Collaborative draping of carbon fiber parts

Informazioni relative al progetto

DrapeBot

ID dell’accordo di sovvenzione: 101006732

DOI

10.3030/101006732

Progetto chiuso

Data della firma CE 3 Dicembre 2020

Data di avvio 1 Gennaio 2021

Data di completamento 31 Dicembre 2024

Finanziato da

• INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Costo totale

€ 7 073 880,00

Contributo UE

€ 6 012 142,50

Coordinato da

PROFACTOR GMBH
Austria

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (5)

Report on operational model of trustworthiness

To estimate trust development in human workers that goes beyond task success and takes observable interaction behavior into account an operational model is developed that focuses on constituents of trust in the concrete application context The model will then be used to categorize and classify levels of trust in ongoing human robot collaborations

"Initial report on the integration of the ""DrapeCell"" demonstrator"

This report will describe the robotic set up (incl. the gripper) used in Profactor’s DrapeCell, the integration activities that have taken place and initial test results that have been obtained. It will provide an outlook which development steps or improvements are still needed.

Report on final integration of demonstrator cells

This report will build upon deliverables D6.1 and D6.2 and describe all further integration activities and tests that have been completed on Profactors DrapeCell and DLRs TEZ.

"Initial report on the integration of the ""TEZ"" demonstrator"

This report will describe the robotic set up (incl. the grippers) used in DLR’s TEZ, the integration activities that have taken place and initial test results that have been obtained. It will provide an outlook which development steps or improvements are still needed.

Report on results of user evaluations

This report deliverable will present the results of the user evaluation done in both workcells and in the different use cases. It will also describe the conclusion, their impact on setting up human robot collaborative draping systems and best practice process specifications for collaborative draping.

Demonstrators, pilots, prototypes (3)

M48 Demonstration and evaluation report

The deliverable will consist of a demonstration of the integrated workcells in a laboratory environment (DLR) and at an end user (Baltico) and an evaluation report. The report will compare the results to the KPIs defined for this demonstration.

M36 Demonstration and evaluation report

The deliverable will consist of a demonstration of the integrated workcells in a laboratory environment (DLR) and at an end user (Dallara) and an evaluation report. The report will compare the results to the KPIs defined for this demonstration.

M28 Demonstration and evaluation report

The deliverable will consist of a demonstration of the integrated grippers in laboratory environments and an evaluation report. The report will compare the results to the KPIs defined for this demonstration.

Websites, patent fillings, videos etc. (1)

Project web page

This deliverable is the project web page The report will describe the structure the content and the intended use of the web page

Other (4)

Data set in repository

This deliverable consists of non-confidential data sets that are made available in public repository for use by other researchers in accordance with the data management plan.

Perception model for human analysis in the workcell

This deliverable is a software module that provides a map populated by human detections and actions based both on visual and inertial data. The module will provide input to the behaviour analysis module and to the robotic system, to enhance robot motion planning and enable security features. Results of initial test of the perception module will be reported in the deliverable.

Multi modal analysis tool for non-verbal trust estimation

The operational model will be integrated into a multi-modal analysis tool that will dynamically assess the works trust level towards the robot based on available sensor data and suggests behavior adaptation to control the workers trust level.

Human-robot real-time interaction

The deliverable is a software implementation of a low-level control algorithm that makes local corrections to the robot’s motion in response to the interaction with the human worker in the Profactor workcell. Test results will also be reported in the deliverable.

Open Research Data Pilot (2)

Data Management Plan

The deliverable will include a plan describing the data collected throughout the project their status and how nonconfidential research data will be made available to other researchers in a repository

Updated Data Management Plan

This deliverable will describe the publishable data sets that have been acquired during the project and briefly explain their structure.

