Cooperative Localization for Autonomous Underwater Vehicles in Strong Ocean Currents

Download or read book Cooperative Localization for Autonomous Underwater Vehicles in Strong Ocean Currents written by Zhuoyuan Song and published by . This book was released on 2014 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unavailability of GPS (global positioning system) for underwater navigation has created significant challenges for operation and localization of autonomous underwater vehicles (AUVs). This is more pronounced in dynamic ocean flows where significant background flows exist. In this thesis, a collaborative underwater localization hierarchy is introduced to improve the cooperative performance of a small AUV swarm by utilizing vehicles with bounded localization error as moving references in the presence of dominating background flows. Initially represented in probability theory, the problem is then decomposed into a cooperative localization problem and a dynamic simultaneous localization and mapping problem with moving features. To address the incomplete covariance updating issue, which arises when directly applying the extended Kalman filter in fully distributed systems, the modified extended Kalman filter (MEKF) is proposed and a MEKF based algorithm is discussed in detail. A particle filter based algorithm is implemented for comparative purposes due to its advantages in modeling multimodal non-Gaussian distributions. However, it is shown that the particle filter requires greater computational effort than the MEKF when the number of vehicles is small. Both proposed algorithms are verified in three-dimensional background flow simulations. The divergent behavior of localization error, which appears when using solely cooperative localization, is avoided through the implementation of either the MEKF algorithm or the particle algorithm. Significant decreases in localization error are subsequently observed.