Time series segmentation for state-model generation of autonomous aquatic drones: A systematic framework. (April 2020)

• Record Type: Journal Article
• Authors: Castellini, Alberto; Bicego, Manuele; Masillo, Francesco; Zuccotto, Maddalena; Farinelli, Alessandro
• Is Part Of: Engineering applications of artificial intelligence. Volume 90(2020)
• Publication Date: 2020-04
• Subjects: Time series segmentation -- Situation assessment -- State-model generation -- Autonomous surface vessels -- Activity recognition -- Water monitoring -- Model interpretation/explanation -- Sensor data analysis; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103499
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.