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Enregistrement W2981503036 · doi:10.1093/biosci/biz117

Nature-Inspired Robots

2019· article· en· W2981503036 sur OpenAlexaff
Niki Wilson

Notice bibliographique

RevueBioScience · 2019
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueSpace Science and Extraterrestrial Life
Établissements canadiensParks Canada
Organismes subventionnairesnon disponible
Mots-clésRobotComputer scienceCognitive scienceArtificial intelligencePsychology

Résumé

récupéré en direct d'OpenAlex

In the lowland scrub and semiopen deciduous woodlands of coastal Ecuador and Northwest Peru, the Pacific parrotlet flies in short, fast bursts from cactus bud to tree branch foraging for fruit. Although this flitting about appears effortless, it requires “incredible feats of coordination and agility,” says William Roderick, a PhD student in mechanical engineering at Stanford University. He and his colleagues published a recent article in eLife on how the parrotlet's feet work to grasp and perch. Roderick is among a growing number of biologists and mechanical engineers working together to unlock the secrets of nature's designs in order to build better robots. Roderick wants to build aerial robots that help study the environment. He grew up accompanying his biologist parents on field expeditions in the rainforests of Hawaii and is well acquainted with the effort required to collect insects and spiders from the forest canopy. “You can only climb so many trees in a day,” he says. Roderick envisions robots that could be deployed in groups to more efficiently collect samples. However, to take off, land, and perch, they need to be able to grasp—a task that requires solving the complex problem of where and how forces should be applied on a variety of landing surfaces. By exploring the foot–surface interactions of parrotlets, he developed a dynamic model that explains how the birds stabilize their grasp. Next, he will work toward gaining a similar understanding of how birds take off, with the hope of applying both of these models to future robotic designs. The process of designing nature-inspired robots is often an iterative one in which robotics engineers also help prompt new questions and a deeper understanding of biology. Malcolm MacIver is a group leader in the Center for Robotics and Biosystems at Northwestern University. Ten years ago, he wanted to better understand the biomechanics of the undulating ribbon fin that propels the black ghost knifefish, but he had a problem. “They are the Cirque du Soleil artists of the underwater domain,” he says. Knifefish are highly maneuverable, using weak electric fields to navigate and hunt in the dark, obstacle-laden environments of Amazon Basin rivers. Their bendable bodies twist and turn in multiple directions, making it difficult to isolate different components of locomotion. MacIver and his colleagues needed a more cooperative fish for their research, so they built the “GhostBot”—a rigid-bodied robot with a knifefish-like ribbon fin they could manipulate and experiment on. With the aid of GhostBot, the team discovered that the collision of “waves” sent down the fin from both ends of the body creates a jet that propels the fish in the opposite direction of the fin, as they described in 2010 in the Journal of the Royal Society Interface. “We couldn’t have understood those fluid dynamics without that device,” says MacIver. GhostBot has since sparked the interest of those in need of more agile underwater vehicles. Remotely operated, propeller-driven machines can be relatively slow, get tangled in weeds, make a lot of noise, and stir up sediment, says MacIver. In contrast, ribbon-finned machines are ideal for tasks such as surveying sensitive coral reefs or work that is dangerous for humans such as inspecting underwater structures in turbulent water. In fact, nature provides all kinds of inspiration for robots that could help in dangerous situations. Harvard's Kaushik Jayaram studied the American cockroach to learn more about its ability to move through confined spaces. He found that cockroaches can squeeze through 3-millimeter openings, speed down tunnels while squashed to half their height, and withstand forces almost 900 times their body weight. These abilities provided valuable design insight for Jayaram to build CRAM, a palm-size “compressible robot with articulated mechanisms.” CRAM may one day be part of search and rescue teams, crawling through the building rubble created by natural disasters to alert first responders to survivors, as Jayaram cowrote in the Proceedings of the National Academy of Sciences in 2016. Jayaram has discovered multiple cockroach adaptations that have informed the design of small robots. Someday, these micromachines might be used to inspect the engines of airplanes or crawl through pipes to find potentially costly or dangerous flaws. The potential applications of ever-smaller robots are far-reaching, he says. Jayaram will soon be heading up the Animal Inspired Movement and Robotics Laboratory at the University of Colorado Boulder, where he has a grant to design microrobots one cubic centimeter in size. “At that scale,” he says “it starts getting close to systems potentially useful for medical applications.” Imagine a robot that could crawl through an artery and remove clots. “That would be my dream scenario,” says Jayaram, “to save human lives.” Niki Wilson is a biologist turned journalist based in the wilds of Jasper National Park, Canada. Read more stories at www.nikiwilson.com, or find her on Twitter @niki_wilson.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0120,003

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 Opus0,007
Tête enseignante GPT0,254
Écart entre enseignants0,247 · 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'étudeSans objet
Domainenon disponible
GenreEmpirique

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

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentnon

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