Abstract
The demand for accurate ocean sampling is continuously growing to provide a better understanding of the complex sea environment. Current economic and social activity is strongly dictated by knowledge built on data collected from thousands of sensors around the world, ranging from space-borne remote sensors to underwater devices transported by profilers. Autonomous sailboats have great potential to gather long-term data to understand multiple aspects of the ocean environment. In terms of oceanography, they can be used to study many processes occurring at the surface, like the energy exchange between the ocean and the atmosphere and how it affects the climate. They can also be a valuable tool to understand the dynamics of episodic events that evolve on a timescale of weeks or months, like harmful algae blooms or the evolution of pollution plumes. Even though these incidents can already be tracked by satellite, the ability to capture in-situ data for the full cycle can provide valuable data about the phenomena.

Abstract

Abstract
The ability to employ autonomous vehicles to find and track the boundary between two different water masses can increase the efficiency in waterborne data collection, by concentrating measurements in the most relevant regions and capturing detailed spacial and temporal variations. In this paper we provide a guidance mechanism to enable an autonomous vehicle to find and track the steepest gradient of a scalar field in the horizontal plane. The main innovation in our approach is the mechanism to adapt the orientation of the crossings to the local curvature of the boundary, so that the vehicle can keep tracking the gradient regardless of its horizontal orientation. As an example, we show how the algorithms can be used to find and track the boundary of a dredged navigation channel, using only altimeter measurements.

Abstract
In this paper we address the problem of tracking multiple AUVs using acoustic signals. Using For this challenging scenario, we propose to use a Probability Hypothesis Density Filter and present a suitable implementation of the Sequential Monte Carlo PHD filter. It will be demonstrated that a particle filter implementation of the aforementioned filter can be used to successfully track multiple AUVs, changing in number over time, using range measurements from the vehicles to a set of acoustic beacons. Simulation results will be presented that allow to evaluate the performance of the filter.

Abstract
Search and rescue (SAR) teams often face several complex and dangerous tasks, which could be aided by unmanned robotic vehicles (UV). UV agents can potentially be used to decrease the risk in the loss of lives both of the rescuers and victims and aid in the search and transportation of survivors and in the removal of debris in a catastrophe scenario. Depending on the nature of a catastrophe and its geographical location, there are potentially three types of UVs that can be deployed: aerial, surface and ground. Due to the control and manipulation particularities each type of UV contemplates, their operators need prior training and certification. To train and certify the operators a tool (serious game) is under development. In this paper we will make an overview about our approach in its development. This game uses a typical client-server architecture where all client agents (virtual UVs and operator client interfaces) share the same immersive virtual environment which is generated through the merging of GIS data and a semantic model extracted from 3D laser data. There will be several types of scenarios suitable to several types of catastrophe situations. Each of these scenarios has its own mission plan for the trainees to follow. The game will also provide an interface for mission planning so that each mission plan will be carefully designed to accurately correspond to a matrix of skills. This matrix lists a set of common skills in various different UV operational case studies which will allow the certification of operators.

Abstract
Autonomous underwater vehicles (AUVs) are increasingly being used to perform mine countermeasures (MCM) operations but its capabilities are limited by the efficiency of the planning process. Here we study the problem of multiobjective MCM mission planning with AUVs. The vehicle should cover the operating area while maximizing the probability of detecting the targets and minimizing the required energy and time to complete the mission. A multi-stage algorithm is proposed and evaluated. Our algorithm combines an evolutionary algorithm (EA) with a local search procedure, aiming at a more flexible and effective exploration and exploitation of the search space. An artificial neural network (ANN) model was also integrated in the evolutionary procedure to guide the search. The combination of different techniques creates another problem, related to the high amount of parameters that needs to be tuned. Thus, the effect of these parameters on the quality of the obtained Pareto Front was assessed. This allowed us to define an adaptive tuning procedure to control the parameters while the algorithm is executed. Our algorithm is compared against an implementation of a known EA as well as another mission planner and the results from the experiments show that the proposed strategy can efficiently identify a higher quality solution set. © 2014 World Scientific Publishing Company.