utilizing diverse DMs within DTs. The results reveal missing standardized interfaces, high manual adaptation effort, and limited support for model reuse across lifecycle phases, highlighting future research needs in automated model composition and semantics-based interoperability.

cs.SE↗

arXiv2024

A Survey on Performance, Current and Future Usage of Vehicle-To-Everything Communication Standards

Wireless communication between road users is essential for environmental perception, reasoning, and mission planning to enable fully autonomous vehicles, and thus improve road safety and transport efficiency. To enable collaborative driving, the concept of vehicle-to-Everything (V2X) has long been introduced to the industry. Within the last two decades, several communication standards have been developed based on IEEE 802.11p and cellular standards, namely Dedicated Short-Range Communication (DSRC), Intelligent Transportation System G5 (ITS-G5), and Cellular- and New Radio- Vehicle-to-Everything (C-V2X and NR-V2X). However, while there exists a high quantity of available publications concerning V2X and the analysis of the different standards, only few surveys exist that summarize these results. Furthermore, to our knowledge, no survey that provides an analysis about possible future trends and challenges for the global implementation of V2Xexists. Thus, this contribution provides a detailed survey on Vehicle-to-Everything communication standards, their performance, current and future applications, and associated challenges. Based on our research, we have identified several research gaps and provide a picture about the possible future of the Vehicle-to-Everything communication domain.

cs.ET↗

arXiv2024

Knowledge Graphs in the Digital Twin: A Systematic Literature Review About the Combination of Semantic Technologies and Simulation in Industrial Automation

The ongoing digitization of the industrial sector has reached a pivotal juncture with the emergence of Digital Twins, offering a digital representation of physical assets and processes. One key aspect of those digital representations are simulation models, enabling a deeper insight in the assets current state and its characteristics. This paper asserts that the next evolutionary step in this digitization journey involves the integration of intelligent linkages between diverse simulation models within the Digital Twin framework. Crucially, for the Digital Twin to be a scalable and cost-effective solution, there is a pressing need for automated adaption, (re-)configuration, and generation of simulation models. Recognizing the inherent challenges in achieving such automation, this paper analyses the utilization of knowledge graphs as a potentially very suitable technological solution. Knowledge graphs, acting as interconnected and interrelated databases, provide a means of seamlessly integrating different data sources, facilitating the efficient integration and automated adaption of data and (simulation) models in the Digital Twin. We conducted a comprehensive literature review to analyze the current landscape of knowledge graphs in the context of Digital Twins with focus on simulation models. By addressing the challenges associated with scalability and maintenance, this research contributes to the effective adaption of Digital Twins in the industrial sector, paving the way for enhanced efficiency, adaptability, and resilience in the face of evolving technological landscapes.

cs.CE↗

arXiv2024

Illustrating the benefits of efficient creation and adaption of behavior models in intelligent Digital Twins over the machine life cycle

The concept of the Digital Twin, which in the context of this paper is the virtual representation of a production system or its components, can be used as a "digital playground" to master the increasing complexity of these assets. One of the central subcomponents of the Digital Twin are behavior models that can enable benefits over the entire machine life cycle. However, the creation, adaption and use of behavior models throughout the machine life cycle is very time-consuming, which is why approaches to improve the cost-benefit ratio are needed. Furthermore, there is a lack of specific use cases that illustrate the application and added benefit of behavior models over the machine life cycle, which is why the universal application of behavior models in industry is still lacking compared to research. This paper first presents the fundamentals, challenges and related work on Digital Twins and behavior models in the context of the machine life cycle. Then, concepts for low-effort creation and automatic adaption of Digital Twins are presented, with a focus on behavior models. Finally, the aforementioned gap between research and industry is addressed by demonstrating various realized use cases over the machine life cycle, in which the advantages as well as the application of behavior models in the different life cycle phases are shown.

cs.CE↗

arXiv2024

LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins

This paper presents a novel design of a multi-agent system framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins. This framework features specialized LLM agents tasked with observing, reasoning, decision-making, and summarizing, enabling them to dynamically interact with digital twin simulations to explore parametrization possibilities and determine feasible parameter settings to achieve an objective. The proposed approach enhances the usability of simulation model by infusing it with knowledge heuristics from LLM and enables autonomous search for feasible parametrization to solve a user task. Furthermore, the system has the potential to increase user-friendliness and reduce the cognitive load on human users by assisting in complex decision-making processes. The effectiveness and functionality of the system are demonstrated through a case study, and the visualized demos and codes are available at a GitHub Repository: https://github.com/YuchenXia/LLMDrivenSimulation

cs.AI↗

arXiv2023

A Modular System Architecture for an Offshore Off-grid Platform for Climate neutral Power-to-X Production in H2Mare

Power-to-X (PtX) products constitute a promising solution component in the defossilisation of hard-to-abate sectors. Where direct electrification is not possible, they can find application as chemical feedstock, easy to transport energy carriers and storage. In this contribution, a modular system architecture for a highly automated operation of an offshore off-grid PtX production platform consisting of five layers (asset, control, communication, digital, application) is presented. The assumed advantage of such an off-grid platform is the good scalability worldwide as well as easy exploitation of renewable potentials in any location. Using the example of a power-to-methanol process, a simplified prototype is outlined.

eess.SY↗

arXiv2022

An Agent-based Realisation for a continuous Model Adaption Approach in intelligent Digital Twins

The trend in industrial automation is towards networking, intelligence and autonomy. Digital Twins, which serve as virtual representations, are becoming increasingly important in this context. The Digital Twin of a modular production system contains many different models that are mostly created for specific applications and fulfil different requirements. Especially simulation models, which are created in the development phase, can be used during the operational phase for applications such as prognosis or operation-parallel simulation. Due to the high heterogeneity of the model landscape in the context of a modular production system, the plant operator is faced with the challenge of adapting the models in order to ensure an application-oriented realism in the event of changes to the asset and its environment or the addition of applications. Therefore, this paper proposes a concept for the continuous model adaption in the Digital Twin of a modular production system during the operational phase. The benefits are then demonstrated by an application scenario and an agent-based realisation.

cs.MA↗

arXiv2022

Intelligent Exploration of Solution Spaces Exemplified by Industrial Reconfiguration Management

Many decision-making approaches rely on the exploration of solution spaces with regards to specified criteria. However, in complex environments, brute-force exploration strategies are usually not feasible. As an alternative, we propose the combination of an exploration task's vertical sub-division into layers representing different sequentially interdependent sub-problems of the paramount problem and a horizontal sub-division into self-sustained solution sub-spaces. In this paper, we present a universal methodology for the intelligent exploration of solution spaces and derive a use-case specific example from the field of reconfiguration management in industry 4.0.

cs.AI↗