This paper presents an acoustic localization system for small and low-cost autonomous underwater vehicles (AUVs). Accurate and robust localization for low-cost AUVs would lower the barrier toward multi-AUV research in river and ocean environments. However, these AUVs introduce size, power, and cost constraints that prevent the use of conventional AUV sensors and acoustic positioning systems, adding great difficulty to the problem of underwater localization. Our system uses a single acoustic transmitter placed at a reference point and is acoustically passive on the AUV, reducing cost and power use, and enabling multi-AUV localization. The AUV has an ultrashort baseline (USBL) receiver array that uses one-way traveltime (OWTT) and phased-array beamforming to calculate range, azimuth, and inclination to the transmitter, providing an instantaneous estimate of the vehicle location. This estimate is fed to a particle filter and graph-based smoothing algorithm to generate a consistent AUV trajectory. We describe the complete processing pipeline of our system, and present results based on experiments using a low-cost AUV. To the authors' knowledge, this work constitutes the first practical demonstration of the feasibility of OWTT inverted USBL navigation for AUVs.

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The conditions for maximum power transfer from a source antenna to a receiving antenna are examined when the two antennas are in close proximity. As an example, computed and measured results are described for the power transfer efficiency for two-element Yagi antennas. These results can be used to design matching networks between the antenna and a load such as a voltage multiplier for power transfer in a wireless sensor network. It is concluded that maximum PTE could be obtained by continuously tuning the antenna and matching network as the antenna separation and load conditions change.

The underwater environment poses significant challenges for accurate autonomous underwater vehicle (AUV) navigation. Electromagnetic (EM) waves rapidly attenuate due to absorption by water, thereby preventing the use of traditional EM-based positioning methods such as Global Positioning System (GPS) or visible-light cameras. Consequently, underwater positioning is often performed using systems that operate in the hydro-acoustic frequency range (≤ 10 MHz). Recent work has demonstrated the efficacy of a novel acoustic positioning approach for multi-AUV operations called passive inverted ultra-short baseline (piUSBL) localization -with each vehicle equipped with a time-synchronized USBL array, oneway travel-time (OWTT) range and angle between the AUV and a single acoustic beacon enables multi-AUV navigation relative to the beacon. In this work, a piUSBL system using a five-hydrophone pyramidal array implemented on a WAM-V autonomous surface vehicle (ASV) was used to experimentally gather acoustic measurements and to compare the accuracy of piUSBL localization against ground-truth from a differential GPS unit. This paper provides a comprehensive analysis of the positioning accuracy of the system in a real-world environment, both prior to and after Bayesian filtering, using two independent acoustic beacons for validation. We demonstrate that piUSBL provides acoustic range and angle measurements with errors of about µ ± σ = 0.03 ± 1.49 m and µ ± σ = −0.11 ± 3.16 • respectively. These experimental results suggest that piUSBL localization can provide a highly accurate, inexpensive, and lowpower navigation solution for the next generation of miniature, low-cost underwater vehicle.

The underwater environment severely constrains robotic navigation and communications, making the use of traditional multi-robot control and coordination schemes very difficult. These challenges are further exacerbated on a new generation of low-cost autonomous underwater vehicle (AUV) that lack a Doppler velocity log (DVL), acoustic modem or high-end inertial sensors typically used for underwater robotic navigation and communications. This work demonstrates multirobot operations for low-cost AUVs via a novel and user-friendly operating paradigm that allows intuitive command and control of an AUV group. Each vehicle is equipped with a low-power and inexpensive acoustic system that enables it to navigate and receive operator commands. This system consists of a passive array of hydrophones and a timed acquisition and data processing stack that allows each AUV to self-localize relative to a single time-synchronized acoustic beacon. Switching between different operational 'modes' on the beacon causes it to broadcast different acoustic signals which, when received by the AUVs, result in the vehicles switching between different autonomous behaviors. These behaviors are defined in a beaconcentric coordinate system using pre-defined parameters unique to each vehicle; as a result, the movement of the beacon itself allows the operator to control the group-wide movement of all vehicles concurrently. This work presents field experiments with three SandShark AUVs in which the beacon and operator are collocated on a motorboat, allowing both operational mode and beacon movement to be controlled manually. However, by installing the beacon on a conventional mid or large-size AUV or autonomous surface vehicle (ASV), this paradigm provides a method for the remote command and control of an arbitrarily large number of miniature, low-cost AUVs, without the need for sophisticated navigational sensors or acoustic modems.

This work implements a hydrodynamic model-based localization and navigation system for low-cost autonomous underwater vehicles (AUVs) that are limited to a micro-electro mechanical system (MEMS) inertial measurement unit (IMU). The hydrodynamic model of this work is uniquely developed to directly determine the linear velocities of the vehicle using the measured vehicle angular rates and propeller speed as inputs. The proposed system was tested in the field using a fleet of low-cost Bluefin SandShark AUVs. Implementation of the model-based localization system and fusing of the solution into the vehicle navigation loop was conducted using backseat computers of the AUV fleet that run mission orientated operating suite -interval programming (MOOS-IvP). With the model-based navigation system, the maximum localization error (i.e., in comparison to a long baseline (LBL) based ground-truth position) was limited to 15 m and 30 m for two 650-second and 1070-second long missions. Extrapolation of the position drift shows that the model-based localization system is able to limit the position uncertainty to less than 100 m by the end of hour-long mission; whereas, the drift in the default IMU-based localization solution was over 1 km per hour. This is a considerable improvement by only using a MEMS IMU that generally costs less than $100. Furthermore, this work is a step towards generalizing and automating the process of hydrodynamic modeling, model parameter estimation and data fusion (i.e., fusing the localization solution with those from other available aiding sensors and feeding to the navigation loop) so that a model-based localization system can be implemented in any AUV that has backseat computing capability.

This paper presents a method for processing sparse, non-Gaussian multimodal data in a simultaneous localization and mapping (SLAM) framework using factor graphs. Our approach demonstrates the feasibility of using a sum-product inference strategy to recover functional belief marginals from highly non-Gaussian situations, relaxing the prolific unimodal Gaussian assumption. The method is more focused than conventional multi-hypothesis approaches, but still captures dominant modes via multi-modality. The proposed algorithm exists in a trade space that spans the anticipated uncertainty of measurement data, task-specific performance, sensor quality, and computational cost. This work leverages several major algorithm design constructs, including clique recycling, to put an upper bound on the allowable computational expense -a major challenge in non-parametric methods. To better demonstrate robustness, experimental results show the feasibility of the method on at least two of four major sources of non-Gaussian behavior: i) the first introduces a canonical range-only problem which is always underdetermined although composed exclusively from Gaussian measurements; ii) a realworld AUV dataset, demonstrating how ambiguous acoustic correlator measurements are directly incorporated into a non-Gaussian SLAM solution, while using dead reckon tethering to overcome short term computational requirements.

Beam-time delay domain deconvolved scheme for high-resolution active localization of underwater targets