Mid-water current aided localization for autonomous underwater vehicles

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Autonomous Robots 40 (2016): 1207–1227, doi:10.1007/s10514-016-9547-3.Survey-class Autonomous Underwater Vehi- cles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the mid- water column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Cur- rent Profiler (ADCP) mode of the DVL during descent, along with data from other sensors, the method dis- cussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Map- ping (SLAM) and on descents up to 3km deep with the Sentry AUV.This work is supported in part by NCRIS IMOS, the Australian Research Council (ARC), the New South Wales Government and the Woods Hole Oceanographic Institution.2017-02-1

Closed‐loop one‐way‐travel‐time navigation using low‐grade odometry for autonomous underwater vehicles

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of FIeld Robotics 35 (2018): 421-434, doi:10.1002/rob.21746.This paper extends the progress of single beacon one‐way‐travel‐time (OWTT) range measurements for constraining XY position for autonomous underwater vehicles (AUV). Traditional navigation algorithms have used OWTT measurements to constrain an inertial navigation system aided by a Doppler Velocity Log (DVL). These methodologies limit AUV applications to where DVL bottom‐lock is available as well as the necessity for expensive strap‐down sensors, such as the DVL. Thus, deep water, mid‐water column research has mostly been left untouched, and vehicles that need expensive strap‐down sensors restrict the possibility of using multiple AUVs to explore a certain area. This work presents a solution for accurate navigation and localization using a vehicle's odometry determined by its dynamic model velocity and constrained by OWTT range measurements from a topside source beacon as well as other AUVs operating in proximity. We present a comparison of two navigation algorithms: an Extended Kalman Filter (EKF) and a Particle Filter(PF). Both of these algorithms also incorporate a water velocity bias estimator that further enhances the navigation accuracy and localization. Closed‐loop online field results on local waters as well as a real‐time implementation of two days field trials operating in Monterey Bay, California during the Keck Institute for Space Studies oceanographic research project prove the accuracy of this methodology with a root mean square error on the order of tens of meters compared to GPS position over a distance traveled of multiple kilometers.This work was supported in part through funding from the Weston Howland Jr. Postdoctoral Scholar Award (BCC), the U.S. Navy's Civilian Institution program via the MIT/WHOI Joint Program (JHK),W. M. Keck Institute for Space Studies, and theWoods Hole Oceanographic Institution

Cooperative localisation in underwater robotic swarms for ocean bottom seismic imaging.

Spatial information must be collected alongside the data modality of interest in wide variety of sub-sea applications, such as deep sea exploration, environmental monitoring, geological and ecological research, and samples collection. Ocean-bottom seismic surveys are vital for oil and gas exploration, and for productivity enhancement of an existing production facility. Ocean-bottom seismic sensors are deployed on the seabed to acquire those surveys. Node deployment methods used in industry today are costly, time-consuming and unusable in deep oceans. This study proposes the autonomous deployment of ocean-bottom seismic nodes, implemented by a swarm of Autonomous Underwater Vehicles (AUVs). In autonomous deployment of ocean-bottom seismic nodes, a swarm of sensor-equipped AUVs are deployed to achieve ocean-bottom seismic imaging through collaboration and communication. However, the severely limited bandwidth of underwater acoustic communications and the high cost of maritime assets limit the number of AUVs that can be deployed for experiments. A holistic fuzzy-based localisation framework for large underwater robotic swarms (i.e. with hundreds of AUVs) to dynamically fuse multiple position estimates of an autonomous underwater vehicle is proposed. Simplicity, exibility and scalability are the main three advantages inherent in the proposed localisation framework, when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation (by 16.53% and 35.17% respectively) at a swarm size of 150 AUVs when compared to the Extended Kalman Filter based localisation with round-robin scheduling. The proposed fuzzy based localisation method requires fuzzy rules and fuzzy set parameters tuning, if the deployment scenario is changed. Therefore a cooperative localisation scheme that relies on a scalar localisation confidence value is proposed. A swarm subset is navigationally aided by ultra-short baseline and a swarm subset (i.e. navigation beacons) is configured to broadcast navigation aids (i.e. range-only), once their confidence values are higher than a predetermined confidence threshold. The confidence value and navigation beacons subset size are two key parameters for the proposed algorithm, so that they are optimised using the evolutionary multi-objective optimisation algorithm NSGA-II to enhance its localisation performance. Confidence value-based localisation is proposed to control the cooperation dynamics among the swarm agents, in terms of aiding acoustic exteroceptive sensors. Given the error characteristics of a commercially available ultra-short baseline system and the covariance matrix of a trilaterated underwater vehicle position, dead reckoning navigation - aided by Extended Kalman Filter-based acoustic exteroceptive sensors - is performed and controlled by the vehicle's confidence value. The proposed confidence-based localisation algorithm has significantly improved the entire swarm mean localisation error when compared to the fuzzy-based and round-robin Extended Kalman Filter-based localisation methods (by 67.10% and 59.28% respectively, at a swarm size of 150 AUVs). The proposed fuzzy-based and confidence-based localisation algorithms for cooperative underwater robotic swarms are validated on a co-simulation platform. A physics-based co-simulation platform that considers an environment's hydrodynamics, industrial grade inertial measurement unit and underwater acoustic communications characteristics is implemented for validation and optimisation purposes

