Cooperative AUV Navigation using a Single Maneuvering Surface Craft

Abstract

This paper describes the experimental implementation of an online algorithm for cooperative localization of submerged autonomous underwater vehicles (AUVs) supported by an autonomous surface craft. Maintaining accurate localization of an AUV is difficult because electronic signals, such as GPS, are highly attenuated by water. The usual solution to the problem is to utilize expensive navigation sensors to slow the rate of dead-reckoning divergence. We investigate an alternative approach that utilizes the position information of a surface vehicle to bound the error and uncertainty of the on-board position estimates of a low-cost AUV. This approach uses the Woods Hole Oceanographic Institution (WHOI) acoustic modem to exchange vehicle location estimates while simultaneously estimating inter-vehicle range. A study of the system observability is presented so as to motivate both the choice of filtering approach and surface vehicle path planning. The first contribution of this paper is the presentation of an experiment in which an extended Kalman filter (EKF) implementation of the concept ran online on-board an OceanServer Iver2 AUV while supported by an autonomous surface vehicle moving adaptively. The second contribution of this paper is provide a quantitative performance comparison of three estimators: particle filtering (PF), Nonlinear Least Squares optimization (NLS), and the EKF for a mission using three autonomous surface craft (two operating in the AUV role). Our results indicate that the PF and NLS estimators outperform the EKF, with NLS providing the best performance.

Simultaneous Localization and Mapping in Marine Environments

“…8.5), which moved around the AUV so as to improve the observability of the AUV, as previously demonstrated in [22]. In Mission 4, as in Mission 3, the support vessel was instead used-although in this case, the support vessel moved from a location due east of the AUV to another location due west of the AUV, as illustrated in Fig.…”

“…As mentioned previously, the EKF has been shown to provide an inconsistent SLAM solution due to information lost during the linearization step [35]. Furthermore, our previous work, [22], demonstrated (off-line) the superior performance of NLS methods in the acoustic ranging problem domain versus both an EKF and a particle filtering implementation-although requiring growing computational resources. For these reasons we present here an application in which iSAM is used for full pose trajectory estimation using acoustic range data.…”

“…8.4.2, round-trip ranging is estimated to have a variance of approximately 7 m, compared with a variance of 3 m for one-way ranging reported in [22]. An additional issue is that with the ranging measurement occurring as much as 10 s before the position and range are transmitted to the AUV, an acausal update of the vehicle position estimate is required.…”

An Origin State Method for Lossy Synchronous-clock Acoustic Navigation

“…Other algorithms based upon the Kalman family of filters (Webster et al, 2009;Fallon et al, 2010;Bahr et al, 2009), and factor-graphs (Cunningham et al, 2010) also exist. These algorithms are summarized below.…”

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

“…Nevertheless, there usually are situations where the robot does not encounter a sufficient number of, or any, such features for the navigation system to reference, hence static-feature-based navigation schemes become inapplicable. The second category of aided navigation systems utilizes features' states that are time-dependent, such as the location of moving beacons [8], [13], or neighboring robots [14], [15]. However, the dynamics governing the changes in states of these features are often not utilized.…”