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Software-defined event-based camera and vision algorithms for digitizing highly dynamic manufacturing tasks

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Software-defined event-based camera and vision algorithms fo...

2024 Low-Cost Digital Solutions for Industrial Automation, LoDiSA 2024

作者： Walker, Emily Turner, Christopher J. Oyekan, John Department of Automatic Control & Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Surrey Business School The University of Surrey Surrey GuildfordGU2 7XH United Kingdom Real Time & Distributed Systems Group Department of Computer Science University of York Heslington YorkYO10 5GH United Kingdom

ISBN: (纸本)9781837242672

Event-based cameras are inspired by the way the retina in the eye process information. Rather than capturing image frames at a constant rate, event cameras generate data asynchronously, based on changes in pixel value between frames. As a result, these cameras have a high spatiotemporal accuracy, low power constraints and reduced artefacts from motion blur. Hence, they represent favourable properties for digitizing highly dynamic manufacturing tasks such as machine vibrations and rapid human assembly actions. However, the costs of event-based cameras are prohibitively high for small to medium sized enterprises (SMEs). In this research, we present the development of a rig that combines low-cost visual sensors with low-cost algorithms to perform action recognition for highly dynamic manual manufacturing tasks. We applied our algorithms to achieve an action classification system via a novel threshold-based approach and in parallel use a neural network to perform classification of objects in the video feed. The results achieved demonstrated that our approach was able to successfully identify various rapid manual tasks being performed on identified objects. In addition to SMEs, our approach would be suited for providing low-cost digitization on legacy machines. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/).

关键词： Machine vibrations

UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-generation Framework

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arXiv 2023年

作者： Amer, Abdelhakim Álvarez-Tuñón, Olaya Ugurlu, Halil Ibrahim le Fevre Sejersen, Jonas Brodskiy, Yury Kayacan, Erdal The Department of Electrical Engineering and Computer Engineering Aarhus University Aarhus8200 Denmark EIVA a/s Skanderborg8660 Denmark The Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

Underwater robotic surveys can be costly due to the complex working environment and the need for various sensor modalities. While underwater simulators are essential, many existing simulators lack sufficient rendering quality, restricting their ability to transfer algorithms from simulation to real-world applications. To address this limitation, we introduce UNav-Sim, which, to the best of our knowledge, is the first simulator to incorporate the efficient, high-detail rendering of Unreal Engine 5 (UE5). UNav-Sim is open-source1 and includes an autonomous vision-based navigation stack. By supporting standard robotics tools like ROS, UNav-Sim enables researchers to develop and test algorithms for underwater environments efficiently. © 2023, CC BY.

关键词： Data visualization

An Approach for Stock Price Prediction Based on Improved Eemd and Attention-Enhanced Bilstm

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SSRN

SSRN 2024年

作者： Liu, Zhinzhong Su, Zhaohui Shang, Ling Sun, Hongxiang Zhao, Bin School of Computer and Control Engineering Yantai University Yantai264005 China Digital Smart Creative Department Henan Culture and Tourism Investment Group Co. Ltd Luoyang471026 China

Accurate stock price prediction is a urgently needed technology in economic investment. However, stock prices are influenced by various factors and display non-linear and non-stationary characteristics, which makes accurate prediction of stock prices a critical challenge. To tackle the above issue, we propose a method for stock price prediction based on improved ensemble empirical mode decomposition (EEMD) and attention-enhanced BiLSTM (AEBiLSTM)(named as SPPEA). First, to reduce the nonlinearity of the stock prices and improve the prediction accuracy, our method first divides the original stock price series into sub-sequences with the same time scale using a sliding window. These sub-sequences are then decomposed into multiple intrinsic mode Function (IMF) components with the EEMD method which is improved by cubic spline interpolation. Then, the decomposed IMF components are input into AEBiLSTM to capture the dependencies of stock prices data and the features of important points of the stock prices, and then the prediction results of each IMF component are obtained with AEBiLSTM. Finally, the prediction results of each component are integrated to obtain the final prediction results. Extensive experiments have been conducted on three stock datasets of BYD, China Unicom and Ping An Bank, experimental results prove that our proposed method has better performance compared with the state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Empirical mode decomposition

Invisible Servoing: a Visual Servoing Approach with Return-Conditioned Latent Diffusion

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arXiv 2024年

作者： Gerges, Bishoy Bazzana, Barbara Botteghi, Nicolò Aboudorra, Youssef Franchi, Antonio Robotics and Mechatronics Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7500AE Netherlands Department of Applied Mathematics University of Twente Enschede7500AE Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy

In this paper, we present a novel visual servoing (VS) approach based on latent Denoising Diffusion Probabilistic Models (DDPMs). Opposite to classical VS methods, the proposed approach allows reaching the desired target view, even when the target is initially not visible. This is possible thanks to the learning of a latent representation that the DDPM uses for planning and a dataset of trajectories encompassing target-invisible initial views. The latent representation is learned using a Cross-Modal Variational Autoencoder, and used to estimate the return for conditioning the trajectory generation of the DDPM. Given the current image, the DDPM generates trajectories in the latent space driving the robotic platform to the desired visual target. The approach is applicable to any velocity-based controlled platform. We test our method with simulated and real-world experiments using generic multi-rotor Uncrewed Aerial Vehicles (UAVs). A video of our experiments can be found at https://***/yu-aTxqceOA. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency control

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Reinforcement Learning-based Output Structured Feedback for ...

