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GARNICS

Project reference: 247947
Funded under: FP7-ICT

Gardening with a Cognitive System [Print to PDF] [Print to RTF]

From 2010-03-01 to 2013-02-28

Project details

Total cost:

EUR 3 721 066

EU contribution:

EUR 2 871 000

Coordinated in:

Germany

Call for proposal:

FP7-ICT-2009-4

Funding scheme:

CP - Collaborative project (generic)

Actions performed at plants interacting with humans

The GARNICS project aims at 3D sensing of plant growth and building perceptual representations for learning the links to actions of a robot gardener. Plants are complex, self-changing systems with increasing complexity over time. Actions performed at plants (like watering), will have strongly delayed effects. Thus, monitoring and controlling plants is a difficult perception-action problem requiring advanced predictive cognitive properties, which so far can only be provided by experienced human gardeners. Sensing and control of the actual properties of a plant is relevant to e.g. seed production and plant breeders. Plant models will be acquired and by interacting with a human gardener the system will be taught the different cause-effect relations resulting from possible treatments. The robot gardener will be able to choose from its learned repertoire the appropriate actions for optimal plant growth.

Objective

The GARNICS project aims at 3D sensing of plant growth and building perceptual representations for learning the links to actions of a robot gardener. Plants are complex, self-changing systems with increasing complexity over time. Actions performed at plants (like watering), will have strongly delayed effects. Thus, monitoring and controlling plants is a difficult perception-action problem requiring advanced predictive cognitive properties, which so far can only be provided by experienced human gardeners. Sensing and control of a plants actual properties, i.e. its phenotype, is relevant to e.g. seed production and plant breeders. We address plant sensing and control by combining active vision with appropriate perceptual representations, which are essential for cognitive interactions. Core ingredients for these representations are channel representations, dynamic graphs and cause-effect couples (CECs). Channel representations are a wavelet-like, biologically motivated information representation, which can be generalized coherently using group theory. Using these representations, plant models -- represented by dynamic graphs -- will be acquired and by interacting with a human gardener the system will be taught the different cause-effect relations resulting from possible treatments. Employing decision making and planning processes via CECs, our robot gardener will then be able to choose from its learned repertoire the appropriate actions for optimal plant growth. This way we will arrive at an adaptive, interactive cognitive system, which will be implemented and tested in an industrially-relevant plant-phenotyping application.

Related information

Documents and Publications

Open Access

Coordinator contact

Hanno SCHARR

Coordinator

FORSCHUNGSZENTRUM JUELICH GMBH
Germany
WILHELM-JOHNEN-STRASSE
JUELICH, Germany
Administrative contact: Petra Insberg
Tel.: +49-2461-612327
Fax: +49-2461-612118
E-mail

Participants

GEORG-AUGUST-UNIVERSITAET GOETTINGEN STIFTUNG OEFFENTLICHEN RECHTS
Germany
WILHELMSPLATZ
GOETTINGEN, Germany
Administrative contact: Ursula Hahn-Woergoetter
Tel.: +49 (0)551 3910769
Fax: +49 (0)551 397720
E-mail
AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS
Spain
CALLE SERRANO
MADRID, Spain
Administrative contact: Carlos Manuel Abad Ruiz
Tel.: +34 91 566 8852
Fax: +34 91 566 89 13
E-mail
LINKOPINGS UNIVERSITET
Sweden
CAMPUS VALLA
LINKOPING, Sweden
Administrative contact: Christina Henriksson
Tel.: +46 13 281084
Fax: +46138526
E-mail
Record Number: 93712 / Last updated on: 2014-10-06