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检索条件"机构=Intelligent Systems and Production Engineering FZI Research Center for Information Technology"

共 81 条记录，以下是1-10 订阅

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Enhancing Robot Arm Trajectory Tracking through Reinforcement Learning based Compensation 4

Enhancing Robot Arm Trajectory Tracking through Reinforcemen...

引用

4th International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Sun, Chenwei Ovtcharova, Jivka Intelligent Systems and Production Engineering FZI Research Center for Information Technology Karlsruhe Germany

ISBN: (纸本)9798350391183

This paper proposes a reinforcement learning (RL) based compensator for minimising the trajectory tracking deviation of a robot arm under external disturbances. The compensator adaptively incorporates the weighted joint position errors into the reference joint position values, with the weighting factor provided by the RL action output. The effectiveness of the method has been verified on a simulated robot model with realistic physical properties in Gazebo simulator. The focus of this study is on evaluating the linear tracking performance of the robot under both sinusoidal and constant forces applied to the its end-effector. The experiment results show that the proposed method can reduce the mean tool center point (TCP) trajectory deviation over 44%. The result also demonstrate the feasibility of compensation without requiring the TCP position information, using only the robot joint information, offering a more cost-efficient solution. © 2024 IEEE.

关键词： Reinforcement learning

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Designing Concept Drift Detection Ensembles: A Survey 10

Designing Concept Drift Detection Ensembles: A Survey

引用

10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023

作者： Trat, Martin Ovtcharova, Jivka Fzi Research Center for Information Technology Intelligent Systems and Production Engineering Germany

ISBN: (纸本)9798350345032

Data streams represent real-world concepts that typically cannot be assumed to be static. Instead, sensors monitoring industrial resources deteriorate, network intrusion attacks exhibit new patterns, consumer behavior or public interest in news topics change. These are examples of a phenomenon commonly known as concept drift. It threatens the performance of estimation models conducting inference on associated data. Explicitly employed concept drift detection is among the most promising approaches to maintain a stable and robust performance of productively used estimators, as it identifies the times when adaptation to new concepts becomes actually necessary. Combining concept drift detectors to form ensembles can additionally increase detection performance as it aggregates their individual specializations and allows advanced processing of concept drift evidence. This survey is the first to systematically retrieve and overview research relevant in the field of concept drift detection ensembles. It identifies the research as being in its initial stages and reveals numerous gaps, which should be addressed to further exploit the substantial performance potential ensembles have over individually applied detectors. Furthermore, it contributes to the literature by discussing and describing key characteristics and principles of their design in a structured manner. © 2023 IEEE.

关键词： Consumer behavior

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Enhancing Robot Arm Trajectory Tracking through Reinforcement Learning based Compensation

Enhancing Robot Arm Trajectory Tracking through Reinforcemen...

引用

Electrical, Computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Chenwei Sun Jivka Ovtcharova Intelligent Systems and Production Engineering FZI Research Center for Information Technology Karlsruhe Germany

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

关键词： Adaptation models Mechatronics Trajectory tracking Error compensation Reinforcement learning End effectors Trajectory

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Designing Concept Drift Detection Ensembles: A Survey

Designing Concept Drift Detection Ensembles: A Survey

引用

International Conference on Data Science and Advanced Analytics (DSAA)

作者： Martin Trat Jivka Ovtcharova Intelligent Systems and Production Engineering FZI Research Center for Information Technology Germany

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A Hybrid Approach for Object Localization Combining Mask R-CNN and Halcon in an Assembly Scenario 8

A Hybrid Approach for Object Localization Combining Mask R-C...

引用

8th IEEE International Conference on Industrial engineering and Applications, ICIEA 2021

作者： Timmermann, David Heid, Dominik Friedel, Thomas Roennau, Arne Dillmann, Ruediger Intelligent Systems and Production Engineering Fzi Research Center for Information Technology Karlsruhe Germany

ISBN: (纸本)9781665428958

Robust and accurate object localization is a key component in the context of robotic grasping. Especially in collaborative assembly scenarios it is important that the robot is able to detect objects precisely to grasp them quickly and reliable. Therefore, classic image-based methods for object localization are not always applicable, as they may experience bad localization rates in large scenes or long runtimes at high camera resolutions. The focus of this paper is to provide a hybrid approach for the localization of objects in a scene by using a deep neural network for classification and segmentation of objects and classic shape-based matching for exact localization in the detected region of interest. We use Mask R-CNN as a deep neural network pixel based classifier and Halcon as an example for a well-known classic image processing program. Last but not least the solution is evaluated in comparison to only using Halcon by evaluating the performance regarding execution time and reliability of the object pose localization. © 2021 IEEE.

关键词： Deep neural networks

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AI4Assembly A Human-Robot Collaboration Assembly Application with AI Support 8

AI4Assembly A Human-Robot Collaboration Assembly Application...

引用

8th IEEE International Conference on Industrial engineering and Applications, ICIEA 2021

作者： Timmermann, David Tanev, Atanas Roennau, Arne Dillmann, Ruediger Intelligent Systems and Production Engineering Fzi Research Center for Information Technology Karlsruhe Germany

ISBN: (纸本)9781665428958

Artificial intelligence (AI) technologies can help to greatly improve the usability and performance of an application. Over the last few years, many approaches have been presented on how AI technologies can improve individual steps in an assembly application that uses human-robot collaboration. This paper presents a concept for an AI supported human-robot collaboration for an assembly application, in which the AI components extend and improve the assembly application in all areas and not just individual steps. This includes the setup of a new assembly sequence as well as the subsequent execution of the assembly. The implemented concept is also compared against the same application with a lower degree of automation. © 2021 IEEE.

