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arXiv 2024年

作者： Igelbrink, Felix Renz, Marian Günther, Martin Powell, Piper Niecksch, Lennart Lima, Oscar Atzmueller, Martin Hertzberg, Joachim German Research Center for Artificial Intelligence Research Department Plan-based Robot Control Osnabrück Germany Osnabrück University Institute of Computer Science Knowledge-Based Systems Group Osnabrück Germany Osnabrück University Institute of Computer Science Semantic Information Systems Group Osnabrück Germany

Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely integrated. However, recent advances in deep learning now allow full integration of prior knowledge, represented as knowledge graphs or language concepts, into sensor data processing and semantic mapping pipelines. Semantic scene graphs and language models enable modern semantic mapping approaches to incorporate graph-based prior knowledge or to leverage the rich information in human language both during and after the mapping process. This has sparked substantial advances in semantic mapping, leading to previously impossible novel applications. This survey reviews these recent developments comprehensively, with a focus on online integration of knowledge into semantic mapping. We specifically focus on methods using semantic scene graphs for integrating symbolic prior knowledge and language models for respective capture of implicit common-sense knowledge and natural language concepts. © 2024, CC BY-NC-ND.

关键词： knowledge graph

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IdentiBug: Model-Driven Visualization of Bug Reports by Extracting Class Diagram Excerpts

IdentiBug: Model-Driven Visualization of Bug Reports by Extr...

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IEEE International Conference on systems, Man and Cybernetics

作者： Gelareh Meidanipour Lahijany Manuel Ohrndorf Johannes Zenkert Madjid Fathi Udo Kelte Software Engineering Group University of Siegen Germany Knowledge-Based Systems and Knowledge Management University of Siegen Germany

ISBN: (纸本)9781665442084

Bug reports are essential software artifacts that describe software bugs using natural language. Bug localization tools can help developers to understand the relation between bug reports and a software system. However, most approaches for localizing bugs work with unstructured textual information from the source codes and bug reports. This paper proposes an approach for locating and visualizing bug reports based on class diagrams representing the overall structural design of a software system. Our approach called IdentiBug takes advantage of deep learning techniques to train our bug localization model to predict connections between a bug report and the system’s class diagram. The result is a ranked list of classes from which we extract and rank a list of class diagram excerpts for assisting the developers during bug documentation and localization.

关键词： Location awareness Visualization Schedules Codes Computer bugs Unified modeling language Natural languages

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Brain-Controlled Computer Tasks for Paralyzed Persons: Framework Overview

Brain-Controlled Computer Tasks for Paralyzed Persons: Frame...

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2023 International Conference on Industry Sciences and Computer Science Innovation, iSCSi 2023

作者： Imran, Amna Chaudhary, Haider Tariq, Maimoona Ali, Zahra Ahad, Abdul Naqvi, Hammad Coelho, Paulo Jorge Pires, Ivan Miguel Knowledge Unit of Science and Technology University of Management and Technology Sialkot Pakistan School of Software Northwestern Polytechnical University Shaanxi Xian710072 China Department of Computer Science Istanbul Technical University Turkey Polytechnic of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra Coimbra Portugal Instituto de Telecomunicações Universidade da Beira Interior Covilhã Portugal

To bridge the digital barrier between those with disabilities and those without impairments, several applications can be operated by brain signals. It highlights the importance of employing brain-computer interfaces (BCI) and mentions a specific system called the "Brain-Controlled Computer System" that can control a computer cursor and perform mouse actions using brain signals.. The system can control the cursor and provide mouse left/right click with brain signals using a low-cost Brainlink Lite device. based on this system, we employed machine learning and deep learning methodologies to develop a system that classifies brain signals for cursor movement. The methodology involved gathering user input through surveys, interviews, and comparison studies, as well as utilizing pre-developed datasets collected using a mind sensor. We initially trained a model on a previous dataset, achieving approximately 70% accuracy. However, when applied to the new dataset, the accuracy dropped to 49%. To improve the results, we experimented with more complex models, including additional layers and a dropout layer to prevent overfitting. Despite these enhancements, the accuracy only reached a maximum of 52%. The research team collected a dataset tailored to their specific application. They made a groundbreaking discovery by applying a convolutional neural network (CNN), typically used for image analysis, to process continuous brainwave data. Despite this non-standard use, their hypothesis proved correct, with the CNN model achieving an impressive 80% accuracy in classifying brain signals for controlling a cursor. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Brain computer interface

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Analysis and illustration of the practical impact of Artificial Intelligence and Intelligent Personal Assistants on business processes in small- and medium-sized service enterprises

Analysis and illustration of the practical impact of Artific...

