Abstract
This paper describes the results of AcousticRobot'13 - a noise measurement campaign that took place off the Portuguese Coast in May 2013, using two high endurance autonomous vehicles capable of silent operation (an underwater glider and an autonmomous sailing vessel) equipped with hydrophones, and a moored hydrophone that served as reference. We show that the autonomous vehicles used can provide useful measurements of underwater noise, and describe the main advantages and shortcomings that became evident during the campaign.

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Abstract
While autonomous underwater vehicles (AUVs) are increasingly being used to perform mine countermeasures (MCM) operations, the capability of these systems is limited by the efficiency of the planning process. In this paper we study the problem of multiobjective MCM mission planning with an AUV. In order to overcome the inherent complexity of the problem, a multi-stage algorithm is proposed and evaluated. Our algorithm combines an evolutionary algorithm (EA) with a local search procedure based on simulated annealing (SA), aiming at a more flexible and effective exploration and exploitation of the search space. An artificial neural network (ANN) model was also integrated in the evolutionary procedure to guide the search. The results show that the proposed strategy can efficiently identify a higher quality solution set and solve the mission planning problem.

Abstract
Traditional coverage path planners create lawnmower-type paths in the operating area completely ignoring the uncertainty in the vehicle's position. However, in the presence of significant uncertainty in localization estimates, one can no longer guarantee that the vehicle will cover all the area according to plan. Aiming to bridge this gap, we present a coverage path planning technique for search operations which takes into account the vehicle's position and detection performance uncertainties and tries to minimize this uncertainty along the planned path. The objective is to plan paths, using a localization error model as input, to reduce as much uncertainty as possible and to minimize the extra path length (swath overlap) while satisfying mission feasibility constraints. We introduce an algorithm that calculates what will be the best moments for bringing the vehicle to surface to ensure a bounded position error. We also consider time and energy constraints that may influence the planned trajectory as path overlap is increased to account for uncertainty. Additionally we challenge the assumption frequently seen in coverage algorithms where two observations of the same target are considered independent. © 2017 IEEE.

Abstract
This paper describes an integrated application that automates the procedure for sea outfall discharges data acquisition with an Autonomous Underwater Vehicle (AUV). Since most applications for this type of technology are research related, the used software tends to be more technical, oriented for engineers. This fact, allied with the bad sea conditions usually encountered at the portuguese coast, cause the mission execution to be extremely difficult at times. Before starting operating the AUV, a wide range of operations must be completed: we need to get data to estimate plume position, calculate mission path, transfer the AUV and acoustic buoys to the water, test communications and configure a variety of systems. So clearly there is a need to develop an application that fully automates a monitoring mission, allowing the operator with little to no experience to conclude it efficiently. Ultimately, by automating the procedure, there is the possibility of expanding the use of AUV's across several fields of study since no prior knowledge about the its systems is required. In summary this guides the user through a series of tasks and provides visual and audio information.

Abstract
This paper describes an integrated application that performs a geostatistical analysis of data acquired by an AUV in monitoring missions to sewage outfalls. This comes as an effort for automating the procedures of a monitoring campaign from data acquisition to data processing. This application is based on the R statistical software and uses the Gstat package for the geostatistical prediction. R is a console based application that uses software packages developed by the community. The application interfaces with R guiding the user through several steps that perform the geostatistical analysis. It was not our intention to cover all geostatistical procedures but only the ones that are needed for the data processing concerned. The major advantage of this application is that the user does not need to be familiar with methods and data structures associated with the base software, allowing the processing and analysis to be more simple, fast and efficient which is particularly important for routine monitoring. This software application also enables us to give a quicker response in case of contamination to near-by beaches.