While commercially available autonomous underwater vehicles (AUVs) are routinely used in survey missions, a new set of applications exist which clearly demand intervention capabilities: the maintenance of permanent underwater structures as well as the recovery of benthic stations or black-boxes are a few of them. These tasks are addressed nowadays using manned submersibles or work-class remotely operated vehicles (ROVs), equipped with teleoperated arms under human supervision. In the context of the TRITON Spanish funded project, a subsea panel docking and an intervention procedure are proposed. The light-weight intervention AUV (I-AUV) Girona 500 is used to autonomously dock into a subsea panel using a funnel-based docking method for passive accommodation. Once docked, an autonomous fixed-based manipulation system, which uses feedback from a digital camera, is used to turn a valve and plug/unplug a connector. The paper presents the techniques used for the autonomous docking and manipulation as well as how the adapted subsea panel has been designed to facilitate such operations.

This paper presents a solution to the 3D Range-Only beacon localization problem using a Sum of Gaussians (SOG) filter together with an Active Localization method, which is based on the minimization of the beacon position uncertainty, in order to ensure the problem observability and a fast convergence. The method is applied to autonomously locate a subsea panel and home to it in order to establish visual contact to later launch a visual servoing based docking task. The method is demonstrated through field experiments in a harbor environment with an Autonomous Underwater Vehicle (AUV) including Ultra-Short Baseline (USBL) ground truth information.

This paper describes a probabilistic surface matching method for pose-based bathymetry SLAM using a multibeam sonar profiler. The proposed algorithm compounds swath profiles of the seafloor with dead reckoning localization to build surface patches. Then, a probabilistic implementation of the ICP is used to deal with the uncertainty of the robot pose as well as the measured points in a two-stage process including point-to-point and point-to-plane metrics. A novel surface adaptation using octrees is proposed to have ICP-derived methods working in feature-poor or highly unstructured areas typical of bathymetric scenarios. Moreover, a heuristic based on the uncertainties of the surface points is used to improve the basic algorithm, decreasing the ICP complexity to O(n). The performance of the method is demonstrated with real data from a bathymetric survey.

We present a novel survey path planning technique which minimizes the robot's position uncertainty along the planned path while taking into account area coverage performance. The proposed technique especially targets bathymetric mapping applications and respects application constraints such as the desire to survey in parallel tracks and to avoid turns in the target area to maximize sonar measurements quality. While accounting for uncertainty in the survey planning process can lead to more accurate data products, existing survey planning tools typically ignore it. Our method bridges this gap using the saliency on an a priori map to predict how the terrain will affect the robot's belief at every point on the target area. Based on this magnitude, we provide an algorithm that computes the order in which to trace parallel tracks to cover the target area minimizing the overall uncertainty along the path. A particle filter keeps track of the robot's position uncertainty during the planning process and, in order to find useful loop-closures for mapping, crossing tracks that visit salient locations are added when the uncertainty surpasses a user-provided threshold. We test our method on real-world datasets collected off the coasts of Spain, Greece and Australia. We evaluate the expected robot's position uncertainty along the planned paths and assess their associated mapping performance using a bathymetric mapping algorithm. Results show that our method offers benefits over a standard lawnmower-type path both in terms of position uncertainty and map quality.