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DEEPFIELD SIGNED

DeepField- Deep Learning in Field Robotics: from conceptualization towards implementation

Total Cost €

EC-Contrib. €

Partnership

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DEEPFIELD project word cloud

Explore the words cloud of the DEEPFIELD project. It provides you a very rough idea of what is the project "DEEPFIELD" about.

dynamics robot techniques physical pushing life global active environments perception navigation tec twinning limitations places solid sensing action data existent pivot equipped though world dataset international specially partnering scenarios ultimately strategies last scientific learning aerial interact decisions limited models excellence human technological agents helping staff deep localization takes leaders capacity productive harsh environment committed linked domain profile fied benchmark feasible links centre intervention robots sensors references plan autonomous network robotics tools inesc actions underwater recognition

Project "DEEPFIELD" data sheet

The following table provides information about the project.

Coordinator	INESC TEC - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIENCIA Organization address address: RUA DR ROBERTO FRIAS CAMPUS DA FEUP city: PORTO postcode: 4200 465 website: www.inescporto.pt contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
Coordinator Country	Portugal [PT]
Total cost	799˙975 €
EC max contribution	799˙975 € (100%)
Programme	1. H2020-EU.4.b. (Twinning of research institutions)
Code Call	H2020-WIDESPREAD-2018-03
Funding Scheme	CSA
Starting year	2019
Duration (year-month-day)	from 2019-10-01 to 2022-09-30

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	INESC TEC - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIENCIA INESC TEC - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIENCIA Organization address address: RUA DR ROBERTO FRIAS CAMPUS DA FEUP city: PORTO postcode: 4200 465 website: www.inescporto.pt contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	PT (PORTO)	coordinator	287˙537.00
2	HERIOT-WATT UNIVERSITY HERIOT-WATT UNIVERSITY Organization address address: Riccarton city: EDINBURGH postcode: EH14 4AS website: www.hw.ac.uk contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	UK (EDINBURGH)	participant	130˙312.00
3	MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV Organization address address: HOFGARTENSTRASSE 8 city: MUENCHEN postcode: 80539 website: n.a. contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	DE (MUENCHEN)	participant	130˙000.00
4	POLITECNICO DI MILANO POLITECNICO DI MILANO Organization address address: PIAZZA LEONARDO DA VINCI 32 city: MILANO postcode: 20133 website: www.polimi.it contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	IT (MILANO)	participant	129˙250.00
5	UNIVERSITAT DE GIRONA UNIVERSITAT DE GIRONA Organization address address: PLACA SANT DOMENEC 3 city: GIRONA postcode: 17004 website: www.udg.es contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	ES (GIRONA)	participant	122˙875.00

Map

Project objective

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.

Publications

List of publications.
year	authors and title	journal	last update
2019	Sara Freitas, Hugo Silva, JosÃ© Miguel Almeida, Eduardo Silva Convolutional neural network target detection in hyperspectral imaging for maritime surveillance published pages: 172988141984299, ISSN: 1729-8814, DOI: 10.1177/1729881419842991	International Journal of Advanced Robotic Systems 16/3	2019-11-28
2019	Bernardo Teixeira \"\"\"Deep Learning Approaches Assessment for Underwater Scene Understanding and Egomotion Estimation\"\"\" published pages: , ISSN: , DOI:		2019-11-28
2019	AntÃ³nio Pedro Oliva Afonso A comparative study of machine learning techniques for underwater visual object recognition published pages: , ISSN: , DOI:		2019-11-28

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The information about "DEEPFIELD" are provided by the European Opendata Portal: CORDIS opendata.