Mid-water Localisation for Autonomous Underwater Vehicles

Survey-class Autonomous Underwater Vehicles (AUVs) rely on Doppler Velocity Logs (DVL) for precise localisation and navigation near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localising between the surface, where GPS is available, and the seafloor presents a localisation problem since both GPS and DVL are unavailable in the mid-water column. Reliance on acoustic tracking methods such as Ultra Short Base Line (USBL) requires a ship to track the vehicle, while Long Base Line (LBL) requires the setting up of an acoustic transponder network. These methods provide bounded error position localisation (~10m) of the underwater vehicle, but inhibits the flexibility and autonomy of the vehicle due to tending or set-up requirements. Proposed alternatives to these include combining GPS on the surface, navigation-grade IMU, the DVL water-track mode and a vehicle model to reduce the dead-reckoning error, although results show that this error is still not competitive with acoustic tracking methods after approximately 10 minutes of descent. Often ocean depth requires hours of descent without GPS or DVL, thus acoustic tracking methods are preferred. This work proposes a solution to localisation in the mid-water column that exploits the fact that current profile layers of water columns are stable over short periods of time (in the scale of minutes). As demonstrated in simulation, using observations of these currents with the ADCP (Acoustic Doppler Current Profiler) mode of the DVL during descent, along with sensor fusion of other low cost sensors, position error growth can be constrained to near the initial velocity uncertainty of the vehicle at the sea surface during a vertical dive. Following DVL bottom-lock, the entire velocity history is constrained to an error similar to the DVL velocity uncertainty. When coupled with a tactical-grade IMU and Time Differenced Carrier Phase (TDCP) GPS measurements, approximately 15 m/hr (2 sigma) position error growth is possible prior to DVL bottom-lock, and 6.5 m/hr (2 sigma) position error growth is possible following DVL bottom-lock. The method is demonstrated using real data from the Sirius AUV coupled with on-bottom view-based SLAM (Simultaneous Localisation and Mapping), without the use of an IMU. Horizontal localisation in the mid-water zone is also explored using an extension to the water-layer framework. The layered water currents are extended to include horizontal gridding, while the ADCP sensor is remodelled to use beam coordinates to exploit horizontal observation. The water current vector field is modelled as correlated spatially through neighbourhood least-squared constraints. Simulations illustrate the performance possible with this method, and results from real data validate this approach. In order to minimize the dead-reckoning error during mid-water zone transits, a novel method to incorporate Inertial Measurements and the constraints of a drag-based vehicle model is outlined. The drag-based Vehicle model uses the water current velocity estimates from the ADCP aiding method, while also accounting for the error from the Vehicle parameters given a prior system identification. Due to the redundant observations of motion from the IMU and DVL when available, there is potential for further improvement in estimates of the Vehicle parameters. Simulations are undertaken to assess the advantage of incorporating a vehicle model, and application on real data from the Sirius AUV validates this method

Contributions to automated realtime underwater navigation

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2012This dissertation presents three separate–but related–contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field–some have been demonstrated in all three arenas. The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors. The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current–the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework–without altering the filter structure or requiring any ad-hoc adjustments. The end result is a mathematically principled treatment of the delay that leads to more consistent estimates with lower error and uncertainty. Field results from dead reckoning aided by acoustic positioning systems demonstrate the applicability of this approach to real-world problems in underwater navigation.I have been financially supported by: the National Defense Science and Engineering Graduate (NDSEG) Fellowship administered by the American Society for Engineering Education, the Edwin A. Link Foundation Ocean Engineering and Instrumentation Fellowship, and WHOI Academic Programs office

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%

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

Multiple Autonomous Systems in Underwater Mine Countermeasures Mission Using Various Information Fusion as Navigation Aid

Autonomous bottom mine neutralization systems have a challenging task of mine reacquisition and navigation in the demanding underwater environment. Even after mine reacquisition, the neutralization payload has to be autonomously deployed near the mine, and before any action the verification (classification) of the existence of a mine has to be determined. The mine intervention vehicle can be an expendable (self-destroyed during the mine neutralization) or a vehicle that deploys the neutralization payload and it is retrieved at the end of the mission. Currently the systems developed by the research community are capable of remotely navigating a mine intervention underwater vehicle in the vicinity of the mine by using remote sonar aided navigation from a master vehicle. However, the task of successfully navigating the vehicle that carries the neutralization payload near the bottom and around the mine remains a challenge due to sea bottom clutter and the target signature interfering with the sonar detection. We seek a solution by introducing navigation via visual processing near the mine location. Using an onboard camera the relative distance to the mine-like object can be estimated. This will improve the overall vehicle navigation and rate of successful payload delivery close to the mine. The paper will present the current navigation system of the mine intervention underwater vehicle and the newly developed visual processing for relative position estimation

CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey

Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized

Efficient and Featureless Approaches to Bathymetric Simultaneous Localisation and Mapping

