Modular robots are promising for their versatility and large design freedom. Modularity can also enable automatic assembly and reconfiguration, be it autonomous or via external machinery. However, these procedures are error-prone and often result in misassemblings. This, in turn, can cause catastrophic effects on the robot functionality, as the controller deployed in each module is optimized for a different robot shape than the actual one. In this work, we address such shortcoming by proposing a shape-aware modular controller, operating with (1) a self-discovery phase, in which each module controller identifies the shape it is assembled in, followed by (2) a parameter selection phase, where the controller selects its parameters according to the inferred shape. We deploy a self-classifying neural cellular automaton for phase (1), and we leverage evolutionary optimization for implementing a library of controller parameters for phase (2). We test the validity of the proposed method considering voxel-based soft robots, a class of modular soft robots, and the task of locomotion. Our findings confirm the effectiveness of such a controller paradigm, and also show that it can be used to partially overcome unforeseen damages or assembly mistakes.

A Fully-distributed Shape-aware Neural Controller for Modular Robots

Nadizar, Giorgia
;
Medvet, Eric;
2023-01-01

Abstract

Modular robots are promising for their versatility and large design freedom. Modularity can also enable automatic assembly and reconfiguration, be it autonomous or via external machinery. However, these procedures are error-prone and often result in misassemblings. This, in turn, can cause catastrophic effects on the robot functionality, as the controller deployed in each module is optimized for a different robot shape than the actual one. In this work, we address such shortcoming by proposing a shape-aware modular controller, operating with (1) a self-discovery phase, in which each module controller identifies the shape it is assembled in, followed by (2) a parameter selection phase, where the controller selects its parameters according to the inferred shape. We deploy a self-classifying neural cellular automaton for phase (1), and we leverage evolutionary optimization for implementing a library of controller parameters for phase (2). We test the validity of the proposed method considering voxel-based soft robots, a class of modular soft robots, and the task of locomotion. Our findings confirm the effectiveness of such a controller paradigm, and also show that it can be used to partially overcome unforeseen damages or assembly mistakes.
2023
9798400701191
https://dl.acm.org/doi/10.1145/3583131.3590419
File in questo prodotto:
File Dimensione Formato  
2023-GECCO-FullyDistributedShapeAwareNeuralController.pdf

Accesso chiuso

Tipologia: Documento in Versione Editoriale
Licenza: Copyright Editore
Dimensione 958.54 kB
Formato Adobe PDF
958.54 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
2023-GECCO-FullyDistributedShapeAwareNeuralController-Post_print.pdf

accesso aperto

Tipologia: Bozza finale post-referaggio (post-print)
Licenza: Digital Rights Management non definito
Dimensione 1.68 MB
Formato Adobe PDF
1.68 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3053898
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact