Abstract
In opposition to the surface, no common solution is available for localization of active objects underwater. Typical solutions use acoustics as a means to implicitly measure ranges or angles and consequently determine the position of a transmitter. If the receivers are synchronized among themselves, the position of the transmitter can be estimated based on the time-of-arrivals (TOA). The confidence on the estimate varies with respect to the relative positions of the receivers and the transmitter. In this paper, we present recent developments for optimal 3D positioning of TOA sensors based on the a metric that uses the Fisher information matrix. We give the necessary conditions to obtain the best possible estimate. To our best knowledge, no analytical solution has been yet presented for this problem. We complete and validate our study with a simulation of optimal positioning of four TOA sensors.

Abstract

Abstract
In this paper we present a set of field tests for detection of human in the water with an unmanned surface vehicle using infrared and color cameras. These experiments aimed to contribute in the development of victim target tracking and obstacle avoidance for unmanned surface vehicles operating in marine search and rescue missions. This research is integrated in the work conducted in the European FP7 research project Icarus aiming to develop robotic tools for large scale rescue operations. The tests consisted in the use of the ROAZ unmanned surface vehicle equipped with a precision GPS system for localization and both visible spectrum and IR cameras to detect the target. In the experimental setup, the test human target was deployed in the water wearing a life vest and a diver suit (thus having lower temperature signature in the body except hands and head) and was equipped with a GPS logger. Multiple target approaches were performed in order to test the system with different sun incidence relative angles. The experimental setup, detection method and preliminary results from the field trials performed in the summer of 2013 in Sesimbra, Portugal and in La Spezia, Italy are also presented in this work.

Abstract
Different Terrain Based Navigation systems for underwater vehicles have already been presented, with experimentally validated results and consistent performance. However, these results are mostly based on the use of both high accuracy inertial navigation systems and high quality sonars. This article presents a study on Particle Filter algorithms that cope with peculiarities of Terrain Based Navigation for sensor limited systems. The focus is on the influence on several parameters, namely the process noise, the measurement noise and the number of the particles, and how these can improve the obtained results. Based on the results obtained by simulation, we present some conclusions relevant for the design of future implementation of the algorithms.

Abstract
This paper proposes a new, fast and computationally light weight methodology to pinpoint a robot in a structured scenario. The localisation algorithm performs a tracking routine to pinpoint the robot's pose as it moves in a known map, without the need for preparing the environment, with artificial landmarks or beacons. To perform such tracking routine, it is necessary to know the initial position of the vehicle. This paper describes the tracking routine and presents a solution to pinpoint that initial position in an autonomous way, using a multi-hypotheses strategy. This paper presents experimental results on the performance of the proposed method applied in two different scenarios: (1) in the Middle Size Soccer Robotic League (MSL), using artificial vision data from an omnidirectional robot and (2) in indoor environments using 3D data from a tilting Laser Range Finder of a differential drive robot (called RobVigil). This paper presents results comparing the proposed methodology and an Industrial Positioning System (the Sick NAV350), commonly used to locate Autonomous Guided Vehicles (AGVs) with a high degree of accuracy in industrial environments.

Abstract
This paper presents an approach to stereovision applied to small water vehicles. By using a small low-cost computer and inexpensive off-the-shelf components, we were able to develop an autonomous driving system capable of following other vehicle and moving along paths delimited by coloured buoys. A pair of webcams was used and, with an ultrasound sensor, we were also able to implement a basic frontal obstacle avoidance system. With the help of the stereoscopic system, we inferred the position of specific objects that serve as references to the ASV guidance. The final system is capable of identifying and following targets in a distance of over 5 meters. This system was integrated with the framework already existent and shared by all the vehicles used in the OceanSys research group at INESC - DEEC/FEUP.