This thesis investigates efficient forms of Simultaneous Localization and Mapping (SLAM) that do not require explicit identification, tracking or association of map features. The specific application considered here is subsea robotic bathymetric mapping. In this context, SLAM allows a GPS-denied robot operating near the sea floor to create a self-consistent bathymetric map. This is accomplished using a Rao-Blackwellized Particle Filter (RBPF) whereby each particle maintains a hypothesis of the current vehicle state and map that is efficiently maintained using Distributed Particle Mapping. Through particle weighting and resampling, successive observations of the seafloor structure are used to improve the estimated trajectory and resulting map by enforcing map self consistency. The main contributions of this thesis are two novel map representations, either of which can be paired with the RBPF to perform SLAM. The first is a grid-based 2D depth map that is efficiently stored by exploiting redundancies between different maps. The second is a trajectory map representation that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronises it to a common log of bathymetric observations. Upon detecting a loop closure each particle is weighted by matching new observations to the current predictions. For the grid map approach this is done by extracting the predictions stored in the observed cells. For the trajectory map approach predictions are instead generated from a local reconstruction of their map using Gaussian Process Regression. While the former allows for faster map access the latter requires less memory and fully exploits the spatial correlation in the environment, allowing predictions of seabed depth to be generated in areas that were not directly observed previously. In this case particle resampling therefore not only enforces self-consistency in overlapping sections of the map but additionally enforces self-consistency between neighboring map borders. Both approaches are validated using multibeam sonar data collected from several missions of varying scale by a variety of different Unmanned Underwater Vehicles. These trials demonstrate how the corrections provided by both approaches improve the trajectory and map when compared to dead reckoning fused with Ultra Short Baseline or Long Baseline observations. Furthermore, results are compared with a pre-existing state of the art bathymetric SLAM technique, confirming that similar results can be achieved at a fraction of the computation cost. Lastly the added capabilities of the trajectory map are validated using two different bathymetric datasets. These demonstrate how navigation and mapping corrections can still be achieved when only sparse bathymetry is available (e.g. from a four beam Doppler Velocity Log sensor) or in missions where map overlap is minimal or even non-existent

An Underwater Vehicle Navigation System Using Acoustic and Inertial Sensors

Unmanned Underwater Vehicles (UUVs) have become an essential tool for different underwater tasks. Compared with other unmanned systems, the navigation and localization for UUVs are particularly challenging due to the unavailability of Global Positioning System (GPS) signals underwater and the complexity of the unstable environment. Alternative methods such as acoustic positioning systems, Inertial Navigation Systems (INS), and the geophysical navigation approach are used for UUV navigation. Acoustic positioning systems utilize the characteristics of acoustic signals that have a lower absorption rate and a more extended propagation distance than electromagnetic signals underwater. The significant disadvantage of the INS is the “drift,” the unbounded error growth over time in the outputs. This thesis is aimed to study and test a combined UUV navigation system that fuses measurements from the INS, Doppler Velocity Log (DVL), and Short Baseline (SBL) acoustic positioning system to reduce the drift. Two Kalman filters are used to do the fusion: the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). After conducting the experiments and simulation, the results illustrated the INS/SBL fusion navigation approach was able to reduce the drift problems in the INS. Moreover, UKF showed a better performance than the EKF in the INS

Multiple-vehicle resource-constrained navigation in the deep ocean

Thesis (S.M.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 139-148).This thesis discusses sensor management methods for multiple-vehicle fleets of autonomous underwater vehicles, which will allow for more efficient and capable infrastructure in marine science, industry, and naval applications. Navigation for fleets of vehicles in the ocean presents a large challenge, as GPS is not available underwater and dead-reckoning based on inertial or bottom-lock methods can require expensive sensors and suffers from drift. Due to zero drift, acoustic navigation methods are attractive as replacements or supplements to dead-reckoning, and centralized systems such as an Ultra-Short Baseline Sonar (USBL) allow for small and economical components onboard the individual vehicles. Motivated by subsea equipment delivery, we present model-scale proof-of-concept experimental pool tests of a prototype Vertical Glider Robot (VGR), a vehicle designed for such a system. Due to fundamental physical limitations of the underwater acoustic channel, a sensor such as the USBL is limited in its ability to track multiple targets-at best a small subset of the entire fleet may be observed at once, at a low update rate. Navigation updates are thus a limited resource and must be efficiently allocated amongst the fleet in a manner that balances the exploration versus exploitation tradeoff. The multiple vehicle tracking problem is formulated in the Restless Multi-Armed Bandit structure following the approach of Whittle in [108], and we investigate in detail the Restless Bandit Kalman Filters priority index algorithm given by Le Ny et al. in [71]. We compare round-robin and greedy heuristic approaches with the Restless Bandit approach in computational experiments. For the subsea equipment delivery example of homogeneous vehicles with depth-varying parameters, a suboptimal quasi-static approximation of the index algorithm balances low landing error with safety and robustness. For infinite-horizon tracking of systems with linear time-invariant parameters, the index algorithm is optimal and provides benefits of up to 40% over the greedy heuristic for heterogeneous vehicle fleets. The index algorithm can match the performance of the greedy heuristic for short horizons, and offers the greatest improvement for long missions, when the infinite-horizon assumption is reasonably met.by Brooks Louis-Kiguchi Reed.S.M

Particle Filter based Autonomous Underwater Vehicle Navigation System aided thru acoustic communication ranging

Autonomous Underwater Vehicles (AUVs) are platforms suitable for a wide variety of applications in the marine environment with economic and operational advantages. In these applications an AUV performs a given task as a mission. During the mission execution, the AUV will move around the environment following paths that allow it to fulfill the mission's objectives. To achieve this, a reliable Navigation System (NS) is required. In addition to this, the current operating concept includes the deployment of multiple AUVs on a given area, thus a communication system between vehicles is also required. In the underwater environment both navigation and communication systems deals with the particular characteristics of the medium that limits the use of conventional techniques. In this work, a complete NS for an AUV is presented. The developed NS is based on an inertial navigation scheme with velocity and position aiding. The position aiding takes advantage of the communication system onboard the vehicle, which avoids the use of additional positioning systems. The fundamentals of the applied solutions are described and experimental results and implementation details are provided. Also conclusions and future works are presented