Autonomous surface vessels are becoming increasingly important for water monitoring. Their aim is to navigate rivers and lakes with limited intervention of human operators, to collect real-time data about water parame...
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Autonomous surface vessels are becoming increasingly important for water monitoring. Their aim is to navigate rivers and lakes with limited intervention of human operators, to collect real-time data about water parameters. To reach this goal, these intelligent systems must interact with the environment and act according to the situations they face. In this work we propose a framework based on the integration of recent time-series clustering/segmentation methods and cluster validity indices, for detecting, modeling and evaluating aquatic drone states. The approach is completely data-driven and unsupervised. It takes unlabeled multivariate time series of sensor traces and returns both a set of statistically significant state-models (generated by different mathematical approaches) and a related segmentation of the dataset. We test the approach on a real dataset containing data of six campaigns, two in rivers and four in lakes, in different countries for about 5.6 h of navigation. Results show that the methodology is able to recognize known states and to discover unknown states, enabling novelty detection. The approach is therefore an easy-to-use tool for discovering and interpreting significant states in sensor data, that enables improved data analysis and drone autonomy.
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