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Modular Cyber-Physical System for Smart Industry: a case study on energy load disaggregation

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Modular Cyber-Physical System for Smart Industry: a case stu...

International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Jorge Alves Pedro Sousa Bárbara Matos Jérôme Mendes Francisco Souza Tiago Matias Department of Electrical and Computer Engineering University of Coimbra Institute of Systems and Robotics Coimbra Portugal Oncontrol Technologies Coimbra Portugal Dept. Analytical Chemistry & Chemometrics Radboud University Nijmegen AJ Netherlands

Constant changes have marked the industry, and cyber-physical systems have offered great potential in Industry 4.0 on production processes, allowing the integration of computational intelligence and real-time processing. In this context, this paper proposes a modular hardware system for industrial applications according to a plug & play concept. The proposed system comprises a processing module (mPP) and specific expansion modules (mEXs). The mPP is responsible for administrating the network/communication. It collects data from the mEXs, extracts features, processes the features through artificial intelligence, and interacts with the production process. The mEXs comprise sensors, conditioning circuits, and an analog/digital converter. They are responsible for acquiring field signals and their transmission to the mPP. The proposed system is designed for operating with electrical protection, allowing reliable operation of the mPP and mEXs. For evaluating and demonstrating the proposed system in a real scenario, we applied it in a case study for non-intrusive energy load disaggregation.

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Orientation Control with Variable Stiffness Dynamical systems

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arXiv 2023年

作者： Michel, Youssef Saveriano, Matteo Abu-Dakka, Fares J. Lee, Dongheui Chair of Human-Centered Assistive Robotics Technical University of Munich Munich Germany Department of Industrial Engineering University of Trento Trento Italy Munich Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany Autonomous Systems TU Wien Vienna Austria Munich Germany

Recently, several approaches have attempted to combine motion generation and control in one loop to equip robots with reactive behaviors, that cannot be achieved with traditional time-indexed tracking controllers. These approaches however mainly focused on positions, neglecting the orientation part which can be crucial to many tasks e.g. screwing. In this work, we propose a control algorithm that adapts the robot’s rotational motion and impedance in a closed-loop manner. Given a first-order Dynamical System representing an orientation motion plan and a desired rotational stiffness profile, our approach enables the robot to follow the reference motion with an interactive behavior specified by the desired stiffness, while always being aware of the current orientation, represented as a Unit Quaternion (UQ). We rely on the Lie algebra to formulate our algorithm, since unlike positions, UQ feature constraints that should be respected in the devised controller. We validate our proposed approach in multiple robot experiments, showcasing the ability of our controller to follow complex orientation profiles, react safely to perturbations, and fulfill physical interaction tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

Modular Adaptive Aerial Manipulation under Unknown Dynamic Coupling Forces

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arXiv 2024年

作者： Yadav, Rishabh Dev Dantu, Swati Pan, Wei Sun, Sihao Roy, Spandan Baldi, Simone Department of Computer Science The Univerity of Manchester United Kingdom Multi-robot systems group Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Department of Cognitive Robotics Delft Univesrity of Technology Netherlands Robotics Research Center International Institute of Information Technology Hyderabad India School of Mathematics Southeast University Nanjing China

Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. Additionally, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position sub-dynamics, the vehicle attitude sub-dynamics, and the manipulator sub-dynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches. © 2024, CC BY.

关键词： Adaptive control systems

Learning from synthetic data generated with GRADE

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arXiv 2023年

作者： Bonetto, Elia Xu, Chenghao Ahmad, Aamir Max Planck Institute for Intelligent Systems Tübingen Germany Institute of Flight Mechanics and Controls University of Stuttgart Stuttgart Germany Faculty of Mechanical Maritime and Materials Engineering Department of Cognitive Robotics Delft University of Technology Delft Netherlands

