Underwater navigation that relies solely on dead reckoning (DR) suffers from unbounded position error growth. A common approach for alleviating the problem is to have the underwater vehicle surface occasionally for a Global Positioning System (GPS) fix, at the risk of jeopardizing the vehicle's safety and consuming precious mission time. Other alternatives include deploying a long-baseline (LBL) acoustic positioning system in the mission area; this involves substantial deployment effort. The idea of having active mobile beacons as navigational aids has recently gained interest. We explore the use of a single-beacon vehicle for range-only localization to support other autonomous underwater vehicles (AUVs). Specifically, we focus on cooperative path-planning algorithms for the beacon vehicle using dynamic programming and Markov decision process formulations. These formulations take into account and minimize the positioning errors being accumulated by the supported AUV. This approach avoids the use of LBL acoustic positioning systems as well as allows the supported AUV to remain submerged for a longer period of time with small position error. Simulation results and field trials data demonstrate that the beacon vehicle is able to help keep the position error of the supported AUV small via acoustic range measurements.

We present a cooperative bathymetry-based localization approach for a team of low-cost autonomous underwater vehicles (AUVs), each equipped only with a single-beam altimeter, a depth sensor and an acoustic modem. The localization of the individual AUV is achieved via fully decentralized particle filtering, with the local filter's measurement model driven by the AUV's altimeter measurements and ranging information obtained through inter-vehicle communication. We perform empirical analysis on the factors that affect the filter performance. Simulation studies using randomly generated trajectories as well as trajectories executed by the AUVs during field experiments successfully demonstrate the feasibility of the technique. The proposed cooperative localization technique has the potential to prolong AUV mission time, and thus open the door for long-term autonomy underwater.

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This paper explores the use of autonomous underwater vehicles (AUVs) equipped with sensors to construct water quality models to aid in the assessment of important environmental hazards, for instance related to point-source pollutants or localized hypoxic regions. Our focus is on problems requiring the autonomous discovery and dense sampling of critical areas of interest in real-time, for which standard (e.g., grid-based) strategies are not practical due to AUV power and computing constraints that limit mission duration. To this end, we consider adaptive sampling strategies on Gaussian process (GP) stochastic models of the measured scalar field to focus sampling on the most promising and informative regions.

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