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IEEE International Conference on systems, Man and Cybernetics

作者： Daniel Hüsson Alexander Holland Madjid Fathi Rocío Arteaga Sánchez FOM Institute for IT-Management and Digitalization Essen Germany Faculty IV Knowledge Based Systems and Knowledge Management University Siegen Germany UCAM - Catholic University of Murcia Spain

ISBN: (纸本)9781665442084

Artificial intelligence (AI) and intelligent personal assistants (IPAs) are becoming more and more important. This is no longer limited to private use but also becoming increasingly important in everyday business life. The identification of optimization potentials through the use of AI and IPAs is therefore relevant from both a theoretical and a practical point of view. This paper, therefore, identifies concrete use cases for relevant processes in small- and medium sized enterprises (SME) in the service industry and enhances them with AI and IPA capabilities. based on a prototype, the use cases were presented to 10 experts who were interviewed regarding their usefulness and influencing factors. Subsequently, the results of the interviews were categorized and validated again by a quantitative survey within the expert panel. As a result, the use cases were evaluated with regard to the specific influencing factors and the potential for optimization was determined. The use cases were evaluated based on this data. It was shown that IPA features in particular are perceived as useful. On average, AI and IPA features have a cost savings potential of over 31%. This shows the importance of these features and the need to consider them when modeling modern business processes.

关键词： Time-frequency analysis Costs Navigation Prototypes Companies Artificial intelligence Personal digital assistants

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Deep Koopman with Control: Spectral Analysis of Soft Robot Dynamics

arXiv

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arXiv 2022年

作者： Komeno, Naoto Michael, Brendan Küchler, Katharina Anarossi, Edgar Matsubara, Takamitsu Robot Learning Lab Nara Institue of Science and Technology Japan Institute of Knowledge Based Systems Group Rwth Aachen University Germany

Soft robots are challenging to model and control as inherent non-linearities (e.g., elasticity and deformation), often requires complex explicit physics-based analytical modelling (e.g., a priori geometric definitions). While machine learning can be used to learn non-linear control models in a datadriven approach, these models often lack an intuitive internal physical interpretation and representation, limiting dynamical analysis. To address this, this paper presents an approach using Koopman operator theory and deep neural networks to provide a global linear description of the non-linear control systems. Specifically, by globally linearising dynamics, the Koopman operator is analyzed using spectral decomposition to characterises important physics-based interpretations, such as functional growths and oscillations. Experiments in this paper demonstrate this approach for controlling non-linear soft robotics, and shows model outputs are interpretable in the context of spectral analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrum analysis

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Detecting spatio-temporal Relations by Combining a Semantic Map with a Stream Processing Engine

Detecting spatio-temporal Relations by Combining a Semantic ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Lennart Niecksch Henning Deeken Thomas Wiemann German Research Center for Artificial Intelligence (DFKI) DFKI Niedersachsen Plan-based Robot Control Group Osnabrück Germany Knowledge Based Systems and Autonomous Robotics groups Institute of Computer Science Osnabrück University Osnabrück Germany

Changes in topological spatial relations of objects are often strong indicators for state transitions in the underlying processes they are involved in. While various aspects of semantic mapping have been extensively researched, the reasoning about the temporal development of spatial relations of instances is often neglected. This paper presents a concept to combine a semantic map with a stream processing framework for live analysis of the spatio-temporal relation of objects, based on the map and information inferred from sensors streams. To demonstrate the functionality of our concept, we implemented a proof-of-concept system to track everyday events in an office environment. The presented application scenario clearly demonstrates the benefits of the proposed architecture for detecting and handling complex spatio-temporal events.

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Mesh-based Object Tracking for Dynamic Semantic 3D Scene Graphs via Ray Tracing

arXiv

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arXiv 2024年

作者： Niecksch, Lennart Mock, Alexander Igelbrink, Felix Wiemann, Thomas Hertzberg, Joachim German Research Centre for Artificial Intelligence Plan-Based Robot Control Osnabrück Germany Osnabrück University Institute of Computer Science Knowledge-Based Systems Osnabrück Germany Fulda University of Applied Sciences Department of Applied Computer Science Robotics in Computer Science Fulda Germany

In this paper, we present a novel method for 3D geometric scene graph generation using range sensors and RGB cameras. We initially detect instance-wise keypoints with a YOLOv8s model to compute 6D pose estimates of known objects by solving PnP. We use a ray tracing approach to track a geometric scene graph consisting of mesh models of object instances. In contrast to classical point-to-point matching, this leads to more robust results, especially under occlusions between objects instances. We show that using this hybrid strategy leads to robust self-localization, pre-segmentation of the range sensor data and accurate pose tracking of objects using the same environmental representation. All detected objects are integrated into a semantic scene graph. This scene graph then serves as a front end to a semantic mapping framework to allow spatial reasoning. Copyright © 2024, The Authors. All rights reserved.

