Cooperative Localization for Autonomous Underwater Vehicles

Abstract

Self-localization of an underwater vehicle is particularly challenging due to the absence of Global Positioning System (GPS) reception or features at known positions that could otherwise have been used for position computation. Thus Autonomous Underwater Vehicle (AUV) applications typically require the pre-deployment of a set of beacons.This thesis examines the scenario in which the members of a group of AUVs exchange navigation information with one another so as to improve their individual position estimates.We describe how the underwater environment poses unique challenges to vehicle navigation not encountered in other environments in which robots operate and how cooperation can improve the performance of self-localization. As intra-vehicle communication is crucial to cooperation, we also address the constraints of the communication channel and the effect that these constraints have on the design of cooperation strategies.The classical approaches to underwater self-localization of a single vehicle, as well as more recently developed techniques are presented. We then examine how methods used for cooperating land-vehicles can be transferred to the underwater domain. An algorithm for distributed self-localization, which is designed to take the specific characteristics of the environment into account, is proposed.We also address how correlated position estimates of cooperating vehicles can lead to overconfidence in individual position estimates.Finally, key to any successful cooperative navigation strategy is the incorporation of the relative positioning between vehicles. The performance of localization algorithms with different geometries is analyzed and a distributed algorithm for the dynamic positioning of vehicles, which serve as dedicated navigation beacons for a fleet of AUVs, is proposed.

Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

“…The idea of AUV cooperative positioning is to have a vehicle with good quality positioning information (beacon vehicle), to transmit its position and range information acoustically to supported AUVs within its communication range during navigation. The range information between the vehicles can then be fused with the data obtained from proprioceptive sensors to reduce the positioning error during underwater navigation (Rui and Chitre 2010;Bahr et al 2009a). Since acoustic ranging only contains information in the direction of ranging, the performance of the approach relies on the ability of the beacon vehicle to perform ranging from different directions with respect to the supported vehicles (Song 1999;Gadre and Stilwell 2005).…”

“…The range information received is commonly used directly to influence the filter's measurement model for beacon-based underwater navigation (Rui and Chitre 2010;Bahr et al 2009a;Tan et al 2014;Maurya et al 2012;Webster et al 2013). However, our approach cannot fuse the range information directly because none of the vehicles in the team is equipped with high accuracy navigational sensors, and the PV may have accumulated significant error by the time the information is broadcast.…”

A distributed localization hierarchy for an AUV swarm

“…Many investigations have been done in regard to solving single-robot localization problems using the PF [17]. It has also been applied to multi-robot localization problems [5], [18], SLAM problems [19] and underwater localization problems [20].…”

“…Multi-agent cooperation is not only helpful but also necessary in small AUV swarms. In the past decade, CL has been widely applied to applications where GPS is not available including indoor and underwater applications [6]- [9]. However, at least one localization reference is required to bound the localization error of the entire mobile network.…”

BathyBoat: An Autonomous Surface Vessel for Stand-alone Survey and Underwater Vehicle Network Supervision

“…In addition to faster coverage of an area, the use of multiple subsea vehicles also results in more accurate positioning information (Bahr et al, 2009.…”