Robot learning applied to autonomous underwater vehicles for intervention tasks

The interest in performing underwater tasks using Autonomous Underwater Vehicles (AUVs) has been growing over the past few years. In this thesis, a flexible framework for underwater interventions using a Learning by Demonstration algorithm as a core has been developed. This algorithm allows to the robot's user to transfer a skill or knowledge to the I-AUV using a natural and intuitive form. The developed framework for interventions has been tailored to the GIRONA 500 AUV in order to enable it to perform an underwater valve turning task under different conditions. The GIRONA 500 has been equipped with a 4 DOF Manipulator and a custom end-effector. Throughout this thesis, the experiments developed have been carried out in a mock-up scenario of a sub-sea installation with a valve panel. The difficulty of the task has been increased gradually in order to test the new improvements and the robustness in the proposed framework ​
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