Intelligent Motion Control under Industry 4.E

Livrables

The deliverable is dedicated to the detailed description and presentation of the IMOCO4.E training activities.

Requirements for advanced motion control (final iteration)

The deliverable is dedicated to description of the final requirements for the control layer based on inputs from D24 D41 D61 and D71 Based on the initial test results the final report will present only the updates if any of the initial requirements defined in D41

The IMOCO4.E eBook

This deliverable will aggregate all related publications and articles that will take place within the project,in a useful collection of knowledge that will support the sustainability of IMOCO4.E solution.

Perception and instrumentation Layer requirements and specifications (first iteration)

The deliverable is dedicated to description of the initial requirements for the perception andinstrumentation layer which are partially also based on inputs from D21 and D22 while working inparallel and considering D71 and D72 This report will summarize the perception and instrumentationlayer requirements specific for the relevant BBs pilots demonstrators and usecases

Guideline of IMOCO4.E methodology and toolchains

The deliverable will describe the first iteration of methodology supporting the integration and utilization of the IMOCO4.E components from the perspectives of the different industrial stakeholders. It will focus on tentative employment of model-based techniques in mechatronics system design.

Integral (system level) requirements for valuable twinning methods (first iteration)

The deliverable D51 will provide requirements and specifications on digital twins and their supporting technologies It will rely on inputs from D21 and D22 The requirements from pilots demonstrators and use cases will be gathered and analysed there from the perspective of BB6 BB8 and BB9 which will be prepared in WP5

Final design report on Perception & Instrumentation Layer

The final deliverable D3.7 of WP3 will provide a report on all BBs developed in WP3, namely BB1, BB2, BB3, BB7, BB8. The final report will provide descriptions of all technologies (with pictures), their main characteristics, functionalities, overview of test results and how they address requirements defined in D3.2.

Perception and instrumentation Layer requirements and specifications (final iteration)

The deliverable is dedicated to description of the final requirements for the perception and instrumentation layer Based on the initial test results the final report will present only the updates if any of the initial requirements defined in D31

General specification and design of IMOCO4.E reference framework

Based on D23 the final requirements deliverables D32 D42 D52 and the IMOCO4E methodology outlined in D61 this deliverable presents a reference architecture of the IMOCO4E framework at an abstract level This will be the basic but extensible open and modular framework to be realised demonstrated and validated in the IMOCO4E project

State-of-the-art methods in Digital Twinning for motion-driven high-tech applications

This deliverable presents a market scan also among consortium partners and literature survey on the state of the art in digital twinning solutions and the application of AI Digital twinning and AI will be considered at several levels ranging from modulelevel to productionlinelevel The report will also describe emerging technologies in this field and identify development directions for IMOCO4E

Requirements for advanced motion control (first iteration)

The deliverable is dedicated to description of the initial requirements for the control layer Layer 2 which are partially also based on inputs from D21 and D22 while working in parallel and considering D71 and D72 This report will summarize the control layer requirements specific for the relevant BBs pilots demonstrators and usecases

Final design report on advance control layer development final report

A summary report will provide a comprehensive description of Control Layer building blocks including implementation aspects on relevant pilot applications, use cases and demonstrators. The final deliverable D4.8 of WP4 will provide a report on all BBs developed in WP3, namely BB4, BB5 and BB10. The final report will provide descriptions of all technologies (with pictures), their main characteristics, functionalities, overview of test results and how they address requirements defined in D4.2.

Integral (system level) requirements for valuable twinning methods (second iteration)

This deliverable will contain an extension of requirements and specifications that were prepared in D 51 Only needed revisions of the existing ones and potentially additional ones will be placed there as they can be revealed in the early stage of work in Tasks 52 to 57

Best practices learned from Pilots and Demos

This deliverable will describe the best practices addressing the implementation and setup and later use of the IMOCO4.E suite. As for the technical reports about the pilots and demonstrators the guideline will be enhanced along the evolutionary approach which is followed for the implementation of the pilots and demonstrators.

