AIforUTracking · Artificial Intelligence methods for Underwater target Tracking
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-03-01 → 2023-09-30
- EU contribution
- €226,802
- Participants
- 2
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Artificial Intelligence methods for Underwater target Tracking
The Artificial Intelligence methods for Underwater target Tracking (AIforUTracking) project has brought to the scientific community new tools for underwater target tracking by Autonomous Underwater Vehicles (AUVs) using Reinforcement Learning (RL) techniques. This project was clearly at the forefront of research and has directly addressed some of the main challenges and needs of the last Marine Strategy Framework Directive of the European parliament, in particular establishing a framework for community action in the field of marine environmental policy. Specifically, it contributed to: (a) designing and developing optimization 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 have increased the autonomy of the AUVs while improving the accuracy of the estimated target position; and (b) demonstrating the effectiveness and application of the path optimization technique using POMPD and MARL methods by conducting real tests in the ocean, i.e. different targets have been tracked using a single AUV or multiple AUVs, as a proof-of-concept. These innovative technologies, together with Range-Only and Single-Beacon (ROSB) methods, are more competitive and offer greater autonomy than the traditional Long BaseLine (LBL) array-based methods. In conclusion, AIforUTracking has demonstrated for the first time that RL and MARL algorithms can be used to improve the target localization and tracking of underwater targets using autonomous vehicles, and the results obtained have been published in top-ranked journals. The studies conducted within this project have demonstrated the capabilities of vehicle path-planning systems using machine-learning techniques. This opens a new line of research to improve the study of the ocean and its inhabitants.
Data: CORDIS, © European Union
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.
Original text from CORDIS.
Participants
- AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS · MadridCoordinatorSpain
- MONTEREY BAY AQUARIUM RESEARCH INSTITUTE · Moss LandingUnited States
Links
Data: CORDIS, © European Union