A collaborative robot for the factory of the future: BAZAR

Cherubini, Andrea; Passama, Robin; Navarro, Benjamin; Sorour, Mohamed; Khelloufi, Abdellah; Mazhar, Osama; Tarbouriech, Sonny; Zhu, Jihong; Tempier, Olivier; Crosnier, André; Fraisse, Philippe; Ramdani, Sofiane

doi:10.1007/s00170-019-03806-y

Cited by 39 publications

(20 citation statements)

References 66 publications

(78 reference statements)

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“…These are preliminary experiments that allow the human operator to communicate with the robot through static hand gestures in real-time while dynamic gestures integration is yet to be done. The experiments were performed on BAZAR robot [ 63 ] which has two Kuka LWR 4+ arms with two Shadow Dexterous Hands attached at the end-effectors. We exploited OpenPHRI [ 64 ], which is an open-source library, to control the robot while corroborating safety of the human operator.…”

Section: Resultsmentioning

confidence: 99%

A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures

Mazhar

Ramdani

Cherubini

2021

Sensors

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Intuitive user interfaces are indispensable to interact with the human centric smart environments. In this paper, we propose a unified framework that recognizes both static and dynamic gestures, using simple RGB vision (without depth sensing). This feature makes it suitable for inexpensive human-robot interaction in social or industrial settings. We employ a pose-driven spatial attention strategy, which guides our proposed Static and Dynamic gestures Network—StaDNet. From the image of the human upper body, we estimate his/her depth, along with the region-of-interest around his/her hands. The Convolutional Neural Network (CNN) in StaDNet is fine-tuned on a background-substituted hand gestures dataset. It is utilized to detect 10 static gestures for each hand as well as to obtain the hand image-embeddings. These are subsequently fused with the augmented pose vector and then passed to the stacked Long Short-Term Memory blocks. Thus, human-centred frame-wise information from the augmented pose vector and from the left/right hands image-embeddings are aggregated in time to predict the dynamic gestures of the performing person. In a number of experiments, we show that the proposed approach surpasses the state-of-the-art results on the large-scale Chalearn 2016 dataset. Moreover, we transfer the knowledge learned through the proposed methodology to the Praxis gestures dataset, and the obtained results also outscore the state-of-the-art on this dataset.

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Section: Resultsmentioning

confidence: 99%

A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures

Mazhar

Ramdani

Cherubini

2021

Sensors

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“…We use our BAZAR robot [22] which is equipped with two lightweight KUKA LWR IV. Planning is carried out in the task space, then projected in the joint space via inverse kinematics.…”

Section: A Hardware Setupmentioning

confidence: 99%

Robotic Manipulation Planning for Shaping Deformable Linear Objects WithEnvironmental Contacts

Zhu

Navarro

Passama

et al. 2020

IEEE Robot. Autom. Lett.

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Humans use contacts in the environment to modify the shape of deformable objects. Yet, few papers have studied the use of contacts in robotic manipulation. In this paper, we investigate the problem of robotic manipulation of cables with environmental contacts. Instead of avoiding contacts, we propose a framework that allows the robot to use them for shaping the cable. We introduce an index to quantify the contact mobility of a cable with a circular contact. Based on this index, we present a planner to plan robot motions. The planner is aided by a visionbased contact detector. The framework is validated with robot experiments on different desired cable configurations.

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“…Cherubini et al [56] introduces BAZAR, a collaborative robot that integrates the most advanced sensing and actuating devices in a unique system designed for the Industry 4.0. The authors present BAZAR's three main features, which are all paramount in the factory of the future.…”

Section: Editorial Overview Of This Special Issue Papers and Their Comentioning

confidence: 99%

Design and management of digital manufacturing and assembly systems in the Industry 4.0 era

Cohen

Faccio

Pilati

et al. 2019

Int J Adv Manuf Technol

127

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The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. This paper first addresses the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published. Then it assesses the challenges faced nowadays by assembly and manufacturing systems. Third, it focuses on the most promising future developments and evolution of such production systems as well as their digitalisation. Finally, this manuscript illustrates the content of the papers selected for this special issue. Through the study presented in this special issue, valuable contributions to both theory and application in this area have been achieved, and a useful reference for future research is given.

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A collaborative robot for the factory of the future: BAZAR

Cited by 39 publications

References 66 publications

A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures

A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures

Robotic Manipulation Planning for Shaping Deformable Linear Objects WithEnvironmental Contacts

Design and management of digital manufacturing and assembly systems in the Industry 4.0 era

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