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Enregistrement W4366721636 · doi:10.1002/aisy.202200284

Energy in Robotics: An Interdisciplinary Challenge

2023· article· en· W4366721636 sur OpenAlex

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Notice bibliographique

RevueAdvanced Intelligent Systems · 2023
Typearticle
Langueen
DomaineEngineering
ThématiqueModular Robots and Swarm Intelligence
Établissements canadiensCanada Research ChairsUniversity of TorontoUniversity of New Brunswick
Organismes subventionnairesnon disponible
Mots-clésRobotRoboticsArtificial intelligenceSoftware deploymentComputer scienceMetric (unit)Energy (signal processing)Matching (statistics)Human–computer interactionEngineeringOperations managementMathematicsSoftware engineering

Résumé

récupéré en direct d'OpenAlex

Advances in Robotics are challenging our definitions of what robots are and our perspective on how we interact with them. The conventional image of a robot as a large structure made of stiff, rigid links, is slowly being replaced by machines that are more lifelike. Now that we know robots are capable of outperforming humans in terms of strength or precision their role in manufacturing has expanded. As researchers in Robotics push the limits of what robots can be made of, at which scales, and what capabilities they offer, other segments of the economy are considering robots as technological solutions. Healthcare, telecommunications, even agriculture or shipping may become the next arenas for widespread deployment of robots. As robots are developed and deployed, based on different technologies and across a wide range of sizes, there is a growing need for a unified framework of discussion. In particular in the rapidly growing fields of soft and small robots, the importance of energy as a performance metric is paramount. How the robot stores energy, or harvests it from the environment sets the baseline for how much valuable work it can perform in the target environment. How the energy is converted from storage to mechanical work used in manipulation and locomotion, at which scales and with what efficiency are the key metrics for matching a robotic technology to a specific application. As robots are miniaturized, the macro scale distinctions between soft and rigid components become blurred, and energy metrics can be used to describe both technology types and allow for direct comparisons. In this new framework, multiple technologies can be discussed and evaluated across different robotic functionalities. For energy storage, the conventional electrical energy storage in a battery or supercapacitor, can be replaced with chemical fuels, storage in mechanical mechanisms, or even direct harvesting from the environment, for example through turboelectric nano generators. For actuators the broad range of technical solutions (e.g. fluidic soft actuators, chemo-mechanical transducers, dielectric elastomer actuators, piezoelectric actuators, etc.) can be compared across standard metrics, including specific energy and energy density, energy conversion efficiency, and bandwidth. Lastly, new components can provide multi functionality, for example actuators that offer opportunities for embedded sensing and unique control strategies. To showcase the recent advances in these rapidly growing fields, we have organized this special section of Advanced Intelligent Systems focusing on “Energy Storage and Delivery in Robotics”. The special section offers both original research work, as well as reviews and perspective articles from different disciplines, including haptics, mechanical, electrical, and chemical engineering, as well as materials science. The general themes and contributions are summarized below: At small scales and with powerful fabrication capabilities, new materials can be developed to push robots towards the performance limit of natural systems. Gracias et al. (article number 2000195) demonstrated solvent responsive self-folding of graphene architectures, an example of chemo-mechanical energy transduction. The researchers were able to show complex structures, including helices and pyramids deforming in response to chemical fuels, with no electronic components. In a similar venue, Ng et al. (2100085) built and operated self-sustained robots based on functionally graded elastomeric actuators carrying up to 22× their body weight. Borrowing lessons from biology, the researchers use soft actuators in combination with a more rigid exoskeleton to boost its performance. Aiming to provide a framework for discussions of energy in soft robotics, Stokes et al. (2000264) analyzed energy-based abstraction for soft robotic system development. In a more conventional robotic framework, Jusufi et al. (2000244) demonstrated modeling and control of a soft robotic fish with integrated soft sensing. The work is a valuable foray into understanding of the swimming mechanics of fish, including the role of stiffness control in achieving adaptable but efficient locomotion underwater. From swimming to flying robots, Floreano et al. (2100150) studied passive perching with energy storage for winged aerial robots. Their experimental work focused on a proof-of-concept claw shows 5% of the kinetic energy can be recaptured during perching, a valuable recovery for an energy intensive locomotion mode, such as flying. Fan et al. (2200045) reviewed next-generation energy harvesting and storage technologies for robots across all scales. Their extensive summary broadly covers energy storage, harvesting and conversion mechanisms, highlighting unique opportunities where significant gains can be made to improve robotic performance. Blurring the lines between material and component, Pikul et al. (2000255) showed computer-free autonomous navigation and power generation using electro-chemotaxis. Their combined power approach can benefit millimeter- to centimeter-scale robots deployed in unstructured environments, alternating between onboard power and the ECVS to extract energy from surrounding metal infrastructure and extend their search time and depth. With a haptics focused application in mind, Wu et al. (2100120) showed self-powered and interface-independent tactile sensors based on bilayer single-electrode triboelectric nanogenerators for robotic electronic skin. For an even broader view of the challenge of energy management at the human/machine interface, Dahiya et al. (2100036) reviewed bioinspired distributed energy in robotics and enabling technologies. Their review highlights opportunities where a robotic platform can benefit from increased energy density, lesser design complexities, improved body dynamics, and operational reliability with distributed energy. Lastly, Duduta (2200093) provided a perspective on robots as energy systems: advances in Robotics across scales and technologies. We hope that this special section focusing on “Energy Storage and Delivery in Robotics” will bring knowledge, insight, and inspiration to readers. We look forward to the rapid growth of the massive developmental efforts in this field which will lead us right into the envisioned smart society. Finally, I want to thank all the authors for their valuable contributions, as well as the editorial team of Advanced Intelligent Systems for their support and the opportunity to organize this special section.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,919
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,028
Tête enseignante GPT0,289
Écart entre enseignants0,261 · 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