Terrain-aided navigation for long-range AUVs in dynamic under-mapped environments

Deploying long‐range autonomous underwater vehicles (AUVs) mid‐water column in the deep ocean is one of the most challenging applications for these submersibles. Without external support and speed over the ground measurements, dead‐reckoning (DR) navigation inevitably experiences an error proportional to the mission range and the speed of the water currents. In response to this problem, a computationally feasible and low‐power terrain‐aided navigation (TAN) system is developed. A Rao‐Blackwellized Particle Filter robust to estimation divergence is designed to estimate the vehicle's position and the speed of water currents. To evaluate performance, field data from multiday AUV deployments in the Southern Ocean are used. These form a unique test case for assessing the TAN performance under extremely challenging conditions. Despite the use of a small number of low‐power sensors and a Doppler velocity log to enable TAN, the algorithm limits the localisation error to within a few hundreds of metres, as opposed to a DR error of 40 km, given a 50 m resolution bathymetric map. To evaluate further the effectiveness of the system under a varying map quality, grids of 100, 200, and 400 m resolution are generated by subsampling the original 50 m resolution map. Despite the high complexity of the navigation problem, the filter exhibits robust and relatively accurate behaviour. Given the current aim of the oceanographic community to develop maps of similar resolution, the results of this study suggest that TAN can enable AUV operations of the order of months using global bathymetric models

Deep-Sea Model-Aided Navigation Accuracy for Autonomous Underwater Vehicles Using Online Calibrated Dynamic Models

In this work, the accuracy of inertial-based navigation systems for autonomous underwater vehicles (AUVs) in typical mapping and exploration missions up to 5000m depth is examined. The benefit of using an additional AUV motion model in the navigation is surveyed. Underwater navigation requires acoustic positioning sensors. In this work, so-called Ultra-Short-Baseline (USBL) devices were used allowing the AUV to localize itself relative to an opposite device attached to a (surface) vehicle. Despite their easy use, the devices\u27 absolute positioning accuracy decreases proportional to range. This makes underwater navigation a sophisticated estimation task requiring integration of multiple sensors for inertial, orientation, velocity and position measurements. First, error models for the necessary sensors are derived. The emphasis is on the USBL devices due to their key role in navigation - besides a velocity sensor based on the Doppler effect. The USBL model is based on theoretical considerations and conclusions from experimental data. The error models and the navigation algorithms are evaluated on real-world data collected during field experiments in shallow sea. The results of this evaluation are used to parametrize an AUV motion model. Usually, such a model is used only for model-based motion control and planning. In this work, however, besides serving as a simulation reference model, it is used as a tool to improve navigation accuracy by providing virtual measurements to the navigation algorithm (model-aided navigation). The benefit of model-aided navigation is evaluated through Monte Carlo simulation in a deep-sea exploration mission. The final and main contributions of this work are twofold. First, the basic expected navigation accuracy for a typical deep-sea mission with USBL and an ensemble of high-quality navigation sensors is evaluated. Secondly, the same setting is examined using model-aided navigation. The model-aiding is activated after the AUV gets close to sea-bottom. This reflects the case where the motion model is identified online which is only feasible if the velocity sensor is close to the ground (e.g. 100m or closer). The results indicate that, ideally, deep-sea navigation via USBL can be achieved with an accuracy in range of 3-15m w.r.t. the expected root-mean-square error. This also depends on the reference vehicle\u27s position at the surface. In case the actual estimation certainty is already below a certain threshold (ca. <4m), the simulations reveal that the model-aided scheme can improve the navigation accuracy w.r.t. position by 3-12%