On AUV actions to correctly label world information

An Autonomous Underwater Vehicle (AUV) needs to demonstrate a number of capabilities, in order to carry on autonomous missions with success. One of the key areas is correctly understanding the surrounding environment. However, most of the state-of-the-art approaches in labelling world information are based on the analysis of a single frame, whilst — especially in scenarios where the vehicle interact with complex structures — there is the need of sensor data from multiple views, in order to correctly classify world information. This paper presents an active approach to solve this problem, with a tree-based (path)-planner which makes the vehicle executing a specific set of actions (following a specific trajectory), in order to discriminate among several possibilities. Results in simulation, with varying parameters, have shown that the algorithm is always bringing the robot to locations where it is expected that measurements would be different, according to the different environment.