Abstract
This paper reports the design of a new remotely operated underwater vehicle (ROV), which has been developed at the Underwater Systems and Technology Laboratory (USTL) - University of Porto. This design is contextualized on the KOS project (Kits for underwater operations). The main issues addressed here concern directional drag minimization, symmetry, optimized thruster positioning, stability and layout of ROV components. This design is aimed at optimizing ROV performance for a set of different operational scenarios. This is achieved through modular configurations which are optimized for each different scenario.

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This paper propose a real-time vision architecture for mobile robotics, and describes a current implementation that is characterised by: low computational cost, low latency, low power, high modularity, configuration, adaptability and scalability. A pipeline structure further reduces latency and allows a paralleled hardware implementation. A dedicated hardware vision sensor was developed in order to take advantage of the proposed architecture. A new method using run length encoding (RLE) colour transition allows real-time edge determination at low computational cost. The real-time characteristics and hardware partial implementation, coupled with low energy consumption address typical autonomous systems applications.

Abstract
This work presents a hybrid coordinated manoeuvre for docking an autonomous surface vehicle with an autonomous underwater vehicle. The control manoeuvre uses visual information to estimate the AUV relative position and attitude in relation to the ASV and steers the ASV in order to dock with the AUV. The AUV is assumed to be at surface with only a small fraction of its volume visible. The system implemented in the Autonomous Surface vehicle ROAZ, developed by LSA-ISEP to perform missions in river environment, test autonomous AUV docking capabilities and multiple AUV/ASV coordinated missions is presented. Information from a low cost embedded robotics vision system (LSAVision), along with inertial navigation sensors is fused in an extended kalman filter and used to determine AUV relative position and orientation to the surface vehicle The real time vision processing system is described and results are presented in operational scenario.

Abstract
This paper proposes a novel architecture for real-time 3D ball trajectory estimation with a monocular camera in Middle Size League scenario. Our proposed system consists on detecting possible multiple ball candidates in the image, that are filtered in a multi-target data association layer. Validated ball candidates have their 3D trajectory estimated by Maximum Likelihood method (MLM) followed by a recursive refinement obtained with an Extended Kalman Filter (EKF). Our approach was validated in real RoboCup scenario, evaluated recurring to ground truth information obtained by alternative methods allowing overall performance and quality assessment. © 2012 Springer-Verlag Berlin Heidelberg.