# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 5 USV Applications for Preventive Maintenance and Disaster Response
### Search and Rescue
#### 5.3.2 Heterogeneous Teams of Unmanned Systems for SAR  
Detection and tracking of people and life-rafts adrift at sea is often performed by airplanes and cameras, which can offer a wider field of view of the sea surface, in comparison to those of ships and USVs at the sea surface. However, this process is often tedious and challenging due light reflections at sea, intermittent target occlusion, the distance from the plane to a target and its size relative to this distance. Therefore, the automation of such process is currently a research topic, where vision systems are used to detect and track survivors [209; 210]. More recently, researchers are investing in effective combinations of heterogeneous unmanned systems, such as UAVs and USVs [211] to further automate SAR operations at sea. The ICARUS project stands out as the leading initiative focusing on SAR in marine environments [205]. Notwithstanding, ICARUS' resources encompass robust unmanned systems designed for air, land, and water SAR operations, and training exercises tested its operational validation, involving different scenarios such as shipwcks, where a team of robots presented effective results while detecting and helping people on the water [201]. The primary goal of the project is to enable first-responders with a team of unmanned systems, along with management tools to enable faster and more effective SAR operations [21]. In [212], a USV-UAV team is used to detect castaways using a particle filter computed in the USV, as it has higher computational power than the UAV, which in turn only provides aerial images as the filter's input. The system uses Artificial Neural Networks to compensate water and wind disturbances, predict the castaway's position, and find and track them. Unfortunately, the system is only tested in simulated scenarios. A combined UAV-USV decision-making strategy for maritime rescue, based on Bayesian Network, is proposed in [213], which also features only simulated experiments. [PERSON] and [PERSON] [214], designed an AUV-USV rescue system to search for survivors in the water efficiently The system is comprised of one USV which can launch four UAVs helicopters which search for survivors by measuring the temperature of each point visited in the search space using an IR camera. The authors conclude that a particle swarm optimization algorithm is superior to a random search strategy in 20 simulated experiments. Cooperation between EMILY and a Fotokite UAV [215] was proposed in [216], to assist drowning victims by determining EMILY pose from pre-processed video feeds from the UAV, using different video strategies. Two methods were used for position estimation: (1) Color thresholding, erosion, dilatation, and blob detection; (2) Histogramming, back-projection, and CamShift. The orientation was estimated by fitting a minimum area ellipse over the blob and finding its greater axis. Later, a different camera stabilization method was developed [217], to correct camera pose errors due to wind and motion of the Fotokite, which is validated in a set of trials. [PERSON] et al. [218], presented several tests in four outdoor scenarios using EMILY and the Fotokite, where the UAV provides first-responders with a top view of the scenario, covering a large area to guide the USV autonomously to victims without the requirement of manual operation, freeing the rescue team to other tasks. In [219] the authors argue that the use of rotary wings UAVs, which prioritize camera motion, can provide better situation awareness to operators in disaster scenarios by increasing the amount of time USVs are visible to the UAV when compared to traditional motion using fixed-wing UAVs. Recently, [PERSON] and [PERSON] [220] aimed to rigorously define the sub-problems and assumptions about the control of USVs using UAV top-view information. They provide an in-depth theoretical background for each sub-problem and define the theoretical lower-bound limits for their solutions.