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An Energy-Aware Decision-Making Scheme for Mobile Robots on a Graph Map Based on Deep Reinforcement Learning

An Energy-Aware Decision-Making Scheme for Mobile Robots on ...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Gabriele Gemignani Margherita Bongiorni Lorenzo Pollini Dept. of Electrical and Information Engineering DAuSy National Programme for Polytechnic of Bari Bari Italy Department of Information Engineering University of Pisa Pisa Italy University of Pisa Automatic Control University of Pisa

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Autonomous decision-making has always been one of the primary goals to pursue as concerns mobile robots. Researchers of this field have recently turned their attention to Deep Reinforcement Learning (DRL). This paper presents a Double Deep Q Network architecture for managing the high level decisions of a mobile robot involved in a site servicing task. We imagined a scenario where an autonomous service robot must react to alarms due to failures in its area of interest; the robot must have onboard the necessary servicing tool by resorting to a tool change station if needed, reach the area of the failure and fix it, while at the same time handling its battery status. One of the key properties, yet rarely examined, when it comes to robots' long-term independence is the energy-awareness, namely the ability of autonomously managing the charge state as a function of current and future needs. The proposed Deep Q Network training reward scheme is defined specifically to obtain an energy-aware high-level controller, by penalizing both extremely low levels of battery charge as well as unnecessary recharges. The model is numerically simulated on a graph scenario constituted of several failure and charging nodes. Results show that the trained agent always succeeds in reaching the destination without ever incurring in a complete discharge, as it promptly performs temporary stops at charging locations whenever needed.

关键词： Training Wireless communication Service robots Decision making Fault location Deep reinforcement learning Discharges (electric) Batteries Planning Mobile robots

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Trust region policy optimization with optimal transport discrepancies: duality and algorithm for continuous actions 22

Trust region policy optimization with optimal transport disc...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Antonio Terpin Nicolas Lanzetti Batuhan Yardim Florian Dörfler Giorgia Ramponi Automatic Control Laboratory ETH Zürich Dept. of Computer Science ETH Zürich Dept. of Computer Science ETH AI Center

ISBN: (纸本)9781713871088

Policy Optimization (PO) algorithms have been proven particularly suited to handle the high-dimensionality of real-world continuous control tasks. In this context, Trust Region Policy Optimization methods represent a popular approach to stabilize the policy updates. These usually rely on the Kullback-Leibler (KL) divergence to limit the change in the policy. The Wasserstein distance represents a natural alternative, in place of the KL divergence, to define trust regions or to regularize the objective function. However, state-of-the-art works either resort to its approximations or do not provide an algorithm for continuous state-action spaces, reducing the applicability of the method. In this paper, we explore optimal transport discrepancies (which include the Wasserstein distance) to define trust regions, and we propose a novel algorithm - Optimal Transport Trust Region Policy Optimization (OT-TRPO) - for continuous state-action spaces. We circumvent the infinite-dimensional optimization problem for PO by providing a one-dimensional dual reformulation for which strong duality holds. We then analytically derive the optimal policy update given the solution of the dual problem. This way, we bypass the computation of optimal transport costs and of optimal transport maps, which we implicitly characterize by solving the dual formulation. Finally, we provide an experimental evaluation of our approach across various control tasks. Our results show that optimal transport discrepancies can offer an advantage over state-of-the-art approaches.

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Deep Image Inpainting to Support Endoscopic Procedures

Deep Image Inpainting to Support Endoscopic Procedures

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Mediterranean Conference on control and Automation (MED)

作者： Danilo Menegatti Filippo Betello Francesco Delli Priscoli Alessandro Giuseppi Dept. of Computer Control and Management Engineering (DIAG) University of Rome “La Sapienza” Rome Italy

Deep image inpainting is a computer vision task that uses Deep Neural Networks to generate plausible content to complete an image, for example for the restoration of a damaged image or the removal of unwanted elements captured in the picture. This paper uses deep image inpainting to restore endoscopic images that are affected by various types of artifacts. To this end, we developed a transfer learning-based procedure that uses the CSA inpainting model, which was originally proposed for unrelated tasks including the restoration of images from the Paris StreetView Dataset. The proposed system is trained and validated on the EndoCV2020 dataset, consisting of images from real endoscopies, highlighting how deep image inpainting may be a promising technology for frame restoration during medical procedures.

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Vertically-Advised Federated Learning for Multi-Strategic Stock Predictions through Stochastic Attention-based LSTM

Vertically-Advised Federated Learning for Multi-Strategic St...