Recently, synthetic data generation and realistic rendering has advanced tasks like target tracking and human pose estimation. Simulations for most robotics applications are obtained in (semi)static environments, with specific sensors and low visual fidelity. To solve this, we present a fully customizable framework for generating realistic animated dynamic environments (GRADE) for robotics research, first introduced in [1]. GRADE supports full simulation control, ROS integration, realistic physics, while being in an engine that produces high visual fidelity images and ground truth data. We use GRADE to generate a dataset focused on indoor dynamic scenes with people and flying objects. Using this, we evaluate the performance of YOLO and Mask R-CNN on the tasks of segmenting and detecting people. Our results provide evidence that using data generated with GRADE can improve the model performance when used for a pre-training step. We also show that, even training using only synthetic data, can generalize well to real-world images in the same application domain such as the ones from the TUM-RGBD dataset. The code, results, trained models, and the generated data are provided as open-source at https://***/grade-rr. Copyright © 2023, The Authors. All rights reserved.

关键词： Target tracking

Design and Optimization of Sensing-Communication Integration System in Industrial Automation Based on FDA and PA

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Design and Optimization of Sensing-Communication Integration...

IEEE International Conference on Industrial Technology (ICIT)

作者： Freddy Y.P. Feng De Mi Mingxiang Guan Lichao Sun Hui Tang Chang Liu Pei Xiao Chen Lu Shenzhen Institute of Information Technology Shenzhen China College of Computing Birmingham City University Birmingham United Kingdom School of Engineering Lancaster University Lancaster United Kingdom Extend Robotics Limited Reading United Kingdom Institute for Communication Systems University of Surrey Guildford United Kingdom

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Industrial automation is a rapidly evolving field that aims to improve efficiency, productivity, and safety in various verticals. The integration of sensing and communication technologies has become crucial in this automation process. Re-cent advancements in Frequency Diverse Array (FDA) antennas have shown promising performance advantages over traditional phased array antennas in certain aspects, e.g., the FDA-based radar can focus its beam in both angle and range domains simultaneously. In this paper, we propose a novel architecture for sensing-communication integration in the context of industrial automation, where FDA antennas are employed as the radar sensing array, while phased array antennas are utilized for communication purposes. The integration of FDA antennas brings benefits such as more accurate object detection, improved spatial resolution, and efficient data transmission. We present a comprehensive analysis of the proposed architecture, including design considerations, system modeling, and performance evalu-ation. The results demonstrate the potential of FDA antennas in enhancing the sensing-communication integration in industrial automation, leading to improved efficiency, productivity, and safety in industrial environments.

关键词： Phased arrays Productivity Automation Transmitting antennas Object detection Radar antennas Sensors

Research on Industrial Control Network Security Measurement Method Based on GRU-SVM

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Research on Industrial Control Network Security Measurement ...

2022 Chinese Automation Congress, CAC 2022

作者： Chen, Hengfei Liu, Xianda Sheng, Chuan Pan, Hao College of Information Engineering Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665465335

Since the industrial Internet entered people's field of vision, industrial control network security has gradually received attention. Traditional network security measurement methods rely on the accuracy of situation sequences and historical situation values, and various factors in the network have a great influence on the results. However, compared with industrial control networks, the number and types of affected factors are less than those of the modern Internet. Therefore, based on the research on network security situational awareness, this paper proposes an industrial control network security measurement method model based on GRU-SVM. First, the data sets with threat type labels are classified, and then the similarity scoring mechanism and softmax function are introduced, to process the classification results. For the GRU-SVM classification module, the analysis of the simulation results shows that the convergence is better and the robustness is stronger. For the safety prediction measurement part, by comparing with different models, the mean absolute error and root mean square error are smaller and the reliability is higher. © 2022 IEEE.

关键词： Network security

Extraction of Key Foreground Information from Visual Feedback Images for Contact Micromanipulation in Liquid Environment

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Extraction of Key Foreground Information from Visual Feedbac...

IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Jiancong Chen Huaping Wang Kailun Bai Kaijun Lin Qing Shi Tao Sun Qiang Huang Toshio Fukuda Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of Education Beijing China Science and Technology on Space Physics Laboratory Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

Contact micromanipulation for cells is an important branch in the field of micromanipulation. Limited by the size of the sensor, it is difficult to integrate the sensor in the macroscopic scene into the micromanipulator. So in most operations, images are often the only reliable source of information. The first prerequisite for realizing automated micromanipulation is to be able to extract key information from images. The occlusion phenomenon will inevitably occur in the contact micromanipulation. Although it is possible to design a specific algorithm to identify the target edge information in the occlusion state according to the characteristics of the operating environment and the end-effector. But there is still no universal image processing method to solve this problem. In this paper, we propose an image processing function based on a composite deep learning network structure to solve this problem. Our algorithm is divided into two steps:In the first step, we input the original image into the target detection network to get the position information of the target and end-effector, and cut the region of interest from the original image according to this information. In the second step, we preprocess these candidate sub-images containing key foreground information, and then input them into the image segmentation network to obtain the contour information of the end-effector and target. We designed a cell aspiration experiment based on the digital holographic microscope imaging system to validate our algorithm. In future work, we will continue to improve the algorithm to have better robustness and generalization.

关键词： Visualization Image segmentation Liquids Micromanipulators Microscopy Object detection End effectors

Pallet and Pocket Detection Based on Deep Learning Techniques

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Pallet and Pocket Detection Based on Deep Learning Technique...

Iberian robotics Conference (ROBOT)

作者： Daniele Caldana Artur Cordeiro João Pedro Souza Ricardo B. Sousa Paulo M. Rebelo António Joaquim Silva Manuel F. Silva CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Portugal Faculty of Engineering University of Porto (FEUP) Portugal School of Science and Technology University of Trás-os-Montes e Alto Douro (UTAD) Portugal Seúibal Industrial Complex The Navigator Company Portugal ISEP Polytechnic of Porto Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

The high level of precision and consistency required for pallet detection in industrial environments and logistics tasks is a critical challenge that has been the subject of extensive research. This paper proposes a system for detecting pallets and its pockets using the You Only Look Once (YOLO) v8 Open Neural Network Exchange (ONNX) model, followed by the segmentation of the pallet surface. On the basis of the system a pipeline built on the ROS Action Server whose structure promotes modularity and ease of implementation of heuristics. Additionally, is presented a comparison between the YOLOv5 and YOLOv8 models in the detection task, trained with a customised dataset from a factory environment. The results demonstrate that the pipeline can consistently perform pallet and pocket detection, even when tested in the laboratory and with successive 3D pallet segmentation. When comparing the models, YOLOv8 achieved higher average metric values, with YOLOv8m providing better detection performance in the laboratory setting.

关键词： YOLO Measurement Three-dimensional displays Pipelines Neural networks Production facilities Servers Pallets Robots Logistics

Test-Time Selection for Robust Skin Lesion Analysis

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arXiv 2023年

作者： Bissoto, Alceu Barata, Catarina Valle, Eduardo Avila, Sandra Recod.ai Lab Institute of Computing University of Campinas Brazil Institute for Systems and Robotics Instituto Superior Técnico Portugal School of Electrical and Computing Engineering University of Campinas Brazil Valeo.ai Paris France

Skin lesion analysis models are biased by artifacts placed during image acquisition, which influence model predictions despite carrying no clinical information. Solutions that address this problem by regularizing models to prevent learning those spurious features achieve only partial success, and existing test-time debiasing techniques are inappropriate for skin lesion analysis due to either making unrealistic assumptions on the distribution of test data or requiring laborious annotation from medical practitioners. We propose TTS (Test-Time Selection), a human-in-the-loop method that leverages positive (e.g., lesion area) and negative (e.g., artifacts) keypoints in test samples. TTS effectively steers models away from exploiting spurious artifact-related correlations without retraining, and with less annotation requirements. Our solution is robust to a varying availability of annotations, and different levels of bias. We showcase on the ISIC2019 dataset (for which we release a subset of annotated images) how our model could be deployed in the real-world for mitigating bias. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning