Area coverage and robot navigation are two important research fields within robotics. However, their intersection has received limited attention. In coverage problems, perfect navigation is often assumed, and in robot navigation, the focus is often to minimize the localization error while traveling a given trajectory. The need for integration of the two becomes clear in environments with very sparse features or landmarks, for example when an Autonomous Underwater Vehicle (AUV) is to search the seafloor for dangerous objects, such as mines. The potential consequences of missing a mine due to navigation errors can be catastrophic. If the localization error is large, a trajectory that was designed to guarantee complete coverage might have missed significant parts of the area. Thus, the coverage trajectory must be planned with the navigation performance in mind, applying a combination of using large enough planned overlaps of sensor footprints to account for the position uncertainty, and reducing this uncertainty by revisiting the known sparse landmarks. In this paper we compute trajectories that guarantee coverage for a given area under assumptions on worst case localization error growth. We further more compute upper bounds for how large areas can be covered using common coverage patterns and a single landmark, which leads to bounds on how sparse the landmarks can be in the regions to be covered.

Efficient Navigation Aware Seabed Coverage using AUVs

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.

Including Heat Balance When Designing the Energy System of Fuel Cell-Powered AUVs

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.

Online Stochastic Variational Gaussian Process Mapping for Large-Scale Bathymetric SLAM in Real Time

The interest for marine research and exploration has increased rapidly during the past decades and autonomous underwater vehicles (AUV) have been found useful in an increased amount of applications ...

Automatic Tuning of Motion Control System for an Autonomous Underwater Vehicle

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. 

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 create a well-performing and yet versatile vehicle. This paper introduces Maribot LoLo and presents the underlying design philosophy which focuses on versatility and endurance. A free-flooded hull offers modularity and modifiability while reliability and robustness are achieved through hardware redundancy and a hierarchical captain-scientist relationship in the embedded system. The vehicle is designed to be operated at moderate water depths and on long-range missions. This leads to challenges in the design of the variable buoyancy system (VBS) which also is presented. The achievable range of the AUV is evaluated with a simple hydrodynamics model based on frictional drag.

Filip Soderling

Papers

Design of an AUV Research Platform for Demonstration of Novel Technologies