## 3 Firefighting Robots
### Surveillance  
Fire surveillance is the most covered activity in the literature about robotics for firefighting. Most of the proposals involve the use of different kinds of aerial robots (fixed-wing and multi-rotor drones) equipped with various types of cameras (RGB, infrared, multispectral...) to watch over the forests from above. Fire surveillance tasks may have up to four objectives: search of potential fires, detection to alert firefighters, diagnosis to get relevant data about the fire, and prognosis to predict fire propagation [20]. The early detection of fire is as important as the complete analysis of it, given that firefighting teams need information such as the ignition and danger potential to organize their operations [21].  
Unmanned aerial vehicles (UAVs) with on-board vision systems have considerable potential in the detection and monitoring of forest fires, since they offer high maneuverability, flexible perspective and resolution, and limited risks to people [22]. For this purpose, surveillance systems should integrate six elements: a fleet of UAVs with payloads, sensor fusion and image processing methods, guidance, navigation and control (GNC) algorithms, coordination and cooperation strategies, path planning algorithms, and ground control stations (GCS) [23]. The selected UAVs shall meet a set of requirements, such as long flight time, accurate localization with the data obtained by the Inertial Measurement Unit (IMU) and Global Navigation Satellite System (GNSS), stable and robust flight, and good image quality [24].  
There are multiple approaches to develop vision systems to detect fires. The work in [25] comprehensively analyzes the potential sensors and methods for terrestrial, aerial, and satellite-based fire detection systems. Regarding the hardware, they use visible [26; 27], thermal [28; 29], multispectral [30; 31] and infrared cameras [20; 32], as well as environmental sensors (mostly used in indoor scenarios [33], but also proposed for forests [21]). Regarding the software, traditional computer vision algorithms [22; 34] compete with recent artificial intelligence solutions [35; 36]. The most common features used to recognize fires in aerial images are color, geometry, and movement. Color and geometry allow detecting potential fires in isolated frames, whereas movement is relevant to check these detections with the whole sequence of frames [22]. A challenge for these algorithms is adapting to different types of fires and scenarios: for instance, subterranean fires show up as columns of smoke, in contrast to common surface forest fires [37].  
Heterogeneous multi-robot systems are also considered for fire surveillance. The work presented in [38] proposes an air-ground robotic team, where the Unmanned Ground Vehicles (UGVs) compensate for the weaknesses of UAVs, such as their limitations in autonomy (flight time) and payload (weight capacity). This work proposes the use of UGVs to transport UAVs to the fire scenario, where UAVs can take off, perform their tasks, and land again. Additionally, UGVs are used as base stations for UAVs, centralizing the communications between the fleet, processing the data collected by them, and coordinate their tasks in the scenario. Moreover, the work published in [35] proposes the use of two different types of drones: fixed-wing UAVs for medium-altitude flights searching fires and rotary-wing UAVs for low-altitude flights checking detections. The need for checking detection to avoid false alarms is also expressed in [39], which suggests the use of multiple drones to collect simultaneous information of every area, as well as the use of various features to detect fires in the provided images (e.g., color and movement).