American control Conference (ACC)

作者： Kyung-Bin Kwon Sayak Mukherjee Hao Zhu Thanh Long Vu Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA Optimization and Control Group Pacific Northwest National Laboratory Richland WA USA

Load frequency control (LFC) is a key factor to maintain stable frequency in multi-area power systems. As modern power systems evolve from a centralized to distributed paradigm, LFC needs to consider the peer-to-peer (P2P) based scheme that considers limited information from the information-exchange graph for the generator control of each interconnected area. This paper aims to solve a data-driven constrained LQR problem with mean-variance risk constraints and output structured feedback, and applies this framework to solve the LFC problem in multi-area power systems. By reformulating the constrained optimization problem into a minimax problem, the stochastic gradient descent max-oracle (SGDmax) algorithm with zero-order policy gradient (ZOPG) is adopted to find the optimal feedback gain from the learning, while guaranteeing convergence. In addition, to improve the adaptation of the proposed learning method to new or varying models, we construct an emulator grid that approximates the dynamics of a physical grid and performs training based on this model. Once the feedback gain is obtained from the emulator grid, it is applied to the physical grid with a robustness test to check whether the controller from the approximated emulator applies to the actual system. Numerical tests show that the obtained feedback controller can successfully control the frequency of each area, while mitigating the uncertainty from the loads, with reliable robustness that ensures the adaptability of the obtained feedback gain to the actual physical grid.

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Modular Model Reduction of Interconnected Systems: A Top-Down Approach

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arXiv 2023年

作者： Janssen, Lars A.L. Besselink, Bart Fey, Rob H.B. van de Wouw, Nathan Dynamics and Control group Department of Mechanical Engineering Eindhoven University of Technology Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Netherlands

Models of complex systems often consist of multiple interconnected subsystem/component models that are developed by multi-disciplinary teams of engineers or scientists. To ensure that such interconnected models can be applied for the purpose of simulation and/or control, a reduced-order model for the interconnected dynamics is needed. In the scope of this paper, we pursue this goal by subsystem reduction to warrant modularity of the reduction approach. Clearly, by reducing the complexity of the subsystem models, not only the accuracy of the subsystem models is affected, but, consequently, also the accuracy of the interconnected model. It is typically difficult to predict a priori how the interconnected model accuracy is affected precisely by the subsystem reduction. In this work, we address this challenge by introducing a top-down approach which enables the translation of given accuracy requirements of the interconnected system model to accuracy requirements at subsystem model level, by using mathematical tools from robust performance analysis. This allows for the independent reduction of subsystem models while guaranteeing the desired accuracy of the interconnected model. In addition, we show how this top-down approach can be used to significantly reduce the interconnected model in an illustrative structural dynamics case study. © 2023, CC BY-NC-ND.

关键词： Large scale systems

Visual Tracking Nonlinear Model Predictive control Method for Autonomous Wind Turbine Inspection

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arXiv 2023年

作者： Amer, Abdelhakim Mehndiratta, Mohit le Fevre Sejersen, Jonas Pham, Huy Xuan Kayacan, Erdal The Department of Electrical and Computer Engineering Aarhus University Aarhus C8000 Denmark GIM Robotics Espoo Finland The Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

Automated visual inspection of on-and offshore wind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framework alongside a global wind turbine path planner is proposed to achieve distance-optimal coverage for wind turbine inspection. Unlike traditional MPC formulations, visual tracking NMPC (VT-NMPC) is designed to track an inspection surface, instead of a position and heading trajectory, thereby circumventing the need to provide an accurate predefined trajectory for the drone. An additional capability of the proposed VT-NMPC method is that by incorporating inspection requirements as visual tracking costs to minimize, it naturally achieves the inspection task successfully while respecting the physical constraints of the drone. Multiple simulation runs and real-world tests demonstrate the efficiency and efficacy of the proposed automated inspection framework, which outperforms the traditional MPC designs, by providing full coverage of the target wind turbine blades as well as its robustness to changing wind conditions. The implementation codes are open-sourced. © 2023, CC BY.

关键词： Inspection

State Derivative Normalization for Continuous-Time Deep Neural Networks

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arXiv 2024年

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Görges, Daniel Tóth, Roland Schoukens, Maarten Ruskowski, Martin Chair of Machine Tools and Control Systems RPTU Kaiserslautern The German Research Center for Artificial Intelligence Kaiserslautern Germany Group The Department of Electrical Engineering Eindhoven University of Technology Netherlands The Systems and Control Laboratory The Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. © 2024, CC BY-SA.

关键词： Ordinary differential equations

CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival Prediction for Mobile Robots

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CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival...

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

作者： Jonas le Fevre Sejersen Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

This study presents the conflict-aware multi-agent estimated time of arrival (CAMETA) framework, a novel approach for predicting the arrival times of multiple agents in unstructured environments without predefined road infrastructure. The CAMETA framework consists of three components: a path planning layer generating potential path suggestions, a multi-agent ETA prediction layer predicting the arrival times for all agents based on the paths, and lastly, a path selection layer that calculates the accumulated cost and selects the best path. The novelty of the CAMETA framework lies in the heterogeneous map representation and the heterogeneous graph neural network architecture. As a result of the proposed novel structure, CAMETA improves the generalization capability compared to the state-of-the-art methods that rely on structured road infrastructure and historical data. The simulation results demonstrate the efficiency and efficacy of the multi-agent ETA prediction layer, with a mean average percentage error improvement of 29.5% and 44% when compared to a traditional path planning method (A *) which does not consider conflicts. The performance of the CAMETA framework shows significant improvements in terms of robustness to noise and conflicts as well as determining proficient routes compared to state-of-the-art multi-agent path planners.

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