A robot made of robots: Emergent transport and control of a smarticle ensemble.


Journal

Science robotics
ISSN: 2470-9476
Titre abrégé: Sci Robot
Pays: United States
ID NLM: 101733136

Informations de publication

Date de publication:
18 Sep 2019
Historique:
received: 22 03 2019
accepted: 31 07 2019
entrez: 2 11 2020
pubmed: 18 9 2019
medline: 18 9 2019
Statut: ppublish

Résumé

Robot locomotion is typically generated by coordinated integration of single-purpose components, like actuators, sensors, body segments, and limbs. We posit that certain future robots could self-propel using systems in which a delineation of components and their interactions is not so clear, becoming robust and flexible entities composed of functional components that are redundant and generic and can interact stochastically. Control of such a collective becomes a challenge because synthesis techniques typically assume known input-output relationships. To discover principles by which such future robots can be built and controlled, we study a model robophysical system: planar ensembles of periodically deforming smart, active particles-smarticles. When enclosed, these individually immotile robots could collectively diffuse via stochastic mechanical interactions. We show experimentally and theoretically that directed drift of such a supersmarticle could be achieved via inactivation of individual smarticles and used this phenomenon to generate endogenous phototaxis. By numerically modeling the relationship between smarticle activity and transport, we elucidated the role of smarticle deactivation on supersmarticle dynamics from little data-a single experimental trial. From this mapping, we demonstrate that the supersmarticle could be exogenously steered anywhere in the plane, expanding supersmarticle capabilities while simultaneously enabling decentralized closed-loop control. We suggest that the smarticle model system may aid discovery of principles by which a class of future "stochastic" robots can rely on collective internal mechanical interactions to perform tasks.

Identifiants

pubmed: 33137776
pii: 4/34/eaax4316
doi: 10.1126/scirobotics.aax4316
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Auteurs

William Savoie (W)

School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Thomas A Berrueta (TA)

Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.

Zachary Jackson (Z)

School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Ana Pervan (A)

Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.

Ross Warkentin (R)

School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Shengkai Li (S)

School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Todd D Murphey (TD)

Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.

Kurt Wiesenfeld (K)

School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Daniel I Goldman (DI)

School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA. daniel.goldman@physics.gatech.edu.

Classifications MeSH