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

Typhoon at CommsNet13: Experimental experience on AUV navigation and localization

This paper presents two acoustic-based techniques for Autonomous Underwater Vehicle (AUV) navigation within an underwater network of fixed sensors. The proposed algorithms exploit the positioning measurements provided by an Ultra-Short Base Line (USBL) transducer on-board the vehicle to aid the navigation task. In the considered framework the acoustic measurements are embedded in the communication network scheme, causing time-varying delays in ranging with the fixed nodes. The results presented are obtained with post-processing elaborations of the raw experimental data collected during the CommsNet13 campaign, organized and scientifically led by the NATO Science and Technology Organization Centre for Maritime Research and Experimentation (CMRE). The experiment involved several research institutions and included among its objectives the evaluation of on-board acoustic USBL systems for navigation and localization of AUVs. The ISME groups of the Universities of Florence and Pisa jointly participated to the experiment with one Typhoon class vehicle. This is a 300 m depth rated AUV with acoustic communication capabilities originally developed by the two groups for archaeological search in the framework of the THESAURUS project. The CommsNet13 Typhoon, equipped with an acoustic modem/USBL head, navigated within the fixed nodes acoustic network deployed by CMRE. This allows the comparison between inertial navigation, acoustic self-localization and ground truth represented by GPS signals (when the vehicle was at the surface)

Cooperative AUV Navigation using a Single Maneuvering Surface Craft

In this paper we describe the experimental implementation of an online algorithm for cooperative localization of submerged autonomous underwater vehicles (AUVs) supported by an autonomous surface craft. Maintaining accurate localization of an AUV is difficult because electronic signals, such as GPS, are highly attenuated by water. The usual solution to the problem is to utilize expensive navigation sensors to slow the rate of dead-reckoning divergence. We investigate an alternative approach that utilizes the position information of a surface vehicle to bound the error and uncertainty of the on-board position estimates of a low-cost AUV. This approach uses the Woods Hole Oceanographic Institution (WHOI) acoustic modem to exchange vehicle location estimates while simultaneously estimating inter-vehicle range. A study of the system observability is presented so as to motivate both the choice of filtering approach and surface vehicle path planning. The first contribution of this paper is to the presentation of an experiment in which an extended Kalman filter (EKF) implementation of the concept ran online on-board an OceanServer Iver2 AUV while supported by an autonomous surface vehicle moving adaptively. The second contribution of this paper is to provide a quantitative performance comparison of three estimators: particle filtering (PF), non-linear least-squares optimization (NLS), and the EKF for a mission using three autonomous surface craft (two operating in the AUV role). Our results indicate that the PF and NLS estimators outperform the EKF, with NLS providing the best performance.United States. Office of Naval Research (Grant N000140711102)United States. Office of Naval Research. Multidisciplinary University Research InitiativeSingapore. National Research FoundationSingapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Monitorin

Dynamic positioning of beacon vehicles for cooperative underwater navigation

Autonomous Underwater Vehicles (AUVs) are used for an ever increasing range of applications due to the maturing of the technology. Due to the absence of the GPS signal underwater, the correct estimation of its position is a challenge for submerged vehicles. One promising strategy to mitigate this problem is to use a group of AUVs where one or more assume the role of a beacon vehicle which has a very accurate position estimate due to an expensive navigation suite or frequent surfacings. These beacon vehicles broadcast their position and the remaining survey vehicles can use this position information and intra-vehicle ranges to update their position estimate. The effectiveness of this approach strongly depends on the geometry between the beacon vehicles and the survey vehicles. The trajectories of the beacon vehicles should thus be planned with the goal to minimize the position uncertainty of the survey vehicles. We propose a distributed algorithm which dynamically computes the locally optimal position for a beacon vehicle using only information obtained from broadcast communication of the survey vehicles. It does not need prior information about the survey vehicles' trajectory and can be used for any group size of beacon and survey vehicles.United States. Office of Naval Research (Grant N00014-97-1-0202)United States. Office of Naval Research (Grant N00014-05-1-0255)United States. Office of Naval Research (Grant N00014-02-C- 0210)United States. Office of Naval Research (Grant N00014-07-1-1102

