A comparison between EKF-based and UKF-based navigation algorithms for AUVs localization

Abstract

Autonomous Underwater Vehicles (AUVs) are increasingly employed in underwater operations within many scientific and industrial tasks (e.g. Oil&Gas operations, exploration and surveillance of archaeological sites, reconnaissance and patrolling for military operations). Autonomous underwater navigation is critical due to lack of access to satellite navigation systems (e.g. the Global Positioning System, GPS) and to the typical low functioning rate of the acoustic underwater localization devices typically used. As a result, an AUV must typically proceed for long time intervals in dead-reckoning, i.e. only relying on the measurements of on-board sensors. In this context, the filtering algorithms used to estimate the state of the AUV play a fundamental role in guaranteeing satisfactory underwater navigation accuracy. In this paper, the authors present a comparison between underwater navigation systems relying on either the Extended Kalman Filter (EKF) or the Unscented Kalman Filter (UKF) for the AUV state estimation. These approaches have been currently tested offline running on the experimental data collected with the Typhoon-class AUVs (TifOne and TifTu) during different missions at sea. Typhoon is an AUV designed by the Department of Industrial Engineering of the University of Florence for exploration and surveillance of underwater archaeological sites in the framework of the THESAURUS project (2011-2013 funded by Tuscan Region) and the European ARROWS project. A performance comparison between the proposed UKF-based navigation system and the standard, EKF-based, system is here presented basing on the experimental data of different missions at sea. In particular the proposed missions are the final demo of the THESAURUS project in Livorno (Italy) in August 2013 and the preliminary tests at sea of the ARROWS project performed during the Breaking the Surface workshop in Biograd na Moru (Croatia) during October 2014. The achieved results are encouraging: the research outcome has shown the effectiveness of both navigation algorithms and the superiority of the UKF

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

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A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

“…Lack of robustness of system failures resulted in less efficiency for this proposed method. An optimal path planning strategy for the AUV to eliminate sound velocity profile prediction uncertainty in the water has been discussed by Sun et al [158] The proposed methodology is based on the MAP estimation framework and one step Kalman filters. Allotta et al [159] compared underwater path planning systems relying on EKF and on "unscented Kalman filter (UKF)" for estimating the AUV position.…”

Robust Self-Contained Pedestrian Navigation by Fusing the IMU and Compass Measurements via UFIR Filtering

“…As one of the most used data fusion algorithm, the Kalman filter (KF) and its improving methods have been widely used in many fields [30][31][32][33][34]. And its pseudo code is listed as Algorithm 1.…”