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Maribot LoLo is an autonomous underwater vehicle (AUV) developed at the KTH Centre for Naval Architecture as part of the Swedish Maritime Robotics Centre (SMaRC). The center’s cross-disciplinary activities require an AUV research platform that can be used for data collection and to test and demonstrate novel technologies. The challenge herein is to...
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... platform that can be used by scientists from various fields for data collection and for testing and demonstration of novel technologies. This has led to the design of the AUV Maribot LoLo which is developed at the KTH Centre for Naval Architecture as the latest addition to its fleet of maritime robots (Maribot) and as an important asset to SMaRC. Fig. 1 shows the AUV during field ...
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... propellers and low-power electric motors, the AUV was able to maintain a course using differential thrust for control. These initial tests revealed minor problems with the RC link that eventually have been identified and solved later on. Further surface tests were performed in October 2018, including autonomous navigation between waypoints (Fig. 1). The DVL has been mounted onto the vehicle but not yet incorporated into the navigation system. There were some minor issues with the AHRS that caused unexpected behavior; these issues are currently being resolved. Before implementation on the AUV the VBS will be tested extensively as a stand-alone system for extended periods of time. ...
Citations
... The underlying bathymetry E from which the simulated MBES are generated was collected off the coast of Sweden by the industrial surveying company Ocean Infinity. The AUV has been parameterized following the hardware constraints of the real AUV Lolo [30], with a constant surveying velocity of 0.8 m/s and a minimum turning radius of 10m. The AUV is equipped with a simulated MBES with an opening angle of 90°, a ping rate of 20Hz and 64 beams per ping. ...
Informative path planning (IPP) applied to bathymetric mapping allows AUVs to focus on feature-rich areas to quickly reduce uncertainty and increase mapping efficiency. Existing methods based on Bayesian optimization (BO) over Gaussian Process (GP) maps work well on small scenarios but they are short-sighted and computationally heavy when mapping larger areas, hindering deployment in real applications. To overcome this, we present a 2-layered BO IPP method that performs non-myopic, real-time planning in a tree search fashion over large Stochastic Variational GP maps, while respecting the AUV motion constraints and accounting for localization uncertainty. Our framework outperforms the standard industrial lawn-mowing pattern and a myopic baseline in a set of hardware in the loop (HIL) experiments in an embedded platform over real bathymetry.
... A Kongsberg Hugin 3000 equipped with a MBES Kongsberg 2040 has collected mission 1 without any external navigation aid. Surveys 2 and 3 were executed by the AUV Lolo [22], with a R2Sonic MBES. While Hugin is a commercially available vehicle with high-end specifications, Lolo is a research platform developed at KTH university and as such it has been a suitable vehicle to test our framework live in mission 2 in its payload computer, an NVIDIA Jetson Orin. ...
Rao-Blackwellized particle filter (RBPF) SLAM solutions with Gaussian Process (GP) maps can both maintain multiple hypotheses of a vehicle pose estimate and perform implicit data association for loop closure detection in continuous terrain representations. Both qualities are of particular interest for SLAM with autonomous underwater vehicles (AUVs) in the open sea, where distinguishable features are scarce. However, the applicability of GP regression to parallel, real-time mapping in an RBPF framework remains limited by the size of the area to survey and the computational cost of the GP training. To overcome these constraints, in this letter we propose the adaption of Stochastic Variational GP (SVGP) regression to online mapping in combination with a novel, efficient particle trajectory storing in the RBPF. We show how the resulting RBPF-SVGP framework can achieve real-time performance in an embedded platform on two AUV surveys containing millions of points. We further test the framework on a live mission on an AUV and we make the implementation publicly available.
... A Kongsberg Hugin 3000 equipped with a MBES Kongsberg 2040 has collected mission 1 without any external navigation aid. Surveys 2 and 3 were executed by the AUV Lolo [22], with a R2Sonic MBES. While Hugin is a commercially available vehicle with high-end specifications, Lolo is a research platform under development at KTH university and as such it has been a suitable vehicle to test our framework live in mission 2 in its payload computer, an NVIDIA Jetson Orin. ...