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Mediterranean Conference on control and Automation (MED)

作者： Danilo Menegatti Emanuele Ciccarelli Michele Viscione Alessandro Giuseppi Dept. of Computer Control and Management Engineering (DIAG) University of Rome ”La Sapienza” Rome Italy

In recent years, stock price forecasting has become a challenging task commonly used to evaluate the performance of various machine learning solutions. This work explores a Federated Learning (FL) framework within a competitive collaboration scenario with the aim of training a centralised model advised by non-recoverable decentralised strategies so that no exchange of private data is required. The proposed Vertically-Advised Federated Learning (VAFL) framework combines elements from both horizontal and vertical FL, as each client trains two independent models. Furthermore, a novel forecasting architecture, based on a stochastic variant of an Attention-based Long Short Term Memory (LSTM) network, is proposed and validated on a simulated scenario based on real data from the stock market.

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A Feature Fusion based Custom Deep Learning Model for Vehicle Make and Model Recognition

A Feature Fusion based Custom Deep Learning Model for Vehicl...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Triyas Ghosh Soumyajit Gayen Sourajit Maity Daria Valenkova Ram Sarkar Dept. of Computer Sc. and Engineering Jadavpur University Kolkata India Dept. of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (数字)9798350387568

ISBN: (纸本)9798350387575

Vehicle Make and Model Recognition (VMMR) is a pivotal task in various domains including surveillance, traffic management, and the automotive industry. Despite significant progress in deep learning approaches, existing VMMR systems still struggle to attain robust accuracy, particularly across diverse environments and viewing angles. In this paper, we propose a novel approach based on the fusion of two feature maps extracted from DenseNet201 and ResNet50V2 baseline models, respectively. Additionally, we employ a modified Convolutional Block Attention Module (CBAM) to enhance the resilience and precision of VMMR systems. By incorporating attention mechanisms in the feature extraction process, our modified CBAM model effectively captures both spatial and channel-wise dependencies, facilitating more potent discriminative feature representations. We assess our proposed approach through extensive experiments on two benchmark datasets namely Stanford Cars and CompCarsSV. Achieved accuracies are 93.51% and 99.03%, on Stanford Cars and CompCarsSV, respectively that are better than past methods. The code of our proposed model can be found at: https://***/JUVCSE/featurefusion.

关键词： Deep learning Industries Accuracy Surveillance Traffic control Feature extraction Robustness

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Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements 36

Digital Map Generation Workflow Demonstrated on ZalaZONE Aut...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Somogyi, Árpád Tettamanti, Tamás Varga, Pál Szalay, Zsolt Baranyai, Dániel Lovas, Tamás Budapest University of Technology and Economics Fac. of Civil Engineering Dept. of Photogrammetry and Geoinformatics Budapest Hungary Institute for Computer Science and Control The Eötvös Loránd Research Network Systems and Control Laboratory Budapest Hungary Budapest University of Technology and Economics Fac. of Electrical Engineering and Informatics Dept. of Telecommunications and Media Informatics Budapest Hungary Budapest University of Technology and Economics Fac. of Transportation Eng. and Vehicle Eng. Dept. of Automotive Technologies Budapest Hungary

ISBN: (纸本)9781665477161

One major goal of digital twin technology applied in the Architecture, engineering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Mutriku's Offshore Wave Power Plant Capture Chamber Model Validation

Mutriku's Offshore Wave Power Plant Capture Chamber Model Va...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Aitor J. Garrido Amparo Villasante Izaskun Garrido Dept. Aut. Control and Syst. Eng. Faculty of Engineering of Bilbao University of the Basque Country (UPV/EHU) Automatic Control Group - ACG. Inst. of Research and Development of Processes - IIDP. Bilbao Spain Automatic Control Group University of the Basque Country (UPV /EHU) Bilbao Spain

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

The strategic vision outlined in the EU Energy Road Map suggests that, by the year 2050, the use of ocean wave energy could fulfill up to 15% of the European Union's energy demands, while allowing a substantial reduction in carbon dioxide emissions, estimated at 136 million metric tons per megawatt-hour. The Spanish Renewable Energies Plan echoes this vision, placing a particular emphasis on the role of wave energy within Spain's energy portfolio. In this milieu, Oscillating Water Column (OWC) converters are emerging as a leading technology for the conversion of wave energy, able to operate from both terrestrial and marine platforms. This study introduces a detailed analytical model for the wave capture chamber of a static OWC wave energy generator, which has been specifically developed and parameterized for the Mutriku Marine Offshore Wave Power Plant (Mutriku's MOWC), located on the coastline of the Basque Country in Spain. The validation of the proposed model is conducted through the utilization of in-situ measured wave input data, alongside the experimental power output data procured from the operational plant. This dual-faceted validation approach ensures the robustness and applicability of the model in real-world scenarios.

关键词： Renewable energy sources Power measurement Wave power Roads Sea measurements Predictive models Data models

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An Edge-based Architecture for Phasor Measurements in Smart Grids

An Edge-based Architecture for Phasor Measurements in Smart ...

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2022 AEIT International Annual Conference, AEIT 2022

作者： Galantino, Stefano Risso, Fulvio Cazzaniga, Andrea Garrone, Fabrizio Terruggia, Roberta Lazzari, Riccardo Politecnico di Torino Dept. of Computer and Control Engineering Torino Italy Ricerca sul Sistema Energetico Tecnologie di Trasmissione e Distribuzione Milano Italy

ISBN: (数字)9788887237559

ISBN: (纸本)9788887237559

This paper investigates the application of Kubernetes and Edge computing technologies to operate IT services in the context of power systems and smart grids. Traditional services for grid monitoring such as Phasor Measurement Units (PMUs) and Phasor Data Concentrators (PDCs) require a centralized architecture and a rigid networking infrastructure in order to properly function, which today is only achieved at the High Voltage (HV) transmission level. Furthermore, manual intervention is often the only option for PMUs/PDCs maintenance. In this work, the traditional PμPDC services were deployed as docker-containers in a decentralized Kubernetes cluster, which can represent any kind of geographically dispersed TCP/IP network. By leveraging remote orchestration, several key benefits are achieved: (1) no manual reconfiguration of the PMU-PDC communications upon network reconfiguration, (2) automatic PMU traffic redirection in case of PDC service redeployment in a different location, and (3) reduced data-loss upon PDC failure and enhanced overall system resiliency due to minimized ICT services down-time. © 2022 AEIT.

关键词： Phasor measurement units

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Robotic Arm Dataset (RoAD): A Dataset to Support the Design and Test of Machine Learning-Driven Anomaly Detection in a Production Line

Robotic Arm Dataset (RoAD): A Dataset to Support the Design ...

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Annual Conference of Industrial Electronics Society

作者： Alessio Mascolini Sebastiano Gaiardelli Francesco Ponzio Nicola Dall'Ora Enrico Macii Sara Vinco Santa Di Cataldo Franco Fummi Dept. of Control and Computer Engineering Polytechnic of Turin Italy Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Regional and Urban Studies and Planning Polytechnic of Turin Italy

The early detection of anomalous behaviors from a production line is a fundamental aspect of Industry 4.0, facilitated by the collection of massive amounts of data enabled by the Industrial Internet of Things. Nonetheless, the design and validation of anomaly detection algorithms, mostly based on sophisticated Machine Learning models, heavily rely on the availability of annotated datasets of realistic anomalies, which is very difficult to obtain in a real production line. To address this problem, we introduce the Robotic Arm Dataset (RoAD), specifically designed to support the development and validation of Multivariate Time Series Anomaly Detection (MTSAD) algorithms. We collect and annotate a large number of data and metadata to characterize the motion and energy consumption of a collaborative robotic arm in a full-fledged production line and annotate a comprehensive set of healthy as well as realistic anomalies scenarios. To prove the significance of RoAD and encourage future developments, we benchmark several state-of-the-art anomaly detection algorithms on our newly introduced dataset, and we freely release it to the scientific community.

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Action Masked Deep Reinforcement learning for controlling Industrial Assembly Lines

Action Masked Deep Reinforcement learning for Controlling In...

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AI IoT Congress (AIIoT), World

作者： Ali Mohamed Ali Luca Tirel Dept. of Mechanical and Aerospace Engineering Carleton University Ottawa Canada Dept. of Computer Control and Management Engineering University of Rome "La Sapienza" Rome Italy

The scalability of control algorithms controlling industrial assembly lines either model-based or model-free is still a big barrier from the point of view of implementation. In this paper, various Deep Reinforcement learning (DRL) algorithms will be tested in a virtual environment of a generic industrial assembly line in terms of optimally with respect to optimal exact solutions, and computational time. The agents will take bigger rewards when they minimize the finishing time while maintaining constraints, such as time precedence between tasks. The control actions will be modeled as a task assignment matrix that assigns the tasks to be completed to specific workstations. Another control action is the resource allocation. Due to the big number of constraints, many actions will be unfeasible leading to a longer time of training of the agent to learn not to apply unfeasible actions, therefore action masking will be introduced to the DRL algorithms to reduce the action space only to the feasible actions, thereby significantly reducing training time. Proximal Policy Optimization (PPO) agent shows faster and more stable convergence to the optimal solution over the Deep-Q Network (DQN) family, including the double DQN and Dueling one.

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