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Accelerating Virtual Fixture Estimation for Robot Manipulation using Reinforcement Learning and Human Demonstrations

Accelerating Virtual Fixture Estimation for Robot Manipulati...

引用

IEEE International conference on Automation Science and Engineering (CASE)

作者： Diego Fernandez Prado Konstantinos Larintzakis Jan Irsperger Eckehard Steinbach Information and Technology / Chair of Media Technology and Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich / School of Computation Munich Germany

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Teleoperation has the potential to greatly simplify the programming of industrial robots by allowing experts to teach tasks from remote. In order to facilitate the accomplishment of a particular task, Virtual Fixtures (VFs) are often added to the teleoperation system, but designing them is a tedious process that requires time and expertise. In this paper, human teleoperated demonstrations are combined with Proximal Policy optimization (PPO) to decrease the convergence time of a Reinforcement Learning (RL) algorithm that automatically estimates the parameters of a VF for a Pegin-Hole insertion task. the acceleration of the convergence allows us to also estimate parameters of more intricate VFs, where the RL algorithm did not converge otherwise. the experiments show that this technique can successfully infer the expected parameters of predefined VFs in a third of the time compared to the state-of-the-art approach. Additionally, the learned behaviour of the robot manipulator in simulation is executed on a robot arm using an impedance controller to demonstrate that the virtual system closely replicates its real counterpart, and that the learned VFs have higher chances of being useful to a user controlling the real system.

关键词： Fixtures Estimation Reinforcement learning Programming Manipulators Industrial robots Trajectory Time factors optimization Convergence

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Development of a Simulation-Based Solution Concept for AI-Driven Clustering / Combination of Pick and Stow Operations to Improve Logistics Performance in SMEs

Development of a Simulation-Based Solution Concept for AI-Dr...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Timo Schroth Vera Hummel Konrad von Leipzig Jan Schuhmacher ESB Business School Reutlingen University Reutlingen Germany Department of Industrial Engineering Stellenbosch University Stellenbosch South Africa

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

the industrial sector is evolving towards increased customization, diminishing batch sizes, and shorter product lifecycles, affecting intralogistics, which faces challenges in managing an expanding variety of parts and variants. this diversification leads to a decline in efficiency owing to the complexity in pick and stow operations, as traditional systems, digital solutions, and optimization methods mainly rely on historical data without incorporating near-real-time process information. Conventional approaches separate pick and stow operations in both process and workforce, culminating in extended process durations. Instead, data-driven AI-based methods offer a solution by clustering and combining pick and stow operations into optimized bundles, considering travel distance and time. the research employs AI algorithms to streamline picking and stowing, aiming to enhance logistics performance by reducing travel distance and time. Due to the absence of real data, a simulation-based procedure to generate synthetic test and training data is adopted. the real-world logistics system of the learning factory Werk150 is modeled in AnyLogic simulation software to carry out picking and stowing in a 3D warehouse layout. this database is leveraged to train an unsupervised machine learning model using the data analytics software TensorFlow by applying algorithms focused on clustering and combination. A comparative study of these algorithms is conducted to pinpoint optimal strategies for improving logistics performance. Future research will target this methodology, which will be enriched by experimental tests in Werk150 involving near-real-time data, practical investigations, and the use of real data to conclude with an analysis to validate the optimization strategies' effectiveness.

关键词： Solid modeling Machine learning algorithms three-dimensional displays Software algorithms Clustering algorithms Training data optimization methods Software Production facilities Logistics

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Python-based Induction Motors Monitoring and Lifetime Estimation system 20

Python-based Induction Motors Monitoring and Lifetime Estima...

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20th IEEE International conference on Modern Electrical and Energy systems, MEES 2021

作者： Husach, Serhii Kremenchuk Mykhailo Ostrohradskyi National University Department of Automation and Computer-Integrated Technologies Kremenchuk Ukraine

ISBN: (纸本)9781665423663

the aim of this paper is to investigate and use modern technologies for induction motor condition analysis. Development of the modeling and prognosis instruments allow to increase efficiency of induction motor diagnosis quality and provide efficient maintenance. Non-intrusive parameters analysis methods significantly reduce costs and provide enough information for condition evaluation. Induction motor lifetime reduction caused by insulation overheating was taken as a basic parameter. According to Montsinger's rule was created Python-based environment for evaluating additional losses that cause overheating. the user's interface provides processed information, recommended repair schedule, and expected induction motor lifetime. Based on the model of the motor also provided a chart with expected exploitation costs. this prognosis is expanded with calculations and charts for additional spending caused by power supply asymmetry. the presented system supports extensions and will receive parameters from other units during the development process. © 2021 IEEE.

关键词： Induction motors

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Design of an Automated Flotation Cell for Laboratory Operations Using Model-Based system Engineering

Design of an Automated Flotation Cell for Laboratory Operati...

