Cognitive system for autonomous underwater intervention

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The implementation of autonomous intervention tasks with underwater vehicles is a non-trivial issue due to the challenging and dynamic conditions of the underwater medium (e.g., water current perturbations, water visibility). Likewise, it requires a significant programming effort each time that the vehicle must perform a different manipulation operation. In this paper we propose, instead, to use a cognitive system that learns the intervention task from an expert operator through an intuitive learning by demonstration (LbD) algorithm. Taking as an input few operator demonstrations, the algorithm generalizes the task knowledge into a model and is able to control the vehicle and the manipulator simultaneously to reproduce the task, thus conferring a more adaptive behavior in front of the environment changes and allowing to easily transfer the knowledge of new tasks. A cognitive architecture has been implemented in order to integrate the LbD algorithm with the onboard sensors and actuators and to allow its interplay with the vehicle perception, control and navigation modules. To validate the full framework we present real experiments in a water tank using an AUV equipped with a four DoF manipulator. A human operator teaches the system to perform a valve turning intervention and we analyze the results of multiple task reproductions, including cases under the effect of water current perturbations, showing the success of the system in autonomously reproducing the task ​
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