An Acoustic Network Navigation System

This work describes a system for acoustic‐based navigation that relies on the addition of localization services to underwater networks. The localization capability has been added on top of an existing network, without imposing constraints on its structure/operation. The approach is based on the inclusion of timing information within acoustic messages through which it is possible to know the time of an acoustic transmission in relation to its reception. Exploiting such information at the network application level makes it possible to create an interrogation scheme similar to that of a long baseline. The advantage is that the nodes/autonomous underwater vehicles (AUVs) themselves become the transponders of a network baseline, and hence there is no need for dedicated instrumentation. The paper reports at sea results obtained from the COLLAB–NGAS14 experimental campaign. During the sea trial, the approach was implemented within an operational network in different configurations to support the navigation of the two Centre for Maritime Research and Experimentation Ocean Explorer (CMRE OEX) vehicles. The obtained results demonstrate that it is possible to support AUV navigation without constraining the network design and with a minimum communication overhead. Alternative solutions (e.g., synchronized clocks or two‐way‐travel‐time interrogations) might provide higher precision or accuracy, but they come at the cost of impacting on the network design and/or on the interrogation strategies. Results are discussed, and the performance achieved at sea demonstrates the viability to use the system in real, large‐scale operations involving multiple AUVs. These results represent a step toward location‐aware underwater networks that are able to provide node localization as a service

Optimal path shape for range-only underwater target localization using a Wave Glider

Underwater localization using acoustic signals is one of the main components in a navigation system for an autonomous underwater vehicle (AUV) as a more accurate alternative to dead-reckoning techniques. Although different methods based on the idea of multiple beacons have been studied, other approaches use only one beacon, which reduces the system’s costs and deployment complexity. The inverse approach for single-beacon navigation is to use this method for target localization by an underwater or surface vehicle. In this paper, a method of range-only target localization using a Wave Glider is presented, for which simulations and sea tests have been conducted to determine optimal parameters to minimize acoustic energy use and search time, and to maximize location accuracy and precision. Finally, a field mission is presented, where a Benthic Rover (an autonomous seafloor vehicle) is localized and tracked using minimal human intervention. This mission shows, as an example, the power of using autonomous vehicles in collaboration for oceanographic research.Peer ReviewedPostprint (author's final draft

Decentralized cooperative trajectory estimation for autonomous underwater vehicles

Autonomous agents that can communicate and make relative measurements of each other can improve their collective localization accuracies. This is referred to as cooperative localization (CL). Autonomous underwater vehicle (AUV) CL is constrained by the low throughput, high latency, and unreliability of of the acoustic channel used to communicate when submerged. Here we propose a CL algorithm specifically designed for full trajectory, or maximum a posteriori, estimation for AUVs. The method is exact and has the advantage that the broadcast packet sizes increase only linearly with the number of AUVs in the collective and do not grow at all in the case of packet loss. The approach allows for AUV missions to be achieved more efficiently since: 1) vehicles waste less time surfacing for GPS fixes, and 2) payload data is more accurately localized through the smoothing approach.Natural Sciences and Engineering Research Council of CanadaDefense Research and Development CanadaUnited States. Office of Naval Research (Grant N00014-13-1-0588

A Fuzzy Cooperative Localisation Framework for Underwater Robotic Swarms

This article proposes a holistic localisation framework for underwater robotic swarms to dynamically fuse multiple position estimates of an autonomous underwater vehicle while using fuzzy decision support system. A number of underwater localisation methods have been proposed in the literature for wireless sensor networks. The proposed navigation framework harnesses the established localisation methods in order to provide navigation aids in the absence of acoustic exteroceptive sensors navigation aid (i.e., ultra-short base line) and it can be extended to accommodate newly developed localisation methods by expanding the fuzzy rule base. Simplicity, flexibility, and scalability are the main three advantages that are inherent in the proposed localisation framework when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. A physics-based simulation platform that considers environment’s hydrodynamics, industrial grade inertial measurement unit, and underwater acoustic communications characteristics is implemented in order to validate the proposed localisation framework on a swarm size of 150 autonomous underwater vehicles. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation by 16.53% and 35.17%, respectively, when compared to the Extended Kalman Filter based localisation with round-robin scheduling

Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

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.Massachusetts Institute of Technology. Department of Mechanical EngineeringSingapore-MIT Alliance for Research and Technology (SMART) (Graduate Fellowship