Overall requirements on IMOCO4.E reference framework

This deliverable describes the requirements on the IMOCO4E reference framework based on inputs from D21 D22 D32 D42 and D51 The deliverable provides requirements on hardware and software building blocks and the instrumentation layer but also on digital twinning the application of AI and ultimately the interfacing between all the IMOCO4E framework components

Report on digital twins, corresponding supporting technologies and their interaction with the cloud

This deliverable is the concluding report of WP5. It describes BB6, BB8 and BB9 components, description of developed interfacing (also with open EU datasets), datasets management on different layers, modelling of complex mechatronics systems and the description of digital twinning technology developed in this WP. The results will address the fulfilment of requirements and specifications defined in D5.1, D5.2 and also in D3.2.

Needs for future smart production in Europe from the mechatronics and robotic point of view

Based on inputs from D21 knowledge among consortium partners and market knowledge this deliverable identifies the solutions required to expedite the introduction of smart production in Europe eg by addressing existing bottlenecks The report will provide the key technologies that IMOCO4E should focus on to benefit a competitive European manufacturing ecosystem

Overview of connections with other Industry4.E initiatives

This deliverable will report an overview of the connections with other Industry 4.E initiatives.

Project Website Functional

A website of IMOCO4E project will be established to inform about IMOCO4E interests progress technicalresults It will include all public reports information about IMOCO4E consortium It will also serve as anexternal communication platform for the consortium partners

Publications

Automated Model-Free Commutation for Coarse Pointing Actuators in Free-Space Optical Communication

Auteurs: Max van Meer, Kjell van Schie, Gert Witvoet, Tom Oomen
Publié dans: 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2024, Page(s) 592-597
Éditeur: IEEE
DOI: 10.1109/aim55361.2024.10637150

Maintenance Reduction of Medical Robotic Manipulators through Automatic Data-Driven Updates of Feedforward Control

Auteurs: Václav Helma, Martin Goubej, Pavel Březina, Henry Stoutjesdijk, Marco Alonso
Publié dans: 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), 2024, Page(s) 1-8
Éditeur: IEEE
DOI: 10.1109/etfa54631.2023.10275591

Vector Reconstruction Error for Anomaly Detection: Preliminary Results in the IMOCO4.E Project

Auteurs: Dario Guidotti, Riccardo Masiero, Laura Pandolfo, Luca Pulina
Publié dans: 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), Numéro 27, 2023, Page(s) 1-4
Éditeur: IEEE
DOI: 10.1109/etfa54631.2023.10275396

Simulation Model and Validation Method Analysis of an Electric Passenger Elevator

Auteurs: Mihail Grovu, Calin Husar, Maria Raluca Raia, Davide Colombo, Alberto Speroni, Daniela Sasu
Publié dans: 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), Numéro 3, 2023, Page(s) 1-5
Éditeur: IEEE
DOI: 10.1109/etfa54631.2023.10275378

Visual Servoing Based on 3D Features: Design and Implementation for Robotic Insertion Tasks

Auteurs: Antonio Rosales, Tapio Heikkilä, Markku Suomalainen
Publié dans: 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA), Numéro 5, 2024, Page(s) 1-6
Éditeur: IEEE
DOI: 10.1109/iciea61579.2024.10665161

Nonlinear Bayesian Identification for Motor Commutation: Applied to Switched Reluctance Motors

Auteurs: van Meer, Max, González, Rodrigo A., Witvoet, Gert, Oomen, Tom
Publié dans: 2023
Éditeur: IEEE

Frequency Domain Identification of Multirate Systems: A Lifted Local Polynomial Modeling Approach

Auteurs: van Haren, Max; Blanken, Lennart; Oomen, Tom
Publié dans: 2022
Éditeur: IEEE 61st Conference on Decision and Control 2022 (CDC)
DOI: 10.5281/zenodo.6982575

Gaussian Process based Feedforward Control for Nonlinear Systems with Flexible Tasks: With Application to a Printer with Friction

Auteurs: Max van Meer; Maurice Poot; Jim Portegies; Tom Oomen
Publié dans: 2022
Éditeur: IFAC Modeling, Estimation, and Control Conference (MECC 2022)
DOI: 10.5281/zenodo.7291337

