Adaptive Sampling Using an Unsupervised Learning of GMMs
A Real-Time Multi-Hypothesis Algorithm Applied to a Fleet of AUVs with CTD Measurements
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This book addresses the problem of real-time adaptive sampling using a coordinated fleet of Autonomous Underwater Vehicles (AUVs). The system setup consists of one leader AUV and one or more follower AUV(s), all equipped with conductivity, temperature and depth (CTD) sensor devices and capable of running an on-line unsupervised learning of Gaussian Mixture Models (GMMs) algorithm. The proposed multi-hypothesis adaptive algorithm continuously updates the number of components and estimates the GMM parameters in real-time, without any initial batch of data. The path to be traced by the leader is ...
This book addresses the problem of real-time adaptive sampling using a coordinated fleet of Autonomous Underwater Vehicles (AUVs). The system setup consists of one leader AUV and one or more follower AUV(s), all equipped with conductivity, temperature and depth (CTD) sensor devices and capable of running an on-line unsupervised learning of Gaussian Mixture Models (GMMs) algorithm. The proposed multi-hypothesis adaptive algorithm continuously updates the number of components and estimates the GMM parameters in real-time, without any initial batch of data. The path to be traced by the leader is predefined. The followers path will depend on the CTD data. More precisely, during each resurfacing of the AUVs (which is done in a coordinated fashion), every follower AUV receives the GMM hypothesis of the leader and computes a distance error between its own GMM and the received one. This error, that provides a notion of how different is the CTD data of each follower from the leader, is used to reconfigure the formation by scaling the distance between the AUVs in the formation (making a zoom-in and zoom-out), in order to improve the efficiency of the CTD data acquisition in a given region.
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