Abstract
It is common the use of the sonar technology in order acquire and posteriorly control the distance of an underwater vehicle towards an obstacle. Although this solution simplifies the problem and is effective in most cases, it might carry some disadvantages in certain underwater vehicles or conditions. In this work it is presented a system capable of controlling the altitude of an underwater vehicle using computer vision. The sensor capable of computing the distance is composed of a CCD camera and 2 green pointer lasers. Regarding the control of the vehicle, the solution used was based on the switching of two controllers, a velocity controller (based on a PI controller), and a position controller (based on a PD controller). The vehicle chosen to test the developed system was a profiler, which main task is the vertical navigation. The mathematical model was obtained and used in order to validate the controllers designed using the Simulink toolbox from Matlab. It was used a Kalman filter in order to have a better estimation of the state variables (altitude, depth, and velocity). The tests relative to the sensor developed responsible for the acquisition of the altitude showed an average relative error equal to 1 % in the range from 0 to 2.5 m. The UWsim underwater simulation environment was used in order to validate the integration of the system and its performance. © 2018 IEEE.

Abstract
The maximum mission duration and range of an Autonomous Underwater Vehicle are governed by the amount of energy carried on board and the way it is spent during the mission. While an increase in battery capacity and a decrease in electronics demand yield a direct increase in vehicle range, the impact of velocity variation is not so obvious. With slower velocities, most of the energy will be spent in electronics, not in motion, while for faster velocities a lot of energy will be needed to balance drag. Flying-type AUVs have a minimum velocity for the control surfaces to be effective, reducing the range of values for optimization. Hovering type AUVs, on the other hand, are typically slower moving platforms, able to travel at arbitrarily slow velocities. This paper addresses the analysis of the power consumption of hovering type AUVs, providing guidelines and analytical expressions to compute the optimal velocity when the vehicle travels in a single direction, and also when the trajectory is a combination of horizontal and vertical motion. © 2019 IEEE.

Abstract

Abstract
The growing needs in exploring ocean resources have been pushing the length and complexity of autonomous underwater vehicle (AUV) missions, leading to more stringent energy requirements. A promising approach to reduce the energy consumption of AUVs is to use variable buoyancy systems (VBSs) as a replacement or complement to thruster action, since VBSs only require energy consumption during limited periods of time to control the vehicle's floatation. This paper presents the development of an electrohydraulic VBS to be included in an existing AUV for shallow depths of up to 100 m. The device's preliminary mechanical design is presented, and a mathematical model of the device's power consumption is developed, based on data provided by the manufacturer. Taking a standard mission profile as an example, a comparison between the energy consumed using thrusters and the designed VBS is presented and compared.

Abstract
Corners usually appear very distinct from the rest of the scene in a mechanical scanning imaging sonar (MSIS) image, generally characterized by sharp intensities. The detection of corners is particularly useful in human-structured environments such as tanks because the knowledge on their location provides a way to compute the vehicle position. The combination of some basic operations typically used for image segmentation have great potential to detect and localize corners in sonar images automatically. This article proposes and evaluates with experimental data a set of image segmentation algorithms for corner detection in sonar scans. The developed algorithms are evaluated with ground truth, and their performance is analyzed following a few relevant metrics for autonomous navigation. © 2019 Marine Technology Society.