Dynamic Accuracy Optimization for NC controlled Industrial Robots

Auteurs: Asier Mandiola; Mikel Armendia; Diego Gonzalez; Jon Eguskiza
Publié dans: 2023
Éditeur: IEEE
DOI: 10.5281/zenodo.10191824

Brief Announcement: Optimized GPU-accelerated Feature Extraction for ORB-SLAM Systems

Auteurs: Filippo Muzzini, Nicola Capodieci , Roberto Cavicchioli, Benjamin Rouxel
Publié dans: 2023
Éditeur: Association for Computing Machinery
DOI: 10.1145/3558481.3591310

Machine Learning Techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems

Auteurs: Alessio Masola, Nicola Capodieci, Benjamin Rouxel, Giorgia Franchini, Roberto Cavicchioli
Publié dans: 2023
Éditeur: IEEE
DOI: 10.1109/rtcsa58653.2023.00026

Digital twins benefits and challenges from intelligent motion control point of view

Auteurs: Matias Vierimaa; Mikko Heiskanen; Sajid Muhamed; Hans Kuppens
Publié dans: 2024
Éditeur: IEEE

Verification of NNs in the IMOCO4.E Project: Preliminary Results

Auteurs: Dario Guidotti, Laura Pandolfo, Luca Pulina
Publié dans: 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), Numéro 325, 2024, Page(s) 1-4
Éditeur: IEEE
DOI: 10.1109/etfa54631.2023.10275345

Memory-Aware Latency Prediction Model for Concurrent Kernels in Partitionable GPUs: Simulations and Experiments

Auteurs: Alessio Masola, Nicola Capodieci, Roberto Cavicchioli, Ignacio Sanudo Olmedo, Benjamin Rouxel
Publié dans: 2023
Éditeur: Springer Cham
DOI: 10.1007/978-3-031-43943-8

Robust and optimal design of fixed structure controllers in collocated motion systems

Auteurs: Martin Goubej, Jakub Tvrz, Břetislav Kubeš
Publié dans: 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), Numéro 14, 2023, Page(s) 1-8
Éditeur: IEEE
DOI: 10.1109/etfa54631.2023.10275382

Ablation Study of a Person Re-Identification on a Mobile Robot Using a Depth Camera

Auteurs: Sebastian Flores; Jana Jost
Publié dans: 2022
Éditeur: IEEE
DOI: 10.1109/isie51582.2022.9831503

Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

Auteurs: Max van Haren; Maurice Poot; Dragan Kostić; Robin van Es; Jim Portegies; Tom Oomen
Publié dans: 2022
Éditeur: IEEE
DOI: 10.1109/amc51637.2022.9729327

Vision-Based Multi-Size Object Positioning

Auteurs: Vibhor Jain, Sajid Mohamed, Dip Goswami, Sander Stuijk
Publié dans: 2023 26th Euromicro Conference on Digital System Design (DSD), Numéro abs/2103.13339, 2024, Page(s) 742-747
Éditeur: IEEE
DOI: 10.1109/dsd60849.2023.00106

Time-Sensitive Networking to meet Hard-real Time Boundaries on Edge Intelligence Applications

Auteurs: Armando Astarloa, Pedro Fernández, Jesús Lázaro, Mikel Idirin, Sergio Salas
Publié dans: 2023 38th Conference on Design of Circuits and Integrated Systems (DCIS), 2023, Page(s) 1-6
Éditeur: IEEE
DOI: 10.1109/dcis58620.2023.10336008

Cascaded Calibration of Mechatronic Systems via Bayesian Inference

Auteurs: Van Meer, Max; Deniz, Emre; Witvoet, Gert; Oomen, Tom
Publié dans: Numéro 1, 2023
Éditeur: IFAC
DOI: 10.48550/arxiv.2304.03136

NC controlled robot for adaptive and constant force 3D polishing

Auteurs: Diego Gonzalez; Mikel Armendia
Publié dans: 2022
Éditeur: IEEE
DOI: 10.1109/ETFA52439.2022.9921666

Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression

Auteurs: Max van Meer; Gert Witvoet; Tom Oomen
Publié dans: 2022
Éditeur: IFAC Modeling, Estimation and Control Conference
DOI: 10.5281/zenodo.7291453

A Kernel-Based Identification Approach to LPV Feedforward: With Application to Motion Systems

Auteurs: Max van Haren, Lennart Blanken and Tom Oomen
Publié dans: Max van Haren, Lennart Blanken and Tom Oomen, 2023
Éditeur: IFAC

Predictable Multi-Core Implementation of Multi-Rate Sensor Fusion for High-Precision Positioning Systems

Auteurs: Chaitanya Jugade; Sajid Mohamed; Dip Goswami; Andrew Nelson; Gijs Van Der Veen; Kees Goossens
Publié dans: 2024
Éditeur: IEEE

Condition Monitoring of Industrial Elevators Based on Machine Learning Models

Auteurs: Maria Raluca Raia, Andrei Ailincai, Alexandra Baicoianu, Calin Husar, Cristi Irimia
Publié dans: 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), 2023, Page(s) 1-5
Éditeur: IEEE
DOI: 10.1109/etfa54631.2023.10275563

DNN-Based Visual Perception for High-Precision Motion Control

Auteurs: Vibhor Jain, Sajid Mohamed, Dip Goswami, Sander Stuijk
Publié dans: 2024 European Control Conference (ECC), 2024, Page(s) 2010-2016
Éditeur: IEEE
DOI: 10.23919/ecc64448.2024.10590800

Person Re-Identification on a Mobile Robot Using a Depth Camera

Auteurs: Sebastian Flores; Jana Jost
Publié dans: 2022
Éditeur: IEEE
DOI: 10.1109/isie51582.2022.9831515

Detection of Component Degradation: A Study on Autoencoder-Based Approaches

Auteurs: Dario Guidotti, Laura Pandolfo, Luca Pulina
Publié dans: 2023 IEEE 19th International Conference on e-Science (e-Science), Numéro 27, 2023, Page(s) 1-2
Éditeur: IEEE
DOI: 10.1109/e-science58273.2023.10254890

Robust Commutation Design: Applied to Switched Reluctance Motors

Auteurs: Max Van Meer, Gert Witvoet, Tom Oomen
Publié dans: 2024 European Control Conference (ECC), Numéro 31, 2024, Page(s) 2448-2453
Éditeur: IEEE
DOI: 10.23919/ecc64448.2024.10590899

Position-Dependent Snap Feedforward: A Gaussian Process Framework

Auteurs: van Haren, Max; Poot, Maurice; Portegies, Jim; Oomen, Tom
Publié dans: 2022
Éditeur: 2022 IEEE American Control Conference (ACC)
DOI: 10.5281/zenodo.6351295

Time-sensitive autonomous architectures

Auteurs: Donato Ferraro, Luca Palazzi, Federico Gavioli, Michele Guzzinati, Andrea Bernardi, Benjamin Rouxel, Paolo Burgio, Marco Solieri
Publié dans: Real-Time Systems, 2023, ISSN 0922-6443
Éditeur: Kluwer Academic Publishers
DOI: 10.1007/s11241-023-09404-2

Beyond Nyquist in Frequency Response Function Identification: Applied to Slow-Sampled Systems

Auteurs: Max van Haren, Leonid Mirkin, Lennart Blanken and Tom Oomen
Publié dans: IEEE Control Systems Letters, 2023, ISSN 2475-1456
Éditeur: IEEE

Leveraging Satisfiability Modulo Theory Solvers for Verification of Neural Networks in Predictive Maintenance Applications

Auteurs: Dario Guidotti, Laura Pandolfo and Luca Pulina
Publié dans: Information, 2023, 2023, ISSN 2078-2489
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/info14070397

Verifying Autoencoders for Anomaly Detection in Predictive Maintenance

Auteurs: Dario Guidotti, Laura Pandolfo, Luca Pulina
Publié dans: Lecture Notes in Computer Science, Advances and Trends in Artificial Intelligence. Theory and Applications, 2024, Page(s) 188-199
Éditeur: Springer Nature Singapore
DOI: 10.1007/978-981-97-4677-4_16

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