Abstract
This paper describes the full development process of TriMARES, a hybrid AUV/ROV designed to fulfil the requirements of a consortium for the inspection and periodic monitoring of a large dam reservoir. The demand of robotic systems for underwater operations is growing exponentially and there are many scenarios for which the commercial solutions are not adequate. Such was the case with TriMARES, where it was possible to take advantage of previous designs to achieve a custom solution in a short time. We describe the initial requirements for the underwater system, we present the main solutions adopted for the vehicle subsystems, and we provide some data from the first in-water tests, performed only 6 months after the beginning of the project.

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.

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
Ocean sewage outfalls are major sources of contaminants to coastal ocean ecosystems. This method of disposal has advantages in terms of economy and relative societal impact, but it also raises important concerns about public health and ecosystem preservation. Autonomous Underwater Vehicles have already been shown to be very useful for monitoring routine of ocean outfalls. The major advantage of this technology over traditional methods is the ability to collect high-resolution data which can be very valuable for environmental impact assessment and comparison with plume prediction models. Once the data has been collected in the field it is necessary to extrapolate from monitoring samples to unsampled locations. Geostatistics has been successfully used to obtain information, for example, regarding the spatial distribution of soil properties. In this work geostatistics is used to model and map the spatial distribution of temperature and salinity measurements gathered by MARES AUV in a monitoring campaign to Foz do Arelho outfall, with the aim of distinguishing the effluent plume from the receiving waters and characterizing its spatial variability in the vicinity of the discharge. The results demonstrate that this methodology provides good estimates of the dispersion of effluent and it is therefore very valuable in assessing the environmental impact and managing sea outfalls.

Abstract
Several monitoring approaches have been used to understand the physical, chemical, and biological processes associated with coastal sewage discharges. However, these efforts have not improved the understanding of the interaction of effluent plume/coastal ocean processes. Autonomous underwater vehicles (AUVs) have already been shown to be very useful for performing high-resolution surveys of small features such as outfall plumes. Some of the advantages of these platforms include easier field logistics, low cost per deployment, good spatial coverage, sampling over repeated sections, and the ability to perform feature based or adaptive sampling. Once the data have been collected in the field, it is necessary to extrapolate from monitoring samples to unsampled locations. Geostatistics has been successfully used to obtain information; for example, regarding the spatial distribution of soil properties. Besides giving estimated values at unsampled locations, it provides a measure of the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. In this work, geostatistics is used to model and map the spatial distribution of temperature measurements gathered by an AUV in a sea ouffall monitoring campaign, with the aim of distinguishing the effluent plume from the receiving waters and characterizing its spatial variability in the vicinity of the discharge. The results demonstrate that this methodology can provide good estimates of the dispersion of effluent, and it is therefore very valuable in assessing the environmental impact and managing sea outfalls.

Abstract