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AIforUTracking SIGNED

Artificial Intelligence methods for Underwater target Tracking

Total Cost €

0

EC-Contrib. €

0

Partnership

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Project "AIforUTracking" data sheet

The following table provides information about the project.

Coordinator
AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS 

Organization address
address: CALLE SERRANO 117
city: MADRID
postcode: 28006
website: http://www.csic.es

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 Spain [ES]
 Total cost 226˙801 €
 EC max contribution 226˙801 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2019
 Funding Scheme MSCA-IF-GF
 Starting year 2020
 Duration (year-month-day) from 2020-05-15   to  2022-12-14

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS ES (MADRID) coordinator 226˙801.00
2    MONTEREY BAY AQUARIUM RESEARCH INSTITUTE US (MOSS LANDING) partner 0.00

Map

 Project objective

The Artificial Intelligence methods for Underwater target Tracking (AIforUTracking) project will bring to the scientific community new tools for underwater target tracking by Autonomous Underwater Vehicles (AUVs) using Reinforcement Learning (RL) techniques. Moving towards the envisioned applications of marine animal tracking by autonomous vehicles, this proposal is clearly at the forefront of research, and directly addresses some of the main challenges and needs of the last Marine Strategy Framework Directive of the European parliament and of the Council, in particular establishing a framework for community action in the field of marine environmental policy. This research project will directly contribute to maintain and improve the health of the ocean by establishing innovative and unique research collaborations, and by introducing novel concepts and original research strategies that could provoke breakthroughs in the field of marine animal behavioural studies by:

a) Designing and developing optimisation algorithms that leverage new RL approaches, such as Partially Observable Markov Decision Process (POMDP) and Multi-Agent Reinforcement Learning (MARL). These Artificial Intelligence (AI) tools will increase the autonomy of the AUVs while improving the accuracy of the estimated target position.

b) Demonstrating the effectiveness and application of the path optimisation technique using POMPD and MARL methods by conducting real tests in the ocean, i.e. different targets will be tracked using a single AUV or multiple AUVs, as a proof-of-concept. These innovative technologies, together with Range-Only and Single-Beacon (ROSB) and Area-Only Target Tracking (AOTT) methods, are more competitive and offers greater autonomy than the traditional Long BaseLine (LBL) arrays-based methods.

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

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