MétaCan
Menu
Retour à la cohorte
Enregistrement W3036214810 · doi:10.1002/aisy.202000072

Opportunities and Challenges in Soft Robotics

2020· article· en· W3036214810 sur OpenAlexaboutno aff
Hamidreza Marvi, Guo Zhan Lum, Ian D. Walker

Notice bibliographique

RevueAdvanced Intelligent Systems · 2020
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueMicro and Nano Robotics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRoboticsArtificial intelligenceSoft roboticsRobotComputer scienceAutomationConformable matrixHuman–computer interactionEngineeringMechanical engineering

Résumé

récupéré en direct d'OpenAlex

Soft Robotics has emerged as a new and rapidly evolving interdisciplinary research area. This technology can provide a wide range of opportunities to create machines with unprecedented mechanical functionalities, as well as robots that are intrinsically safe to interact with human beings. However, the potential of this technology has not been fully realized as it is still a significant challenge to design, model and control such robots. This special issue, building on a workshop co-organized by the guest editors at the 2019 IEEE International Conference on Robotics and Automation in Montreal, Canada, focuses on recent advancements in soft robotics. The set of accepted papers highlights the opportunities and critical challenges of this field. Successfully realized soft robotics technologies could have a major impact on numerous industries and human activities (1900166, 1900171). Indeed, soft robotics offers the potential to be much more conformable and adaptable through novel sensing (1900080, 1900171, 1900178, 2000002; see Figure 1 A,B) and actuation mechanisms (1900177, 1900163; see Figure 1 C,D). As a result, these robots will be able to demonstrate significantly higher dexterity and manipulation capabilities than their traditional rigid counterparts. For example, grippers/gloves with embedded soft sensors that can empower service robots to manipulate a broad range of objects (1900080; see Figure 1 A) or enable computational proprioception and task identification (2000002; see Figure 1 B). Bio-inspired soft robots can also significantly benefit search and rescue and exploratory operations as they can potentially negotiate across much more complicated terrestrial and aquatic terrains with soft bodies (1900183, 1900154, 1900186; see Figure 1 E,F). Furthermore, soft robotic technologies could be used to create highly functional magnetically controlled devices, which can potentially change minimally-invasive surgeries and targeted drug delivery (1900086; see Figure 1 G). This special issue of Advanced Intelligent Systems is aimed at both roboticists and material scientists. Based on a rigorous peer-review process, we have selected a set of papers that illustrate the inherent interdisciplinary nature of, and the diverse approaches being adopted within current soft robotics research. We hope that the Special Issue will stimulate researchers currently working in soft robotics, and also encourage other researchers to engage this emerging and challenging area. Hamid Marvi received his B.Sc. from Iran University of Science and Technology in 2004, M.Sc. degree from Clemson University in 2009 and Ph.D. in mechanical engineering from Georgia Tech in 2013. He was a postdoctoral fellow at Georgia Tech and then at Carnegie Mellon University till August 2015. Since then, he has been an Assistant Professor of Mechanical and Aerospace Engineering at Arizona State University. His research aims to study fundamental physics behind interactions of biological and robotic systems with their surrounding solid, granular, and fluidic environments. Guo Zhan Lum received his B.Eng. from Nanyang Technological University in 2010. He went on to pursue his postgraduate studies under the dual Ph.D. program of Nanyang Technological University and Carnegie Mellon University. He received his M.Sc. degree from Carnegie Mellon University in 2015, and dual Ph.D. degrees in 2016. From 2016 to 2017, he was a post-doctoral researcher at the Max Planck Institute for Intelligent Systems. He is now an Assistant Professor at Nanyang Technological University and his research interests include soft robots, miniature robots and biomedical devices. Ian Walker received the B.Sc. from the University of Hull, England, in 1983 and the M.S. and Ph.D. from the University of Texas at Austin in 1985 and 1989, respectively. He was an Assistant and Associate Professor at Rice University from 1989 to 1997. Since 1997, he has been with the Department of Electrical and Computer Engineering at Clemson University, where he is a full Professor. Professor Walker's research centers on robotics, particularly novel continuous backbone “continuum” and soft robots.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,030

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,005
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0020,008
Communication savante0,0060,010
Science ouverte0,0010,005
Intégrité de la recherche0,0050,007
Charge utile insuffisante (le modèle a refusé de juger)0,0090,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,132
Tête enseignante GPT0,268
Écart entre enseignants0,137 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueAdvanced Intelligent SystemsMême sujetMicro and Nano RoboticsTravaux en français237 207