Autonomous underwater vehicles (AUVs) are becoming standard tools for underwater exploration and seabed mapping in both scientific and industrial applications \cite{graham2022rapid, stenius2022system}. Their capacity to dive untethered allows them to reach areas inaccessible to surface vessels and to collect data more closely to the seafloor, regardless of the water depth. However, their navigation autonomy remains bounded by the accuracy of their dead reckoning (DR) estimate of their global position, severely limited in the absence of a priori maps of the area and GPS signal. Global localization systems equivalent to the later exists for the underwater domain, such as LBL or USBL. However they involve expensive external infrastructure and their reliability decreases with the distance to the AUV, making them unsuitable for deep sea surveys.
... AUV LoLo is a mid-size, flooded-hull type AUV designed as a demonstrator platform for novel technologies [58]. The AUV is divided into three sections: fore (payload), aft (propulsion and control), and center. ...
... The Swedish Maritime Robotics Centre (SMaRC) aims to develop fuel cell-powered AUVs capable of performing long-range missions in unknown water environments. The studied AUV is named LoLo (long-range, long-endurance), and has been under development as a demonstrator platform for the SMaRC project [48]. In Figure 2, the general shape of the vehicle is presented. ...
Fuel cell-powered Autonomous Underwater Vehicles (AUVs) represent a growing area of research as fuel cells can increase their endurance. Fuel cells consume hydrogen and oxygen to generate electricity. Typically, the fuel cell generates as much heat as electrical energy, and heat management becomes a crucial parameter when designing AUVs. For underwater applications, there is a need to store both gases and several types of storage units with different characteristics exist which have impacts on the energy density and heat behavior. This study aims at including the heat properties of the storage units in the design process of fuel cell-powered AUVs. A heat balance over the energy system of an AUV is calculated for each combination of hydrogen and oxygen storage units. In addition, a multi-criteria decision-making analysis is conducted, considering the calculated total heat, the specific energy, the energy density and the volumetric mass of each combination of storage units as criteria, enabling a comparison and ranking them using two objective criteria weighting methods. Results show that the fuel cell is the major contributor to the heat balance, and that the combinations of liquid oxygen with liquid or compressed hydrogen can be relevant and suitable for underwater applications.
The design of autonomous underwater vehicles (AUVs) and their docking stations has been a popular research topic for several decades. Although many AUV and dock designs have been proposed, materialized, and commercialized, most of these existing designs prioritize the functionality of the AUV over the dock, or vise versa; there has been limited formal research in analytical optimization for AUV docking systems. In this paper, a multidisciplinary optimization framework is presented with the aim to fill this theoretical gap. We propose a co-design optimization method that optimizes multiple design parameters governing the archetype of an AUV and its docking system. Capturing the user design intents in the optimization process, the proposed method produces a set of optimal design parameters that satisfies a set of predefined bounds, constraints, and initial conditions. Three cases of design optimization are reported for different design intents. Each optimal design found in the three cases is compared to an existing system to show the validity of this design optimization framework.
Introduction: The continuous improvement of autonomous underwater vehicles, the complexity of their systems and the use of a hybrid energy supply system have led to the need of developing a control system using multi-agent technology. To date, a large number of styles of multi-agent architectures have been formed, mainly in the field of organizing the manufacture and developing software. It is important to choose the most suitable architecture style for a multi-agent control system of an autonomous underwater vehicle with a hybrid energy supply system, taking into account its features. Purpose: The development of a method for choosing the most suitable style of a multi-agent architecture among a variety of alternative options. Method: The developed method is based on comparative assessment of various architecture styles according to non-functional requirements. For this purpose, a target graph is specially developed, taking into account the features of the device to be designed. In addition, when generating the final result, the label distribution algorithm was used as the most suitable one for this problem. Results: The proposed method of choosing the architecture style includes the following components: developing indicators by which it is advisable to compare the alternative options; forming various styles of architectures most suitable for the device under construction; analyzing the positive and negative effects of the architecture style according to non-functional requirements; formalizing these influences in the form of qualitative or quantitative labels; obtaining the final grade by applying the label distribution algorithm. Practical relevance: The proposed method allows you to select the most appropriate architecture for a multi-agent control system of an autonomous underwater vehicle. The method can also be used for a wider range of ground-based and air-based robotic systems.
