Abstract
Autonomous underwater vehicles (AUVs) are becoming increasingly ubiquitous due to the growing needs in exploring Ocean resources. One of the most challenging tasks in this domain relates to the energy these vehicles require, given the increase in the number of scientific payloads and on the mission complexity. One way to potentially reduce the amount of energy consumed during vertical motion is to replace or complement the thruster action with a controlled change of the vehicles floatation, using a variable buoyancy system (VBS). This paper presents the development of an electromechanical VBS for shallow depths, up to 100 m, to be included in an existing AUV. A preliminary mechanical design is presented, along with a mathematical model allowing the calculation of the energy spent by this device, based on the components manufacturers' data. A comparison between the energy consumption using thrusters and the designed VBS is presented. © 2019 IEEE.

Abstract
The lack of penetration of light and electromagnetic radiation beyond a few meters in the ocean makes acoustics the technique of choice for data transmission, target detection and ocean sensing in general. Acoustic transducers are typically based on piezoelectric materials due to the good response at high frequencies. Depending on the application it can be built using ceramics, polymers and composite materials. In the hydrostatic mode PZT ceramics hydrophones have low performance due to the low hydrostatic piezoelectric stress value. On the other hand, PVDF have shown relatively high hydrostatic mode response. This work presents the development of a PVDF hydrophone for deep sea applications. The hydrophone was subjected to a pressure test up to 25 MPa to evaluate the response variation under high hydrostatic pressure. The results show an increase up to 6 dB sensitivity under 15 MPa pressure.

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
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.

Abstract
Purpose This paper aims to present a mosaicking method for underwater robotic applications, whose result can be provided to other perceptual systems for scene understanding such as real-time object recognition. Design/methodology/approach This method is called robust and large-scale mosaicking (ROLAMOS) and presents an efficient frame-to-frame motion estimation with outlier removal and consistency checking that maps large visual areas in high resolution. The visual mosaic of the sea-floor is created on-the-fly by a robust registration procedure that composes monocular observations and manages the computational resources. Moreover, the registration process of ROLAMOS aligns the observation to the existing mosaic. Findings A comprehensive set of experiments compares the performance of ROLAMOS to other similar approaches, using both data sets (publicly available) and live data obtained by a ROV operating in real scenes. The results demonstrate that ROLAMOS is adequate for mapping of sea-floor scenarios as it provides accurate information from the seabed, which is of extreme importance for autonomous robots surveying the environment that does not rely on specialized computers. Originality/value The ROLAMOS is suitable for robotic applications that require an online, robust and effective technique to reconstruct the underwater environment from only visual information.

Abstract
Autonomous Surface Vehicles (ASVs) provide the ideal platform to further explore the many opportunities in the cargo shipping industry, by making it more profitable and safer. This paper presents an architecture for the autonomous docking operation, formed by two stages: a maneuver module and, a situational awareness system to detect a mooring facility where an ASV can safely dock. Information retrieved from a 3D LIDAR, IMU and GPS are combined to extract the geometric features of the floating platform and to estimate the relative positioning and orientation of the moor to the ASV. Then, the maneuver module plans a trajectory to a specific position and guarantees that the ASV will not collide with the mooring facility. The approach presented in this paper was validated in distinct environmental and weather conditions such as tidal waves and wind. The results demonstrate the ability of the proposed architecture for detecting the docking platform and safely conduct the navigation towards it, achieving errors up to 0.107 m in position and 6.58 degrees in orientation.