Abstract
This paper addresses the topic of target detection and tracking using a team of UAVs for maritime border surveillance. We present a novel method on how to integrate the perception into the control loop using two distinct teams of UAVs that are cooperatively tracking the same target. We demonstrate and evaluate the effectiveness of our approach in a simulation environment.

Abstract

Abstract
Today, in our landscape perception exists a gap that needs to be fulfilled. It's important to increase the coverage, temporal and spatial resolution in order to cover this gap, as well as reduce costs with human resources that usually take this kind of tasks. Unmanned Autonomous vehicles with their inherent autonomy and reduced needs of human and communication resources, can provide additional capabilities and a new innovative solution to this problem This paper presents and describes the participation of ICARUS Team at euRathlon 2015 and the importance of this type of events performed with multiple unnamed systems.

Abstract
The multirotor UAVs are being integrated into a wide range of application scenarios due to maneuverability in 3D, versatility and reasonable payload of sensors. One of the application scenarios is the inspection of structures where the human intervention is difficult or unsafe and the UAV can provide an improvement of the collected data. At the same time introduce challenges due to low altitude missions and also the fact of being manually operated without line of sight. In order to overcome these issues, this paper presents a LiDAR-based realtime collision avoidance algorithm, denoted by Escape Elliptical Search Point with the ability to be integrated into autonomous and manned modes of operation. The algorithm was validated in a simulation environment developed in Gazebo and also in a mixed environment composed by a real robot in an outdoor scenario and simulated obstacle and LiDAR.

Abstract
The fish farming industry is becoming widespread all over the world. By 2039 most of the fish we eat will come from the fish farming industry. In this work, we propose an autonomous robotic solution for indoor fish farming biomass estimation. Our proposed system moves silently on top of the tank borders using differential wheels and a structured light vision system (SLS). The SLS system is composed by a camera and two line lasers (projectors) equipped with a line beam that allows to obtain the fish depth profile present in the tank to perform biomass estimation. Results in laboratory and in real aquaculture environment with live fish are presented.

Abstract
Underwater experiments with unmanned vehicles are complex, costly, time-consuming and in some circumstances potentially dangerous, involving the risk of losing or damaging the robots. The nature of the underwater environment, makes it very difficult, for researchers, to observe the evolution of the running system. Simulators are useful tools for the development of unmanned vehicle software, algorithm benchmarking and system preliminary validation. In this work, the problem of simulating a complex underwater scenario for marine robotics and a comparative analysis of simulators for marine robotics are presented. Relevant sensors for underwater robots under development, such as multibeam and imaging 2D sonar were implemented in two simulators and tested in a realistic experimental scenario like a flooded mine.