Abstract
The genetic algorithm is a general purpose optimization metaheuristic for solving complex optimization problems. Because the algorithm usually requires a large number of iterations to evolve a population of solutions to good final solutions, it normally exhibits long execution times, especially if running on low-performance conventional processors. In this work, we present a scalable computing array to parallelize and accelerate the execution of cellular GAs (cGAs). This is a variant of genetic algorithms which can conveniently exploit the coarse-grain parallelism afforded by custom parallel processing. The proposed architecture targets Xilinx FPGAs and was implemented as an auxiliary processor of an embedded soft-core CPU (MicroBlaze). To facilitate the customization for different optimization problems, a high-level synthesis design flow is proposed where the problem-dependent operations are specified in C++ and synthesised to custom hardware, thus demanding of the programmer only minimal knowledge of low-level digital design for FPGAs. To demonstrate the efficiency of the array processor architecture and the effectiveness of the design methodology, the development of a hardware solver for the minimum energy broadcast problem in wireless ad hoc networks is employed as a use case. Implementation results for a Virtex-6 FPGA show significant speedups, especially when comparing to embedded processors used in current FPGA devices.

Abstract
In this work we present a reconfigurable and scalable custom processor array for solving optimization problems using cellular genetic algorithms (cGAs), based on a regular fabric of processing nodes and local memories. Cellular genetic algorithms are a variant of the well-known genetic algorithm that can conveniently exploit the coarse-grain parallelism afforded by this architecture. To ease the design of the proposed computing engine for solving different optimization problems, a high-level synthesis design flow is proposed, where the problem-dependent operations of the algorithm are specified in C++ and synthesized to custom hardware. A spectrum allocation problem was used as a case study and successfully implemented in a Virtex-6 FPGA device, showing relevant figures for the computing acceleration.

Abstract
Underwater sensing and mapping operations using autonomous vehicles are becoming widely used. This article describes an automated system to launch and recover an AUV. It can operate in any host platform and can transport any torpedo-shaped vehicle with 0.2 meters of diameter, length up to 3 meters and weight up to 1000 N. The system ensures a restrained transportation of the vehicle and guarantees that it performs a smooth entrance in the water. It was instrumented for continuous status remote monitoring, using linear and angular motion sensors, as well as enables to remotely take control over the operation. Experimental results carried out within the XPRIZE competition demonstration scope are presented.

Abstract
This paper presents an overview of a generalized 6 degrees of freedom model for surface vessels and explains how it can be extended for twin hull surface vehicles. The extended model takes into account the hull characteristics (dimensions and location), which are important to improve the accuracy of simulations and the performance of controllers. The method involves the calculation of the submerged volume of each hull, location of each hull's center of buoyancy and restoring forces/ torques due to buoyancy contributions. To evaluate the proposed model, some simulations were performed, using an example of allocation of propulsion system and realistic hydrodynamic coefficients (added mass and damping) and inertial tensors.

Abstract
Unmanned Underwater Vehicles (UUVs), such as Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) are versatile tools, suitable for many activities in different fields, and have seen an increase in usage, making them an area of interest in the study of robotics. The performance of any underwater vehicle in any given task is deeply affected by the precision of its localization system. The main challenge in underwater localization is the significant attenuation of any Radio Frequency (RF) signal underwater, which prevents the use of many common location methods such as the Global Positioning System (GPS). Many methods have been studied for the localization of UUVs, including the use of acoustic beacons. One of these methods is the use of a single moving beacon to obtain acoustic ranges, as opposed to a stationary single beacon, which restricts the UUV's trajectory or multiple beacons, involving more hardware, complicating missions' logistics and increasing costs. In this paper, a guidance algorithm based on the Fisher Information Matrix is proposed for an Autonomous Surface Vehicle to serve as a beacon vehicle and aid in the navigation of a UUV. The approach performances are assessed by means of simulations of the complete system under realistic conditions.

Abstract
Robotic vehicles are already deployed around the globe as a preferred tool for ocean sampling, from surface coastal waters down to the deepest remote locations. They are mainly used to perform routine measurement tasks, with obvious benefits in terms of space and time density, as well as accuracy in the localization of measurements. As the underlying technology matures, new features are being introduced and validated in operational scenarios, enabling new paradigms in ocean observation. This chapter describes the latest developments in marine robotics, presented in different stages of maturity, and sheds light on upcoming features available to the scientific community.