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Scalable Co optimization of Collective Robotic Mobility and the Artificial Envir...

Scalable Co optimization of Collective Robotic Mobility and the Artificial Environment The behavior of intelligent systems, both living and artificial, is influenced through the structure of their surrounding environment. In nature, environmental constraints dictate the creation, unfolding, and interaction of living... ver más

30/06/2026

THE CHANCELLOR MAS...

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-10-07

ERC-2020-STG: ERC STARTING GRANTS

I+D

Cerrada hace 5 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

THE CHANCELLOR MASTERS AND S...

1.50M€ | Lider

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Duración del proyecto: 68 meses Fecha Inicio: 2020-10-07
Fecha Fin: 2026-06-30

Líder del proyecto

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The behavior of intelligent systems, both living and artificial, is influenced through the structure of their surrounding environment. In nature, environmental constraints dictate the creation, unfolding, and interaction of living beings. Living systems are prototypes for collective robot behaviors— yet, despite the obvious influence of spatial constraints on interactions, the optimization of mobile robots and their immediate environment has been disjoint. Little thought has been given to what would make an artificial environment conducive to effective and efficient collective robotic mobility. The premise of this project is that the environment is as much a variable as the robot itself. I want to expose the coupling between environmental structure and collective robotic mobility. In pursuit of this goal, I propose a co-optimization scheme that finds the best robot-environment pairs in an automated, scalable manner. The work in this project will (i) optimize control policies that define the behavior of collective mobile robot systems, and (ii) find environments that are more conducive to efficient coordination and cooperation. The developed techniques will allow us to perform first-of-a-kind analyses that would reveal novel environmental paradigms and the collective robot policies optimized around them. Ultimately, this project will spearhead new ways of thinking about transport planning and urban design, in the wake of a new generation of mobile vehicles that are connected and coordinated.

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