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Eurocon

作者： Amine Ennawaoui Younes Chhiti Mohammed Bensitel Ilyass Mousaid Chouaib Ennawaoui Ahmed Chebak Benguerir Laboratoire des Matériaux Avancés et de Génie des Precédés Green Tech Institute UM6P École Nationale Superieure de Chimie Ibn Tofail University Kenitra Morocco Chemistry Department Laboratory of Catalysis and Corrosion of Materials (LCCM) Chouaib Doukkali UniversÏty El Jadida Morocco Technology Transfer Office (TTO) Mohammed VI Polytechnic University Benguerir Morocco Laboratory of Engineering Sciences for Energy (LabSIPE) National School of Applied Sciences Chouaib Doukkali University El jadida Morocco Green Tech Institute Mohammed VI Polytechnic University Benguerir Morocco

Automation in flotation cells has become an increasingly popular topic in the mineral processing industry due to its ability to increase efficiency and reduce operational costs. this paper discusses the design and implementation of an automated flotation cell system for a mineral processing plant. the system utilizes advanced control algorithms and sensors to optimize the flotation process, resulting in improved product quality and reduced energy consumption. this manuscript outlines the first stage in the creation and development of an automated flotation cell for use in educational and research settings, using model-based systems engineering (MBSE) and systems modeling language (SysML) via the CESAM modeling and architecting framework. Our case study employs this method to construct and demonstrate an operational perspective layout of the flotation cell's initial design phase. Moreover, the potential for the automation of flotation cells is highlighted as a promising avenue for improving sustainability and revolutionizing the mineral processing industry.

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Using Subject-Specific Models to find Differences in Underlying optimization Criteria of Sprinting with and without Prostheses 20

Using Subject-Specific Models to find Differences in Underly...

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20th IEEE-RAS International conference on Humanoid Robots (HUMANOIDS)

作者： Emonds, Anna Lena Mombaur, Katja Heidelberg Univ Inst Comp Engn Optimizat Robot & Biomech Grp Heidelberg Germany Univ Waterloo Canada Excellence Res Chair Human Ctr Robot & Mac Waterloo ON Canada

ISBN: (纸本)9781728193724

A comprehensive knowledge of the underlying criteria of sprint motions, both of non-amputee and below-knee amputee athletes, is helpful for design improvement of prosthetic devices or towards a fair judgment of (dis-)advantage due to the running-specific prosthesis. For the study of sprint motions with and without running-specific prostheses, we created rigid multi-body system models of three non-amputee and one unilateral transtibial amputee athlete. We restricted the motions to the sagittal plane, ending up with 16 degrees of freedom (DOFs). the internal rotational DOFs are controlled by joint torque actuators. As the prosthetic device has to be passive, the joint torque actuator is replaced by a linear spring-damper system in the prosthetic ankle joint. the aim of our study is to identify the optimal weight factors which combine five elementary optimization criteria in such a way that the resulting synthesized motions comes as close as possible to recorded reference motions. To this aim, we formulated an inverse optimal control problem (IOCP) as a bi-level problem: In the outer loop, the weight factors are adapted such that the comparison of the reference motion and the solutions of the optimal control problem (OCP) which computes sprint motions in the inner loop match each other as close as possible. In contrast to previous studies, we investigated more athletes, added subject-specific joint torque limits based on Muscle Torque Generators and left the average velocities of the running motions free. For all four athletes, we identified a set of optimal weights that generates sprint motions which closely match the recorded reference motion. Significant differences in the identified weights between amputee and non-amputee sprinting have been found. Especially angular momentum control plays a decisive role in unilateral transtibial amputee sprinting.

关键词： Actuators Torque Optimal control Humanoid robots Muscles Generators optimization

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Parametric Randomization for Accelerating the Nested Global optimization 20th

Parametric Randomization for Accelerating the Nested Global...

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20th International conference on Mathematical modeling and Supercomputer Technologies, MMST 2020

作者： Grishagin, Vladimir Gergel, Victor Lobachevsky State University Gagarin Avenue 23 Nizhni Novgorod603950 Russia

ISBN: (纸本)9783030787585

In the framework of the nested optimization scheme for reducing a multidimensional global search problem to a family of subproblems of less dimensions an information-statistical method for solving internal subproblems of the nested scheme is considered. the method’s efficiency depends essentially on parameters influencing its convergence speed and reliability. Two techniques of the parameter randomization aimed at the acceleration of the reliable global search are studied. the experimental results of efficiency evaluation for the considered methods on two multidimensional multiextremal classes of benchmarks of different dimensions widely used for testing global optimization methods are presented. the results demonstrate advantages of the randomized techniques compared to prototypical method with invariable values of parameters. © 2021, Springer Nature Switzerland AG.

关键词： Global optimization

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20th International conference on Business Informatics Research, BIR 2021

20th International Conference on Business Informatics Resear...