关键词： Robots

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Longitudinal Vehicle Motion Prediction in Traffic Junctions Using a Data-Driven Multi-Object intelligent Driver Model 27

Longitudinal Vehicle Motion Prediction in Traffic Junctions ...

引用

27th IEEE International Conference on intelligent Transportation systems, ITSC 2024

作者： Buyer, Johannes Wetzel, Konstantin Zollner, Raoul Zollner, J. Marius Heilbronn University of Applied Sciences Faculty Engineering Heilbronn74081 Germany Fzi Research Center for Information Technology Department of Technical Cognitive Assistance Systems Karlsruhe76131 Germany

ISBN: (纸本)9798331505929

The paper presents an approach for longitudinal interaction-aware vehicle motion prediction using an intelligent Driver Model (IDM) with a data-driven extension, which is able to consider several traffic participants. The basic prediction concept for that approach was originally designed for multi-lane traffic scenarios in the authors' previous work [1], [2]. The approach is based on model knowledge, probabilistic estimation as well as machine learning components. The innovation of the paper at hand is the transfer of the concept for its usage in traffic junctions. Thereby, a decision tree is developed that processes different interaction features in order to select the relevant traffic participants. To estimate the model parameter values, a Sequential Importance Resampling (SIR) particle filter is used. Time-varying parameters which weight the selected objects considered in the multi-object IDM are adapted over the prediction horizon using a trained support vector machine (SVM) regression model. For validation, roundabout motion trajectories of the INTERACTION dataset [3] are used. Since the proposed prediction approach is able to consider vehicles' turn signal status, an augmentation of the dataset with turn signal attributes is realized. The accuracy of the prediction approach is compared to baseline methods by calculating the mean absolute error (MAE) of predicted vehicle speeds. © 2024 IEEE.

关键词： Prediction models

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NavPathNet: Onboard Trajectory Prediction for Two-Wheelers using Navigation Maps

NavPathNet: Onboard Trajectory Prediction for Two-Wheelers u...

引用

IEEE Symposium on intelligent Vehicle

作者： Fabian Immel Alessandro Moia Haohao Hu Intelligent Systems and Production Engineering FZI Research Center for Information Technology Karlsruhe Germany Robert Bosch GmbH Gerlingen-Schillerhoehe Germany Institute of Measurement and Control Systems Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This work proposes NavPathNet, a graph neural network based trajectory prediction system capable of accurately predicting the trajectory of a two-wheeler with onboard sensors for up to 6 seconds using the vehicle state and only navigation map information. Possible paths are generated from the local road network in a regular navigation map using Bézier curves and a multimodal prediction captures the different possibilities of the road network. A kinematic model integrated into the network allows to give guarantees on physical realism and on motion constraints, essential for safety-relevant *** proposed system was trained and evaluated on an in-house e-bike data set and was able to reach a top 1 final displacement error of 2 m for four seconds and 3.8 m for six seconds of prediction time, significantly outperforming other baselines. The improvement in prediction quality compared to purely physical models opens up new possibilities for driver assistance systems in connected vehicles.

关键词： Connected vehicles Navigation intelligent vehicles Roads Kinematics Predictive models Graph neural networks

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Flexible Automotive Power Management: Formalization of an Auction-based Approach

Flexible Automotive Power Management: Formalization of an Au...

引用

IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Tobias Schürmann Max Grobbel Sören Hohmann Embedded Systems and Sensors Engineering FZI Research Center for Information Technology Karlsruhe Germany Institute of Control Systems Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798331541606

ISBN: (纸本)9798331541613

The electrification of former mechanical components and the expansion of infotainment systems lead to an increasing automotive power network (APN) in terms of components and power consumption. Additionally, customers desire a flexible hardware composition. As a consequence, automotive power management has to ensure power balancing for volatile generation and demand and to support the integration of new hardware during development, vehicle configuration, and even after production. In this paper, we propose an adapted management architecture, formalize an auction framework for automotive power management of APNs with multiple voltage levels, and show respective simulation results.

关键词： HVAC Power demand Power system management Simulation Voltage Propulsion Hardware Temperature control Resource management Automotive engineering

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Unsupervised Anomaly Detection and Root Cause Analysis for an Industrial Press Machine based on Skip-Connected Autoencoder

Unsupervised Anomaly Detection and Root Cause Analysis for a...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Chenwei Sun Martin Trat Janek Bender Jivka Ovtcharova George Jeppesen Jan Bär Intelligent Systems and Production Engineering FZI Research Center for Information Technology Karlsruhe Germany Dieffenbacher GmbH Maschinen- und Anlagenbau Eppingen Germany

We propose an unsupervised-learning-based method for anomaly detection and root cause analysis for an industrial press machine. A skip-connected autoencoder with 55% performance improvement measured by reconstruction root mean square error to vanilla variant in average is used to train the collected multivariant time series data in different schemes. We then conduct a stacked evaluation method for both machine- level anomalies with the root cause localization and anomaly on specific cylinder tracks. Both real-world and synthetic anomalies embedded in real data are used for evaluation. The result shows that the multi-models training scheme and the relatively short window length can gain better performance, i.e., fewer anomaly false alarms and misses.

关键词： Training Location awareness Root cause analysis Presses Time series analysis Measurement uncertainty Machine learning

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