Pubblicazioni

Peer reviewed articles (7)

Design of an Assistive Controller for Physical Human–Robot Interaction Based on Cooperative Game Theory and Human Intention Estimation

Autori: Paolo Franceschi, Davide Cassinelli, Nicola Pedrocchi, Manuel Beschi, Paolo Rocco
Pubblicato in: IEEE Transactions on Automation Science and Engineering, 2024, Pagina/e 1-16, ISSN 1545-5955
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tase.2024.3429643

METRIC—Multi-Eye to Robot Indoor Calibration Dataset

Autori: Davide Allegro; Matteo Terreran; Stefano Ghidoni
Pubblicato in: Information, 2023, ISSN 2078-2489
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/info14060314

Human–Robot Role Arbitration via Differential Game Theory

Autori: Franceschi, Paolo; Pedrocchi, Nicola; Beschi, Manuel;
Pubblicato in: IEEE Transactions on Automation Science and Engineering, 2023, ISSN 1545-5955
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tase.2023.3320708

Co-Manipulation of Soft-Materials Estimating Deformation from Depth Images

Autori: Nicola, Giorgio; Villagrossi, Enrico; Pedrocchi, Nicola;
Pubblicato in: Robotics and Computer-Integrated Manufacturing, 2024, ISSN 0736-5845
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.rcim.2023.102630

Identification of human control law during physical Human–Robot Interaction

Autori: Franceschi, Paolo; Pedrocchi, Nicola; Beschi, Manuel;
Pubblicato in: Mechatronics, 2023, ISSN 0957-4158
Editore: Pergamon Press Ltd.
DOI: 10.1016/j.mechatronics.2023.102986

A general skeleton-based action and gesture recognition framework for human–robot collaboration

Autori: Matteo Terreran, Leonardo Barcellona, Stefano Ghidoni
Pubblicato in: Robotics and Autonomous Systems, 2023, ISSN 0921-8890
Editore: Elsevier BV
DOI: 10.1016/j.robot.2023.104523

Multi-Camera Hand-Eye Calibration for Human-Robot Collaboration in Industrial Robotic Workcells

Autori: Davide Allegro, Matteo Terreran, Stefano Ghidoni
Pubblicato in: IEEE Robotics and Automation Letters, Numero 9, 2024, Pagina/e 9852-9859, ISSN 2377-3766
Editore: IEEE
DOI: 10.1109/lra.2024.3468089

Conference proceedings (27)

Clustering-based refinement for 3D human body parts segmentation

Autori: Barcellona, Leonardo; Terreran, Matteo; Evangelista, Daniele; Ghidoni, Stefano
Pubblicato in: Proceedings of the 17th International Conference on Intelligent Autonomous Systems, 2022
Editore: IAS Society
DOI: 10.5281/zenodo.6706376

Multi-view Pose Fusion for Occlusion-Aware 3D Human Pose Estimation

Autori: Bragagnolo, Laura; Terreran, Matteo; Allegro, Davide; Ghidoni, Stefano
Pubblicato in: Proceedings of the 18th European Conference on Computer Vision, 2024
Editore: Springer
DOI: 10.5281/zenodo.13384834

Collision-free Volume Estimation Algorithm for Robot Motion Deformation

Autori: Miotto, Nicola; Gottardi, Alberto; Castaman, Nicola; Menegatti, Emanuele
Pubblicato in: IEEE 21st International Conference on Advanced Robotics (ICAR), 2023
Editore: IEEE
DOI: 10.5281/zenodo.10404965

From Human Perception and Action Recognition to Causal Understandingof Human-Robot Interaction in Industrial Environments

Autori: Stefano Ghidoni, Matteo Terreran, Daniele Evangelista, Emanuele Menegatti, Christian Eitzinger, Enrico Villagrossi, Nicola Pedrocchi, Nicola Castaman, Marcin Malecha, Sariah Mghames, Luca Castri, Marc Hanheide, Nicola Bellotto
Pubblicato in: Proceedings of the Ital-IA workshops, 2021
Editore: CINI national labs

Dynamic Human-Aware Task Planner for Human-Robot Collaboration in Industrial Scenario

Autori: Alberto Gottardi; Matteo Terreran; Christoph Frommel; Manfred Schoenheits; Nicola Castaman; Stefano Ghidoni; Emanuele Menegatti
Pubblicato in: Proceeding of the 11th European Conference on Mobile Robots (ECMR), 2023, Pagina/e 8
Editore: IEEE
DOI: 10.1109/ecmr59166.2023.10256268

