On modelling and handling of flexible materials: A review on Digital Twins and planning systems

Andronas, Dionisis; Kokotinis, George; Makris, Sotiris

doi:10.1016/j.procir.2020.08.005

Cited by 18 publications

(7 citation statements)

References 29 publications

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“…11, the application-dependent adaptation occurs at almost all levels. Regarding the tasks orchestration, Behaviour Trees are used to address complex assembly tasks with our Robot Companion platform while simple lists of actions are sufficient to cope with the Robothon® disassembly logic, and the Workcell Controller developed by one of the project's partners is used to address the manipulation of plastic pouches within the context of the Merging Project [26]. The software components and skills also can be adapted.…”

Section: Discussionmentioning

confidence: 99%

An Intelligent Robotics Modular Architecture for Easy Adaptation to Novel Tasks and Applications

Gosselin,

Acher,

Gradoussoff

et al. 2023

2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)

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Section: Discussionmentioning

confidence: 99%

An Intelligent Robotics Modular Architecture for Easy Adaptation to Novel Tasks and Applications

Gosselin,

Acher,

Gradoussoff

et al. 2023

2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)

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“…The DrapeBot project focuses on the robotic manipulation of carbon fiber and fiberglass plies during the draping process; in particular, Eitzinger, Frommel, Ghidoni, and Villagrossi (2021) highlights the importance of the human-robot co-manipulation when the dimension of the ply is such as to require more than one robot. The project Merging looks at the manipulation of flexible and fragile objects exploiting multiple industrial robots, designing new Electro-Adhesive (EA) grasping devices, using information from perception systems fused with the data from a digital twin to estimate the deformations of flexible elements Andronas, Kokotinis, and Makris (2021); Makris, Kampourakis, and Andronas (2022).…”

Section: Related Workmentioning

confidence: 99%

Co-manipulation of soft-materials estimating deformation from depth images

Nicola¹,

Villagrossi²,

Pedrocchi³

2023

Preprint

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Human-robot co-manipulation of soft materials, such as fabrics, composites, and sheets of paper/cardboard, is a challenging operation that presents several relevant industrial applications. Estimating the deformation state of the co-manipulated material is one of the main challenges. Viable methods provide the indirect measure by calculating the human-robot relative distance. In this paper, we develop a datadriven model to estimate the deformation state of the material from a depth image through a Convolutional Neural Network (CNN). First, we define the deformation state of the material as the relative rototranslation from the current robot pose and a human grasping position. The model estimates the current deformation state through a Convolutional Neural Network, specifically a DenseNet-121 pretrained on ImageNet. The delta between the current and the desired deformation state is fed to the robot controller that outputs twist commands. The paper describes the developed approach to acquire, preprocess the dataset and train the model. The model is compared with the current state-of-the-art method based on a skeletal tracker from cameras. Results show that our approach achieves better performances and avoids the various drawbacks caused by using a skeletal tracker. Finally, we also studied the model performance according to different architectures and dataset dimensions to minimize the time required for dataset acquisition.

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“…If recombination is also used as in other evolutionary algorithms, ρ = 2 holds. A special case of (µ/ρ, λ) algorithms is the (µ/µ, λ) evolution strategy [1] (µ/ρ + λ)-analogously to (µ/ρ, λ)-evolution strategies, the (µ/ρ + λ)-evolution strategies are (µ, λ) approaches where ρ denotes the number of parents of an offspring individual; (µ ,λ (µ,λ)γ)-Geyer et al [2][3][4] have developed nested evolution strategies [62], where λ offspring are created and isolated for γ generations from a population of the size µ . In each of the γ generations, λ children are created from which the fittest µ are passed on to the next generation.…”

Section: Evolution Strategymentioning

confidence: 99%

“…The increasing use of digital twins (DTs) is one of the most important trends in the Industry 4.0 concept and industrial engineering [1], and some authors directly refer to the Industry 4.0 era as the era of DT [2]. The concept of Industry 4.0 extends the possibilities and use of DTs for, e.g., decision support and production planning [3], solving unexpected situations/problems or predicting such situations [4], as well as training and knowledge transfer of leadership, management, and executives [5,6].…”

Section: Introductionmentioning

confidence: 99%

Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods

2021

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The paper presents possible approaches for reducing the volume of data generated by simulation optimisation performed with a digital twin created in accordance with the Industry 4.0 concept. The methodology is validated using an application developed for controlling the execution of parallel simulation experiments (using client–server architecture) with the digital twin. The paper describes various pseudo-gradient, stochastic, and metaheuristic methods used for finding the global optimum without performing a complete pruning of the search space. The remote simulation optimisers reduce the volume of generated data by hashing the data. The data are sent to a remote database of simulation experiments for the digital twin for use by other simulation optimisers.

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On modelling and handling of flexible materials: A review on Digital Twins and planning systems

Cited by 18 publications

References 29 publications

An Intelligent Robotics Modular Architecture for Easy Adaptation to Novel Tasks and Applications

An Intelligent Robotics Modular Architecture for Easy Adaptation to Novel Tasks and Applications

Co-manipulation of soft-materials estimating deformation from depth images

Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods

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