Ítem
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El-Fakdi Sencianes, Andrés
Carreras Pérez, Marc Palomeras Rovira, Narcís Ridao Rodríguez, Pere |
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| Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task | |
| http://hdl.handle.net/2072/58633 | |
| eng | |
| IEEE | |
| Tots els drets reservats | |
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Aprenentatge per reforç
Robots autònoms -- Sistemes de control Vehicles submergibles -- Sistemes de control Autonomous robots -- Control systems Reinforcement learning Submersibles -- Control systems |
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| Autonomous underwater vehicle control using reinforcement learning policy search methods | |
| info:eu-repo/semantics/article | |
| Recercat |