Inverse Optimal Control for the identification of human objective: a preparatory study for physical Human-Robot Interaction

Autori: Franceschi, Paolo; Pedrocchi, Nicola; Beschi, Manuel;
Pubblicato in: 27th International Conference on Emerging Technologies and Factory Automation (ETFA), 2022, ISBN 978-1-6654-9996-5
Editore: IEEE
DOI: 10.1109/etfa52439.2022.9921553

Collision-Free Volume Estimation Algorithm for Robot Motion Deformation

Autori: Nicola Miotto, Alberto Gottardi, Nicola Castaman, Emanuele Menegatti
Pubblicato in: 2023 21st International Conference on Advanced Robotics (ICAR), 2024, Pagina/e 348-354
Editore: IEEE
DOI: 10.1109/icar58858.2023.10406816

Human-Robot Task and Motion Planning in an Industrial Application

Autori: Gottardi, Alberto; Pagello, Enrico; Castaman, Nicola; Menegatti, Emanuele
Pubblicato in: IEEE/RSJ IROS Workshop on Task and Motion Planning: from Theory to Practice, 2023
Editore: IEEE/RSJ IROS Workshop
DOI: 10.5281/zenodo.10405045

Adaptive Impedance Controller for Human-Robot Arbitration based onCooperative Differential Game Theory

Autori: Franceschi, Paolo; Pedrocchi, Nicola; Beschi, Manuel;
Pubblicato in: Proceedings of 2022 International Conference on Robotics and Automation (ICRA), 2022, ISBN 978-1-7281-9681-7
Editore: IEEE-RAS
DOI: 10.1109/icra46639.2022.9811853

Human-Aware Motion Planner for Collaborative Transportation of Flexible Materials

Autori: Gottardi Alberto, Pagello Enrico, Menegatti Emanuele, Tonello Stefano
Pubblicato in: European Robotics Forum 2024, 2024
Editore: ERF24
DOI: 10.5281/zenodo.13950896

Integrating Task and Motion Planning for Manufacturing Processes

Autori: Gottardi, Alberto; Castaman, Nicola; Pagello, Enrico; Menegatti, Emanuele
Pubblicato in: Italian Workshop on Artificial Intelligence and Robotics (AIRO) at AIxIA, 2023
Editore: AIxIA
DOI: 10.5281/zenodo.10405873

A data-driven approach to human-robot co-manipulation of soft materials

Autori: Giorgio Nicola; Enrico Villagrossi; Nicola Pedrocchi
Pubblicato in: Proceedings of the I-RIM 3D conference, Numero 4, 2022, ISBN 978-88-945805-3-2
Editore: I-RIM
DOI: 10.5281/zenodo.7531378

Skeleton-based Action and Gesture Recognition for Human-Robot Collaboration

Autori: Terreran, Matteo; Lazzaretto, Margherita; Ghidoni, Stefano
Pubblicato in: Proceedings of the 17th International Conference on Intelligent Autonomous Systems, 2022
Editore: IAS society
DOI: 10.5281/zenodo.6706704

A Multi-view Framework for Human Parsing in Human-Robot Collaboration scenarios

Autori: MATTEO TERRERAN; Barcellona, Leonardo; Daniele Evangelista; Emanuele Menegatti; Stefano Ghidoni
Pubblicato in: Proceedings of the I-RIM 3D conference, 2021
Editore: I-RIM
DOI: 10.5281/zenodo.5900497

A Smart Workcell for Human-Robot CooperativeAssembly of Carbon Fiber Parts

Autori: Stefano Ghidoni, Matteo Terreran, Daniele Evangelista,Christian Eitzinger, Sebastian Zambal,Enrico Villagrossi, Nicola Pedrocchi,Nicola Castaman,Marcin Malecha
Pubblicato in: Proceedings of the I-RIM 3D conference, 2021
Editore: Third Italian Conference on Robotics and Intelligent Machines
DOI: 10.5281/zenodo.6367920

System concept for human-robot collaborative draping

Autori: Christian Eitzinger, Christoph Frommel, Stefano Ghidoni, Enrico Villagrossi
Pubblicato in: SAMPE Europe Conference and Exhibition 2021, 2021, ISBN 978-90-829101-3-1
Editore: SAMPE

