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.

This paper presents a system for obstacle detection in railway level crossings from 3D point clouds acquired with tilting 2D laser scanners. Although large obstacles in railway level crossings are detectable with current solutions, the detection of small obstacles remains an open problem. By relying on a tilting laser scanner, the proposed system is able to acquire highly dense and accurate point clouds, enabling the detection of small obstacles, like rocks laying near the rail. During an offline training phase, the system learns a background model of the level crossing from a set of point clouds. Then, online, obstacles are detected as occupied space contrasting with the background model. To reduce the need for manual on-site calibration, the system automatically estimates the pose of the level crossing and railway with respect to the laser scanner. Experimental results show the ability of the system to successfully perform on a set of 41 point clouds acquired in an operational one-lane level crossing.

This paper presents RIVERWATCH, an autonomous surface-aerial marsupial robotic team for riverine environmental monitoring. The robotic system is composed of an Autonomous Surface Vehicle (ASV) piggybacking a multirotor Unmanned Aerial Vehicle (UAV) with vertical takeoff and landing capabilities. The ASV provides the team with longrange transportation in all-weather conditions, whereas the UAV assures an augmented perception of the environment. The coordinated aerial, underwater, and surface level perception allows the team to assess navigation cost from the near field to the far field, which is key for safe navigation and environmental monitoring data gathering. The robotic system is validated on a set of field trials.

A robust maintenance of ecosystems demands for highly accurate and frequent monitoring of their status. The extension and remoteness of some environments renders their human-based monitoring extremely difficult. Riverine environments are a notorious example, as their sampling requires to bear into account both streams and riverbanks. The relevance of monitoring riverine environments is magnified by the intricate interactions that occur between river waters and coastal waters. This article provides a critical survey of existing solutions using robots for environmental monitoring of water bodies. Based on the survey, this article argues that autonomous robotic marsupial systems are especially adequate for the tasks at hand. Lessons learned, as well as future avenues on the application of marsupial robotic teams to environmental monitoring, are laid out in this article.

Testing and debugging real hardware is a time consuming task, in particular for the case of aquatic robots, for which it is necessary to transport and deploy the robots on the water. Performing waterborne and airborne field experiments with expensive hardware embedded in not yet fully functional prototypes is a highly risky endeavour. In this sense, physics-based 3D simulators are key for a fast paced and affordable development of such robotic systems. This paper contributes with a modular, open-source, and soon to be freely online available, ROS-based multi-robot simulator specially focused for aerial and water surface vehicles. This simulator is being developed as part of the RIVERWATCH experiment in the ECHORD european FP7 project. This experiment aims at demonstrating a multi-robot system for remote monitoring of riverine environments.