Automated environmental monitoring in marine environments is currently carried out either by small-scale robotic systems, composed of one or few robots, or static sensor networks. In this paper, we propose the use of swarm robotics systems to carry out marine environmental monitoring missions. In swarm robotics systems, each individual unit is relatively simple and inexpensive. The robots rely on decentralized control and local communication, allowing the swarm to scale to hundreds of units and to cover large areas. We study the application of a swarm of aquatic robots to environmental monitoring tasks. In the first part of the study, we synthesize swarm control for a temperature monitoring mission and validate our results with a real swarm robotics system. Then, we conduct a simulation-based evaluation of the robots' performance over large areas and with large swarm sizes, and demonstrate the swarm's robustness to faults. Our results show that swarm robotics systems are suited for environmental monitoring tasks by efficiently covering a target area, allowing for redundancy in the data collection process, and tolerating individual robot faults.

The sea as a very extensive area, renders difficult a pre-emptive and long-lasting search for shipwreck survivors. The operational cost for deploying manned teams with such proactive strategy is high and, thus, these teams are only reactively deployed when a disaster like a shipwreck has been communicated. To reduce the involved financial costs, unmanned robotic systems could be used instead as background surveillance teams patrolling the seas. In this sense, a robotic team for search and rescue (SAR) operations at sea is presented in this work. Composed of an Unmanned Surface Vehicle (USV) piggybacking a watertight Unmanned Aerial Vehicle (UAV) with vertical take-off and landing capabilities, the proposed cooperative system is capable of search, track and provide basic life support while reporting the position of human survivors to better prepared manned rescue teams. The USV provides long-range transportation of the UAV and basic survival kits for victims. The UAV assures an augmented perception of the environment due to its high vantage point.

The basic idea of a cartogram is to distort a geographical map by substituting the geographic area of a region by some other variable of interest. The objective is to rescale each region according to the value of the variable of interest while keeping the map, as much as possible, recognizable. There are several algorithms for building cartograms. None of these methods has proved to be universally better than any other, since the trade‐offs made to get the correct distortion vary. In this paper we present a new method for building cartograms, based on self‐organizing neural networks (Kohonen's self‐organizing maps or SOM). The proposed method is widely available and is easy to carry out, and yet has several appealing properties, such as easy parallelization, making up a good tool for geographic data presentation and analysis. We present a series of tests on different problems, comparing the new algorithm with existing ones. We conclude that it is competitive and, in some circumstances, can perform better then existing algorithms.