Enhancement of an Optical Cut-Piece Detection System for Pick and Place Use Cases

Autori: Christoph Frommel, Alfons Schuster, Marcin Malecha
Pubblicato in: 15th International Conference on Mechanical and Aerospace Engineering (ICMAE), 2024
Editore: IEEE conference proceedings

Depth image-based deformation estimation of deformable objects for collaborative mobile transportation

Autori: Nicola, Giorgio; Mutti, Stefano; Villagrossi, Enrico; Pedrocchi, Nicola;
Pubblicato in: 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2023, ISBN 979-8-3503-3670-2
Editore: IEEE
DOI: 10.1109/ro-man57019.2023.10309337

MEMROC: Multi-Eye to Mobile RObot Calibration

Autori: Davide Allegro, Matteo Terreran, Stefano Ghidoni
Pubblicato in: Proceedings of IROS 2024, 2024
Editore: IEEE
DOI: 10.48550/arxiv.2410.08805

Human-Robot Collaboration for Complex Draping Processes of Carbon-Fibre-Reinforced Polymers for Aerospace Parts

Autori: Marcin Malecha, Alberto Gottardi, Matteo Terreran, Kasper Hald, Enrico Villagrossi
Pubblicato in: 15th International Conference on Mechanical and Aerospace Engineering (ICMAE), 2024
Editore: IEEE conference proceedings

Human-Robot Collaborative Transportation via Distance-based Role Allocation for Precise Positioning of Flexible Materials

Autori: Matteo Terreran, Alberto Gottardi, Emanuele Menegatti, Stefano Ghidoni
Pubblicato in: 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA), 2024, Pagina/e 1-8
Editore: IEEE
DOI: 10.1109/etfa61755.2024.10711079

Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback

Autori: Nicola, Giorgio; Villagrossi, Enrico; Pedrocchi, Nicola;
Pubblicato in: 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2022, ISBN 978-1-7281-8859-1
Editore: IEEE
DOI: 10.1109/ro-man53752.2022.9900710

Multi-view Human Parsing for Human-Robot Collaboration

Autori: Terreran, Matteo; Barcellona, Leonardo; Evangelista, Daniele; Ghidoni, Stefano
Pubblicato in: Proceedings of the 20th International Conference on Advanced Robotics (ICAR), 2021
Editore: IEEE
DOI: 10.5281/zenodo.6706945

Modeling and analysis of pHRI with Differential Game Theory

Autori: Franceschi, Paolo; Beschi, Manuel; Pedrocchi, Nicola; Valente, Anna
Pubblicato in: 21st International Conference on Advanced Robotics (ICAR), 2023, ISBN 978-1-6654-3684-7
Editore: IEEE
DOI: 10.48550/arxiv.2307.10739

SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution Image Classification and Semantic Segmentation

Autori: Alberto Bacchin, Davide Allegro, Stefano Ghidoni, Emanuele Menegatti
Pubblicato in: Proceedings of the 18th European Conference on Computer Vision, 2024
Editore: Springer
DOI: 10.48550/arxiv.2409.01109

Learning human motion intention for pHRI assistive control

Autori: Franceschi, Paolo; Bertini, Fabio; Braghin, Francesco; Roveda, Loris; Pedrocchi, Nicola; Beschi, Manuel;
Pubblicato in: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, ISBN 978-1-6654-9190-7
Editore: IEEE
DOI: 10.1109/iros55552.2023.10342014

A Smart Workcell for Cooperative Assembly of Carbon Fiber Parts Guided by Human Actions

Autori: Terreran, Matteo; Ghidoni, Stefano; Menegatti, Emanuele; Villagrossi, Enrico; Pedrocchi, Nicola; Castaman, Nicola; Gottardi, Alberto; Eitzinger, Christian; Vescovi, Luca; Salemi, Giuseppe; Casubolo, Matteo; Malecha, Marcin
Pubblicato in: Proceedings of the I-RIM 3D conference, 2022
Editore: Fourth Italian Conference on Robotics and Intelligent Machines
DOI: 10.5281/zenodo.7531293