Abstract
The ability to employ autonomous vehicles to find and track the boundary between two different water masses can increase the efficiency in waterborne data collection, by concentrating measurements in the most relevant regions and capturing detailed spacial and temporal variations. In this paper we provide a guidance mechanism to enable an autonomous vehicle to find and track the steepest gradient of a scalar field in the horizontal plane. The main innovation in our approach is the mechanism to adapt the orientation of the crossings to the local curvature of the boundary, so that the vehicle can keep tracking the gradient regardless of its horizontal orientation. As an example, we show how the algorithms can be used to find and track the boundary of a dredged navigation channel, using only altimeter measurements.

Abstract
The continued development of mobile robots (MR) must be accompanied by an increase in robotics' safety measures. Not only must MR be capable of detecting and diagnosing faults, they should also be capable of understanding when the dangers of a mission, to themselves and the surrounding environment, warrant the abandonment of their endeavors. Analysis of fault detection and diagnosis techniques helps shed light on the challenges of the robotic field, while also showing a lack of research in autonomous decision-making tools. This paper proposes a new skill-based architecture for mobile robots, together with a novel risk assessment and decision-making model to overcome the difficulties currently felt in autonomous robot design.

Abstract
In this paper we present a novel method for the acoustic tracking of multiple Autonomous Underwater Vehicles. While the problem of tracking a single moving vehicle has been addressed in the literature, tracking multiple vehicles is a problem that has been overlooked, mostly due to the inherent difficulties on data association with traditional acoustic localization networks. The proposed approach is based on a Probability Hypothesis Density Filter, thus overcoming the data association problem. Our tracker is able not only to successfully estimate the positions of the vehicles, but also their velocities. Moreover, the tracker estimates are labelled, thus providing a way to establish track continuity of the targets. Using real word data, our method is experimentally validated and the performance of the tracker is evaluated.

Abstract
This paper describes the PISCES system, an integrated approach for fully autonomous mapping of large areas of the ocean in deep waters. A deep water AUV will use an acoustic navigation system to compute is position with bounded error. The range limitation will be overcome by a moving baseline scheme, with the acoustic sources installed in robotic surface vessels with previously combined trajectories. In order to save power, all systems will have synchronized clocks and implement the One Way Travel Time scheme. The mapping system will be a combination of an off-the-shelf MBES with a new long range bathymetry system, with a source on a moving surface vessel and the receivers on board the AUV. The system is being prepared to participate in round one of the XPRIZE challenge.

Abstract
Autonomous Underwater Vehicles are remarkable machines that revolutionized the collection of data at sea. There are many examples of highly operational man-portable vehicles for shallow waters, but there was no similar solution for deep water operations. This paper describes the development of a portable, modular, hovering AUV for deep water operations. The vehicle has little over 50kg, 2.4m of length, and a depth rating of 4000m. The first version of the vehicle has been assembled, it has gone through the initial tests in water tanks, and it is being prepared for the first operations at sea. © 2017 Marine Technology Society.

Abstract
In this article a new Data-Driven formulation of the Particle Filter framework is proposed. The new formulation is able to learn an approximate proposal distribution from previous data. By doing so, the need to explicitly model all the disturbances that might affect the system is relaxed. Such characteristics are particularly suited for Terrain Based Navigation for sensor-limited AUVs, where typical scenarios often include non-negligible sources of noise affecting the system, which are unknown and hard to model. Numerical results are presented that demonstrate the superior accuracy, robustness and efficiency of the proposed Data-Driven approach.