关键词： Mesh generation

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Feature Ranking for the Prediction of Energy Consumption on CNC Machining Processes

Feature Ranking for the Prediction of Energy Consumption on ...

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Hafez Kader Robin Ströbel Alexander Puchta Jürgen Fleischer Benjamin Noack Myra Spiliopoulou Autonomous Multisensor Systems Group Institute for Intelligent Cooperating Systems Otto von Guericke University Magdeburg Germany Institute of Production Science Karlsruhe Institute of Technology(KIT) Germany Knowledge Management and Discovery Lab Faculty of Computer Science Otto von Guericke University Magdeburg Germany

ISBN: (数字)9798350368031

ISBN: (纸本)9798350368048

Energy consumption is a critical factor that nega-tively impacts the environment. Sustainable production is essen-tial for addressing the climate crisis, as low-emission manufacturing can both reduce costs and minimize environmental impact. Energy-efficient CNC machine tools significantly contribute to achieving ambitious environmental objectives. In recent years, numerous studies have focused on low-energy consumption production, analyzing factors that contribute to sustainable manufacturing. When using the analytical or empirical model, factors and corrections might be omitted. With advancements in machine learning and the increasing availability of large datasets, models are being developed to predict energy consumption with high accuracy. However, these models often overlook the importance of features that contribute to a transparent prediction process and their influence on the results. In our paper, a LSTM model is initially utilized to predict the energy consumption of CNC machines. Following this, a method is devised to rank the features based on their predictive power, considering temporal variations. We show that some of the features ranked in the top positions agree with independent literature findings, while others are new and demand further investigation.

关键词： Energy consumption Accuracy Production Machine learning Predictive models Data models Stability analysis Manufacturing Machine tools Meteorology

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MICP-L: Mesh-based ICP for Robot Localization Using Hardware-Accelerated Ray Casting

MICP-L: Mesh-based ICP for Robot Localization Using Hardware...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Alexander Mock Thomas Wiemann Sebastian Pütz Joachim Hertzberg Knowledge-Based Systems Group Institute of Computer Science Os-nabrück University Osnabrück Germany Robotics in Computer Science Fulda University of Applied Sciences Fulda Germany Nature Robots GmbH Osnabruck Germany Plan-Based Robot Control DFKI Os-nabrück Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Triangle mesh maps are a versatile 3D environment representation for robots to navigate in challenging indoor and outdoor environments exhibiting tunnels, hills and varying slopes. To make use of these mesh maps, methods are needed to accurately localize robots in such maps to perform essential tasks like path planning and navigation. We present Mesh ICP Localization (MICP-L), a novel and computationally efficient method for registering one or more range sensors to a triangle mesh map to continuously localize a robot in 6D, even in GPS-denied environments. We accelerate the computation of ray casting correspondences (RCC) between range sensors and mesh maps by supporting different parallel computing devices like multicore CPUs, GPUs and the latest NVIDIA RTX hardware. By additionally transforming the covariance computation into a reduction operation, we can optimize the initial guessed poses in parallel on CPUs or GPUs, making our implementation applicable in real-time on many architectures. We demonstrate the robustness of our localization approach with datasets from agricultural, aerial, and automotive domains.

关键词： Location awareness Casting Three-dimensional displays Navigation Computer architecture Robot sensing systems Sensors Software reliability State estimation Robots

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Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Ray Tracing for Embedded Hardware on Mobile Robots

Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Ra...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alexander Mock Thomas Wiemann Joachim Hertzberg Knowledge Based Systems Group Institute of Computer Science Osnabrück University Osnabrück Germany Autonomous Robotics Group Institute of Computer Science Osnabrück University Osnabrück Germany Plan-based Robot Control DFKI Niedersachsen Osnabrück Germany

Sensor simulation has emerged as a promising and powerful technique to find solutions to many real-world robotic tasks like localization and pose tracking. However, commonly used simulators have high hardware requirements and are therefore used mostly on high-end computers. In this paper, we present an approach to simulate range sensors directly on embedded hardware of mobile robots that use triangle meshes as environment map. This library, called Rmagine, allows a robot to simulate sensor data for arbitrary range sensors directly on board via ray tracing. Since robots typically only have limited computational resources, Rmagine aims at being flexible and lightweight, while scaling well even to large environment maps. It runs on several platforms like Laptops or embedded computing boards like NVIDIA Jetson by putting an unified API over the specific proprietary libraries provided by the hardware manufacturers. This work is designed to support the future development of robotic applications depending on simulation of range data that could previously not be computed in reasonable time on mobile systems.

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