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20th International conference on Business Informatics Research, BIR 2021

ISBN: (纸本)9783030872045

the proceedings contain 16 papers. the special focus in this conference is on Business Informatics Research. the topics include: Organizations Crisis Framework: Classification Crisis Factors with Using Conceptual Modelling;towards Regulation Change Aware Warning system;comparative Study of Normative Document modeling and Monitoring;the Impact of the ‘Right to Be Forgotten’ on Algorithmic Fairness;how Companies Develop a Culture for Digital Innovation: A Multiple-Case Study;requirements for a Digital Business Ecosystem Modelling Method: An Interview Study with Experts and Practitioners;understanding E-Learning Styles in Distance Learning in Times of the Covid-19 Pandemic – Towards a Taxonomy;Quality of OER from the Perspective of Lecturers – Online Survey of Quality Criteria for Quality Assurance;Using Wearable Fitness Trackers to Detect COVID-19?!;Towards Data Ecosystem Based Winter Road Maintenance ERP system;Trends on the Usage of BPMN 2.0 from Publicly Available Repositories;capabilities in Crisis: A Case Study Using Enterprise modeling for Change Analysis;post-merger Integration Specific Requirements Engineering Model;itsVALUE - A Method Supporting Value-Oriented ITSM.

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A Machine Learning Approach for modeling Safety Stock optimization Equation in the Cosmetics and Beauty Industry 1

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20th Mexican International conference on Artificial Intelligence (MICAI)

作者： Diaz, David Marta, Regina Ortega, German Ponce, Hiram Univ Panamer Fac Ingn Augusto Rodin 498 Ciudad De Mexico 03920 Mexico

ISBN: (数字)9783030898175

ISBN: (纸本)9783030898175;9783030898168

Safety Stock is generally accepted as an appropriate inventory management strategy to deal with the uncertainty of demand and supply, as well as for limiting the risk of service loss and overproduction [6]. In particular, companies from the cosmetics and beauty industry face additional inventory management challenges derived from the strict regulatory standards applicable in different jurisdictions, in addition to the constantly changing trends, which highlight the importance of defining an accurate safety stock. In this paper, on the basis of the Linear Regression, Decision Trees, Support Vector Machine ("SVM") and Neural Network machine learning techniques, we modeled a general Safety Stock equation and one per product category for a multinational enterprise operating in the cosmetics and beauty industry. the results of our analysis indicate that the Linear Regression is the most accurate model to generate a reasonable and effective prediction of the company's Safety Stock.

关键词： Stock optimization Safety stock Consumer industry Inventory management Linear Regression Decision Trees Support Vector Machine Neural Networks

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A Workflow Management system Approach To Federated Learning: Application to Industry 4.0

A Workflow Management System Approach To Federated Learning:...

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International conference on Distributed Computing in Sensor systems (DCOSS)

作者： Hamza Safri George Papadimitriou Frédéric Desprez Ewa Deelman Berger-Levrault Toulouse France Grenoble University Grenoble France University of Southern California Los Angeles USA INRIA Grenoble France

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Federated Learning (FL) combined with the Industrial Internet of things (IIoT) enhances decision-making in industrial settings by leveraging decentralized machine learning (ML) to ensure data privacy, optimize edge computing, and facilitate adaptive model training. However, implementing FL and IIoT presents challenges due to distributed architectures, including communication, data transfer, and file management across wide area networks. this paper addresses these challenges by introducing FL and Clustered FL (CFL) models using Pegasus workflows. Evaluated with real data from airport baggage conveyor systems, it offers practical insights into FL's application in IIoT environments, contributing to advancements in intelligent industrial decision-making.

关键词： Wide area networks Training Adaptation models Federated learning Computational modeling Atmospheric modeling Decision making

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Resilience-Based Workforce Routing and Scheduling Problem in Substation system Emergency Operation*

Resilience-Based Workforce Routing and Scheduling Problem in...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Tianbao Liang Yaping Liu Zhanbo Xu Jiang Wu Haoming Zhao Xiaohong Guan Moe Klinns Lab and Systems Engineering Institute of Xi’an Jiaotong University Xi’an China Department of Automation Center for Intelligent and Networked Systems (CFINS) TNLIST Tsinghua University Beijing China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Global climate changes have increased the frequency and consequences of disasters, which affects the stable operation of the electric power network. thus, substation system as a significant part of electric power network, its optimal emergency operation facing disaster has attracted much attention. this paper focuses on the workforce routing and scheduling problem (WRSP) in the substation system emergency operation, which is formulated as a mixed integer linear programming problem with the objective of maximizing system resilience. Task association constraint model and technicians routing constraint model are established to describe the cooperative relationship between technicians of different crews and the complex relationship between tasks respectively. Task and feeder output correlation constraint model is established to correlate the execution of tasks in continuous time with the discrete time output of the substation feeders. In this way, the technician assignment is independent of time and the problem can be efficiently solved by optimization solver. the performance and effectiveness of the developed method is demonstrated using a practical case study. the results show that the developed model can effectively coordinate technicians and tasks, and improve the resilience of this system to disasters.

关键词： Substations Correlation Disasters Routing Numerical models Mixed integer linear programming Indexes optimization Resilience Meteorology

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