Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, the research involving autonomous boats or Unmanned Surface Vehicles (USVs) for Disaster Management (DM) is currently spread across several publications, with varying degrees of depth, and focusing on more than one unmanned vehicle—usually under the umbrella of Unmanned Marine Vessels (UMV). Therefore, the current importance of USVs for the DM process in its different phases is not clear. This paper presents the first comprehensive survey about the applications and roles of USVs for DM, as far as we know. This work demonstrates that there are few current deployments in disaster scenarios, with most of the research in the area focusing on the technological aspects of USV hardware and software, such as Guidance Navigation and Control, and not focusing on their actual importance for DM. Finally, to guide future research, this paper also summarizes our own contributions, the lessons learned, guidelines, and research gaps.

The use of robotics in disaster scenarios has become a reality. However, an Unmanned Surface Vehicle (USV) needs a robust navigation strategy to face unpredictable environmental forces such as waves, wind, and water current. A starting step toward this goal is to have a programming environment with realistic USV models where designers can assess their control strategies under different degrees of environmental disturbances. This paper presents a simulation environment integrated with robotic middleware which models the forces that act on a USV in a disaster scenario. Results show that these environmental forces affect the USV’s trajectories negatively, indicating the need for more research on USV control strategies considering harsh environmental conditions. Evaluation scenarios were presented to highlight specific features of the simulator, including a bridge inspection scenario with fast water current and winds.

Hydroelectric power plants often make use of tunnels to redirect the flow of water to the plant power house. Such tunnels are often flooded and can span considerable distances. Periodical inspections of such tunnels are highly desirable since a tunnel collapse will be catastrophic, disrupting the power plant operation. In many cases, the use of Unmanned Underwater Vehicles (UUVs) equipped with mechanical profiling sonars is a suitable and affordable way to gather data to generate 3D mapping of flooded tunnels. In this paper, we study the resolution of 3D tunnel maps generated by one or more mechanical profiling sonars working in tandem, considering synchronization and occlusion problems. The article derives the analytical equations to estimate the sampling of the underwater tunnels using mechanical profiling sonars (scanning sonars). Experiments in a simulated environment using up to four sensors simultaneously are presented. We also report experimental results obtained by a UUV inside a large power plant tunnel, together with a first map of this environment using a single sonar sensor.