Descrizione del progetto
Rafforzare i veicoli autonomi intelligenti per lo studio degli animali marini
Uno dei nostri obiettivi europei comuni è la protezione della salute e della biodiversità dell’ecosistema marino poiché oggi l’ambiente subacqueo, di fondamentale importanza, è in pericolo. Tuttavia, la protezione e la conservazione delle acque marine europee richiedono oggi approcci completamente nuovi e innovativi per ottenere miglioramenti reali. Il progetto AIforUTracking, finanziato dall’UE, condurrà ricerche all’avanguardia incentrate sul tracciamento di animali marini mediante veicoli autonomi usando tecniche di apprendimento per rinforzo (RL, Reinforcement Learning). Grazie a nuove strategie e collaborazioni che consentono applicazioni quali il processo decisionale di Markov parzialmente osservabile (POMDP, Partially Observable Markov Decision Process) o l’apprendimento per rinforzo multi-agente (MARL, Multi-Agent Reinforcement Learning) per rivoluzionare le possibilità di studio degli animali marini, saranno progettati nuovi algoritmi per una maggiore autonomia delle macchine. Tali metodi saranno testati e aggiornati per ottenere risultati migliori.
Obiettivo
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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Meccanismo di finanziamento
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinatore
28006 Madrid
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