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DEEPFIELD

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DeepField Deep Learning in Field Robotics from conceptualization towards imple...

DeepField Deep Learning in Field Robotics from conceptualization towards implementation Robots are active agents that need to interact with the physical world, to do so, robots are equipped with different sensors, whose data is used to build models that ultimately will allow robots to plan actions and make decisions.... ver más

30/09/2023

INESC TEC

800K€

Presupuesto del proyecto: 800K€

Líder del proyecto

INESC TEC INSTITUTO DE ENGENHARIADE SISTEMAS... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 6 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-09-30

WIDESPREAD-03-2018: Twinning Specific Challenge:The specific challenge is to enhance networking activities between the research i...

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Información adicional privada

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

6 Participantes

INESC TEC

287.54K€ | Lider

INSTITUTO BIOMAR

281.15K€ | Participante

UDG

122.88K€ | Participante

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Duración del proyecto: 51 meses Fecha Inicio: 2019-06-20
Fecha Fin: 2023-09-30

Líder del proyecto

INESC TEC INSTITUTO DE ENGENHARIADE SISTEMAS... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 800K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Robots are active agents that need to interact with the physical world, to do so, robots are equipped with different sensors, whose data is used to build models that ultimately will allow robots to plan actions and make decisions. Currently, there is strong focus in developing deep learning strategies data driven to help solve this perception problem, even though these approaches work well in dataset and benchmark scenarios. There are still strong limitations in the use of this techniques in real world robot activities, specially due to the strong dynamics in robots operational environment, that is pushing the development of new tools and methods to make these approaches feasible in the real world. INESC TEC is strongly committed to become a centre of excellence with focus on field robotics, in particular, in the aerial and underwater robotics domain. In the last years, the centre for Robotics and Autonomous systems, of INESC TEC has advance its scientific knowledge in sensing and perception methods for robots navigation and localization in harsh operational environments. The key objective of INESC TEC is to become one of the European references in field robotics, and help to bring robot technology to solve real life problems where human intervention is still limited or non-existent. This proposal aims at creating solid knowledge and productive links in the global field of deep learning in field robotics between INESC TEC and established leading research European institutions, capable of enhancing the scientific and technological capacity of INESC TEC and linked institutions (as well as the capacity of partnering institutions involved in the twinning action), helping raising its staff’s research profile and its recognition as an European research centre of excellence in field robotics. In particular, it takes INESC TEC and places it as the pivot of a network of excellence, involving four international leaders in deep learning technology and fied robotics.

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