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IEEE International Conference on Industrial technology (ICIT)

作者： Mochammad Rizky Diprasetya Ali Nafih Pullani Andreas Schwung Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

In this paper we propose a novel approach for transfer of model-free Reinforcement learning (RL) methods from simulation to a real-world model of a six-link robotic arm for industrial tasks. We develop an alternative approach to training robots for industrial tasks solely using a simulation of the system instead of the physical model itself as direct training on the physical robot models is a time and energy-consuming endeavor. The use of simulations not only ensures safe and controlled training implementation but also permits parallelization of the environment for improved training speed. As actions learned through the reinforcement learning approach in simulation often underperform in the real world because of the considerable gap in modeling real-world physical effects involved in robot actuation we therefore focuses on developing a iterative learning scheme using increasingly more high-fidelity models of the robot and the subsequent transfer. The experimental results prove the effectiveness of the proposed approach where combining the fine-tuned RL algorithm with the high-fidelity simulation of the robot resulted in a hundred percent task completion rate in simulation and subsequently in the real world.

关键词： Training Service robots Reinforcement learning Manipulators Industrial robots Iterative methods Task analysis

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Self Optimisation and Automatic Code Generation by Evolutionary Algorithms in PLC based Controlling Processes

arXiv

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arXiv 2023年

作者： Löppenberg, Marlon Schwung, Andreas Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany

The digital transformation of automation places new demands on data acquisition and processing in industrial processes. Logical relationships between acquired data and cyclic process sequences must be correctly interpreted and evaluated. To solve this problem, a novel approach based on evolutionary algorithms is proposed to self optimise the system logic of complex processes. Based on the genetic results, a programme code for the system implementation is derived by decoding the solution. This is achieved by a flexible system structure with an upstream, intermediate and downstream unit. In the intermediate unit, a directed learning process interacts with a system replica and an evaluation function in a closed loop. The code generation strategy is represented by redundancy and priority, sequencing and performance derivation. The presented approach is evaluated on an industrial liquid station process subject to a multi-objective optimisation problem. © 2023, CC BY.

关键词： Data handling

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Self Optimisation and Automatic Code Generation by Evolutionary Algorithms in PLC based Controlling Processes

Self Optimisation and Automatic Code Generation by Evolution...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Marlon Löppenberg Andreas Schwung Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany

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Towards Agile Factory automation: Integrating ABB Robot Manipulators, 3D Machine Vision Cameras, and Robot Operating System

Towards Agile Factory Automation: Integrating ABB Robot Mani...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Steve Yuwono Marlon Löppenberg Jakob Steenweg Carsten Cruse Andreas Schwung Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany Bildverarbeitung & Robotik CLK GmbH Altenberge

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

In modern manufacturing, the demand for agile factory automation solutions is prominent. However, the implementation remains challenging, particularly in orchestrating a workflow that integrates diverse modern technologies. This paper introduces a novel integrated workflow for addressing these challenges, which includes the development of a software technology capable of facilitating the integration of robot manipulators, 3D machine vision cameras, and Robot Operating systems. We augment the proposed workflow by incorporating a machine learning framework, specifically MLPro. Furthermore, the paper presents a method for deploying the integrated workflow using container technology, thereby ensuring scalability for real-world large-scale industrial systems. The study begins with an in-depth analysis of state-of-the-art technologies. Subsequently, a comprehensive analysis and discussion of the proposed workflow are provided, also including path planning processes, collision detection mechanisms, machine learning integration, computer vision techniques, and many more. To demonstrate the practical relevance of the proposed workflow, two real-world industrial applications are presented, focusing on the automation of the welding processes for steel looped hooks using dual ABB robot manipulators and for reinforcing steel mesh in concrete stairs using an ABB robot manipulator.

关键词： Three-dimensional displays Service robots Robot vision systems Machine learning Manipulators Cameras Path planning Steel Collision avoidance Manufacturing automation

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MLPro-MPPS - A Versatile and Configurable Production systems Simulator in Python

MLPro-MPPS - A Versatile and Configurable Production Systems...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Steve Yuwono Marlon Löppenberg Detlef Arend Mochammad Rizky Diprasetya Andreas Schwung Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany

In the current era of automation, there has been a growing interest in integrating artificial intelligence into manufacturing systems. A digital representation of the systems is often important to develop, test, and implement learning algorithms. However, developing a digital representation that seamlessly integrates with artificial intelligence remains challenging. The majority of existing simulators lack the capability of such integration or are not available as open-source platforms. To overcome these challenges, we propose MLPro-MPPS, a versatile and configurable Python-based production systems simulations framework integrated with the established machine learning framework, namely MLPro. MLPro offers advanced templates for machine learning in both scientific and industrial contexts, where both MLPro and MLPro-MPPS are open-source frameworks. In this paper, we present the design and fundamentals of MLPro-MPPS, which accurately models the behaviour of real hardware. We demonstrate the potential of MLPro-MPPS in facilitating standardized machine learning within production system simulations and conduct two sample applications of production systems within multiple machine learning methods. The results show that MLPro-MPPS offers a clean and reproducible simulation framework.

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Flexible Activation Bag: learning Activation Functions in Autoencoder Networks

Flexible Activation Bag: Learning Activation Functions in Au...

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IEEE International Conference on Industrial technology (ICIT)

作者： Hendrik Klopries Andreas Schwung Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany

An active area of research in the field of Machine learning is the optimization of network structures, including activation functions. However, selecting a suitable activation function is not that simple and usually requires an elaborate empirical process. Unfortunately, this process is very resource intensive and does not always lead to the best solution. This paper presents a novel approach to select, compose and generate adaptive activation functions without any supervision. The best-suited activation function is learned from a collection of function candidates while training the neural network. Standard activation functions are replaced by a sub-network that outputs an indi-vidually parameterized function. The work raises the question whether modern activation functions that are rectified linear unit shaped are beneficial in unsupervised models. We evaluate our approach in autoencoder structures on industrial data and show performance improvements compared to existing methods. The approach is generally applicable to any task and data domain. Finally, ablation studies on the corresponding parameters under-line the cardinality of the approach and performance impacts.

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Integration of ABB Robot Manipulators and Robot Operating System for Industrial automation

Integration of ABB Robot Manipulators and Robot Operating Sy...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Mochammad Rizky Diprasetya Steve Yuwono Marlon Löppenberg Andreas Schwung Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany

The integration of advanced robotic technologies with modern software platforms has enabled significant advancements in industrial automation. This paper aims to introduce a novel workflow to integrate ABB industrial robot manipulators and the Robot Operating System (ROS), where communication is built through OPC Unified Architecture or a virtual controller with EGM+RWS. Moreover, we also provide a method to deploy the ROS environment using container technology in our workflow. The developed workflow was tested and evaluated in a real-world welding application, demonstrating its capability to automate the welding process of steel looped hooks for supermarket shelves with high accuracy and efficiency, in which the path planner for the robot is obtained from MoveIt by ROS. The introduced workflow can also be integrated with the MLPro framework, which enriches the workflow with machine learning-related algorithms, e.g. for path planning. The results of this study demonstrate the potential of integrating ABB industrial robot manipulators with ROS for industrial process automation and highlight the benefits of using ROS as a software platform for industrial robotics applications.

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Multimodal Synthetic Dataset Balancing: a Framework for Realistic and Balanced Training Data Generation in Industrial Settings

Multimodal Synthetic Dataset Balancing: a Framework for Real...

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Annual Conference of Industrial Electronics Society

作者： Diyar Altinses Andreas Schwung Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany

Deep networks have been successfully applied to industrial applications for clean unimodal data (e.g., sensors, images, or audio). Leveraging multimodal data is a common approach to enhance performance, guided by the principle that a larger quantity of data leads to improvement. However, performance may decline considerably if corruption in the data is present (e.g., noise, blur, failure). Although researchers have explored various data augmentation methods to improve the generalization capacity, these methods are not adapted for industrial settings. The primary distinction is that current augmentation methods are designed to enhance model generalization capabilities and not realistically simulate real-world industry scenarios. In this paper, we present industry-related augmentation methods for temporal and spatial data for multimodal fusion with deep neural networks. Our methods are specifically designed to encourage modality collaboration and reinforce generalization capability. The impact of the proposed data extension strategy to train multimodal fusion models is assessed on a synthetic dataset from an industrial UR5 robot with varying degrees of imbalance. In our study, we analyze different combinations of methods and evaluate their performance. Through these experiments, we are able to identify the challenges in multimodal fusion with deep learning models in an industrial setting.

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Deep Multimodal Fusion with Corrupted Spatio-Temporal Data Using Fuzzy Regularization

Deep Multimodal Fusion with Corrupted Spatio-Temporal Data U...

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Annual Conference of Industrial Electronics Society

作者： Diyar Altinses Andreas Schwung Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany

Deep networks have been successfully applied to unsupervised feature learning and supervised classification and regression for unimodal data (e.g., sensors, images, or audio). Multimodal data is often used to improve the performance of networks according to the slogan: the more, the better. Limited research is available to compensate for corrupted signals from multimodal approaches. In this work, we propose a novel regularization method for deep networks to learn features over multiple modalities designed to compensate for relative sensor weaknesses, such as sensor malfunction, inaccuracy, restricted spatial coverage, and uncertainty. We have used a special augmentation strategy for image and time series modalities to enhance the dataset of underrepresented industrial failure cases. The primary objective is to prevent these cases from negatively impacting the model's predictions. Our approach involves incorporating a fuzzy regularizer that can modify the intensity of activations depending on the quality of the signal, enabling disturbances from various modalities to be identified based on the activations. Our experiments on a simulated Universal Robots UR5 dataset demonstrate the effectiveness of our proposed regularization in increasing the model's stability, accuracy, and generalization to uncertainties and failures in the input signals. By incorporating fuzzy regularization in deep fusion models, their efficiency on complex tasks can be improved while reducing the complexity of the architectures.

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Bag-of-Functions Denoising: Extracting main components in time series

Bag-of-Functions Denoising: Extracting main components in ti...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Hendrik Klopries Andreas Schwung Department of Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany

Denoising sequences of time series is one of the elementary preprocessing steps in data mining. Current statistical methods work on the univariate input data stream and do not obtain long dependencies over the whole series. Due to advanced architectures and learning techniques, machine learning approaches are getting more effective. However, none of the existing methods propose a learnable denoising process to select interpretable components out of a given subset of time dependencies. Therefore, we propose a novel denoising method in the field of machine learning. In particular, we decompose multivariate time series data in a dense representation of selected functional relationships. The decomposition process is assembled as a residual stacking of autoencoders, while the last remainder denotes the extracted noise. We elaborate our approach on a comparison between statistical and machine learning methods, dealing with synthetic as well as industrial data. Beside the performance boost, the approach is capable to learn the main components of a time series as interpretable features in the latent space representation, even on the difficult blind denoising task.

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