Abstract
In this paper, we present an approach to control an autonomous underwater vehicle in the vertical and the horizontal planes while pitching down or up (theta = +/-pi/2). Such a capability is explored in MARES, a small-sized, torpedo-shaped hovering AUV with four degrees of freedom. Despite the fact that roll angle is not controllable, we find a guidance law that makes the vehicle reach any point in the horizontal plane while maintaining the vehicle in the vertical position.

Abstract
It is becoming more and more common to use Autonomous Underwater Vehicles to perform tasks underwater. The use of this vehicles is affordable and its use doesn't raise any significant risk nor does it requires any human intervention. The traditional applications for the use of such vehicles were related with bathymetric tasks. But nowadays AUVs are being more and more used for variety of missions in open water environments, including the inspection of underwater structures and environmental monitoring in diverse oceanographic expeditions. Following some previous work, this paper addresses the problem of bottom following by an Autonomous Underwater Vehicle in an environment which is not previously known. In particular, the focus is on integrating a reactive behaviour based on environment sensing, with the on-board navigation software of the MARES AUV. For this, a guidance algorithm will provide the necessary pitch and depth references to the control layer of the vehicle. While the altitude towards the seabed can be measured with an altimeter, the pitch reference values are based on realtime estimation of the slope of the seabed. By doing so, it is possible to control the vehicle in a way that it will always maintain a constant attitude towards the bottom, and the trajectory followed will remain parallel to bottom, regardless of it's profile. © 2012 IEEE.

Abstract
The inspiration for this paper comes from a successful experiment conducted with students in the "Mobile Robots" course in the fifth year of the integrated Master's program in the Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto (FEUP), Porto, Portugal. One of the topics in this Mobile Robots course is "Localization of Mobile Robots using the Extended Kalman Filter in a LEGO NXT," which gives the students the opportunity to study the concepts of localization. This experiment comes within the framework of teaching localization concepts in mobile robotics and focuses primarily on explaining the Kalman filter concept. It involves a specific tool developed by the authors and based on LEGO NXT technology. The work presented here could be a helpful guide for teaching concepts related to localization in mobile robotics to ensure adequate understanding of the concept and of the use of the extended Kalman filter (EKF). The LegoFeup robot described here was built using a LEGO Mindstorms NXT and tested both in simulation and in real scenarios. Based on the results obtained, the authors concluded that the developed tool is effective in motivating students. The implementation of the tool, the structure of the Mobile Robots course, and the criteria for student assessment are described in this paper.

Abstract
It is well-known that ROVs require human intervention to guarantee the success of their assignment, as well as the equipment safety. However, as its teleoperation is quite complex to perform, there is a need for assisted teleoperation. This study aims to take on this challenge by developing vision-based assisted teleoperation maneuvers, since a standard camera is present in any ROV. The proposed approach is a visual servoing solution, that allows the user to select between several standard image processing methods and is applied to a 3-DOF ROV. The most interesting characteristic of the presented system is the exclusive use of the camera data to improve the teleoperation of an underactuated ROV. It is demonstrated through the comparison and evaluation of standard implementations of different vision methods and the execution of simple maneuvers to acquire experimental results, that the teleoperation of a small ROV can be drastically improved without the need to install additional sensors.

Abstract
This paper presents a visual localization approach that is suitable for domestic and industrial environments as it enables accurate, reliable and robust pose estimation. The mobile robot is equipped with a single camera which update sits pose whenever a landmark is available on the field of view. The innovation presented by this research focuses on the artificial landmark system which has the ability to detect the presence of the robot, since both entities communicate with each other using an infrared signal protocol modulated in frequency. Besides this communication capability, each landmark has several high intensity light-emitting diodes (LEDs) that shine only for some instances according to the communication, which makes it possible for the camera shutter and the blinking of the LEDs to synchronize. This synchronization increases the system tolerance concerning changes in brightness in the ambient lights over time, independently of the landmarks location. Therefore, the environment's ceiling is populated with several landmarks and an Extended Kalman Filter is used to combine the dead-reckoning and landmark information. This increases the flexibility of the system by reducing the number of landmarks required. The experimental evaluation was conducted in a real indoor environment with an autonomous wheelchair prototype.

Abstract