Synchronous-clock range-angle relative acoustic navigation: a unified approach to multi-AUV localization, command, control, and coordination

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rypkema, N., Schmidt, H., & Fischell, E. Synchronous-clock range-angle relative acoustic navigation: a unified approach to multi-AUV localization, command, control, and coordination. Journal of Field Robotics, 2(1), (2022): 774–806, https://doi.org/10.55417/fr.2022026.This paper presents a scalable acoustic navigation approach for the unified command, control, and coordination of multiple autonomous underwater vehicles (AUVs). Existing multi-AUV operations typically achieve coordination manually by programming individual vehicles on the surface via radio communications, which becomes impractical with large vehicle numbers; or they require bi-directional intervehicle acoustic communications to achieve limited coordination when submerged, with limited scalability due to the physical properties of the acoustic channel. Our approach utilizes a single, periodically broadcasting beacon acting as a navigation reference for the group of AUVs, each of which carries a chip-scale atomic clock and fixed ultrashort baseline array of acoustic receivers. One-way travel-time from synchronized clocks and time-delays between signals received by each array element allow any number of vehicles within receive distance to determine range, angle, and thus determine their relative position to the beacon. The operator can command different vehicle behaviors by selecting between broadcast signals from a predetermined set, while coordination between AUVs is achieved without intervehicle communication by defining individual vehicle behaviors within the context of the group. Vehicle behaviors are designed within a beacon-centric moving frame of reference, allowing the operator to control the absolute position of the AUV group by repositioning the navigation beacon to survey the area of interest. Multiple deployments with a fleet of three miniature, low-cost SandShark AUVs performing closed-loop acoustic navigation in real-time provide experimental results validated against a secondary long-baseline positioning system, demonstrating the capabilities and robustness of our approach with real-world data.This work was partially supported by the Office of Naval Research, the Defense Advanced Research Projects Agency, Lincoln Laboratory, and the Reuben F. and Elizabeth B. Richards Endowed Funds at WHOI

Underwater & out of sight: towards ubiquity in underwater robotics

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 September 2019.The Earth's oceans holds a wealth of information currently hidden from us. Effective measurement of its properties could provide a better understanding of our changing climate and insights into the creatures that inhabit its waters. Autonomous underwater vehicles (AUVs) hold the promise of penetrating the ocean environment and uncovering its mysteries; and progress in underwater robotics research over the past three decades has resulted in vehicles that can navigate reliably and operate consistently, providing oceanographers with an additional tool for studying the ocean. Unfortunately, the high cost of these vehicles has stifled the democratization of this technology. We believe that this is a consequence of two factors. Firstly, reliable navigation on conventional AUVs has been achieved through the use of a sophisticated sensor system, namely the Doppler velocity log (DVL)-aided inertial navigation system (INS), which drives up vehicle cost, power use and size. Secondly, deployment of these vehicles is expensive and unwieldy due to their complexity, size and cost, resulting in the need for specialized personnel for vehicle operation and maintenance. The recent development of simpler, low-cost, miniature underwater robots provides a solution that mitigates both these factors; however, removing the expensive DVL-aided INS means that they perform poorly in terms of navigation accuracy. We address this by introducing a novel acoustic system that enables AUV self-localization without requiring a DVL-aided INS or on-board active acoustic transmitters. We term this approach Passive Inverted Ultra-Short Baseline (piUSBL) positioning. The system uses a single acoustic beacon and a time-synchronized, vehicle-mounted, passive receiver array to localize the vehicle relative to this beacon. Our approach has two unique advantages: first, a single beacon lowers cost and enables easy deployment; second, a passive receiver allows the vehicle to be low-power, low-cost and small, and enables multi-vehicle scalability. Providing this new generation of small and inexpensive vehicles with accurate navigation can potentially lower the cost of entry into underwater robotics research and further its widespread use for ocean science. We hope that these contributions in low-cost underwater navigation will enable the ubiquitous and coordinated use of robots to explore and understand the underwater domain.This research was funded and supported by a number of sponsors; we gratefully acknowledge them below. Defense Advanced Research Projects Agency (DARPA) and SSC Pacific via Applied Physical Sciences Corp. (APS) under contract number N66001-11-C-4115. SSC Pacific via Applied Physical Sciences Corp. (APS) under award number N66001-14-C-4031. Air Force via Lincoln Laboratory under award number FA8721-05-C-0002. Office of Naval Research (ONR) via University of California-San Diego under award number N00014-13-1-0632. Defense Advanced Research Projects Agency (DARPA) via Applied Physical Sciences Corp. (APS) under award number HR0011-18-C-0008. Office of Naval Research (ONR) under award number N00014-17-1-2474

Probablistic approaches for intelligent AUV localisation

This thesis studies the problem of intelligent localisation for an autonomous underwater vehicle (AUV). After an introduction about robot localisation and specific issues in the underwater domain, the thesis will focus on passive techniques for AUV localisation, highlighting experimental results and comparison among different techniques. Then, it will develop active techniques, which require intelligent decisions about the steps to undertake in order for the AUV to localise itself. The undertaken methodology consisted in three stages: theoretical analysis of the problem, tests with a simulation environment, integration in the robot architecture and field trials. The conclusions highlight applications and scenarios where the developed techniques have been successfully used or can be potentially used to enhance the results given by current techniques. The main contribution of this thesis is in the proposal of an active localisation module, which is able to determine the best set of action to be executed, in order to maximise the localisation results, in terms of time and efficiency

Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.

This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture. The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks. In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd

Cooperative localization for autonomous underwater vehicles

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 2009Self-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.This work was funded by Office of Naval Research grants N00014-97-1-0202, N00014-05-1-0255, N00014-02-C-0210, N00014-07-1-1102 and the ASAP MURI program led by Naomi Leonard of Princeton University

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

AUV planning and calibration method considering concealment in uncertain environments

IntroductionAutonomous underwater vehicles (AUVs) are required to thoroughly scan designated areas during underwater missions. They typically follow a zig-zag trajectory to achieve full coverage. However, effective coverage can be challenging in complex environments due to the accumulation and drift of navigation errors. Possible solutions include surfacing for satellite positioning or underwater acoustic positioning using transponders on other vehicles. Nevertheless, surfacing or active acoustics can compromise stealth during reconnaissance missions in hostile areas by revealing the vehicle’s location.MethodsWe propose calibration and planning strategies based on error models and acoustic positioning to address this challenge. Acoustic markers are deployed via surface ships to minimize navigation errors while maintaining stealth. And a new path planning method using a traceless Kalman filter and acoustic localization is proposed to achieve full-area coverage of AUVs. By analyzing the statistics of accumulated sensor errors, we optimize the positions of acoustic markers to communicate with AUVs and achieve better coverage. AUV trajectory concealment is achieved during detection by randomizing the USV navigation trajectory and irregularizing the locations of acoustic marker.ResultsThe proposed method enables the cumulative determination of the absolute position of a target with low localization error in a side-scan sonar-based search task. Simulations based on large-scale maps demonstrate the effectiveness and robustness of the proposed algorithm.DiscussionSolving the problem of accumulating underwater localization errors based on inertial navigation by error modeling and acoustic calibration is a typical way. In this paper, we have implemented a method to solve the localization error in a search scenario where stealth is considered

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