## 4 System Overview
### Drone Swarm  
The mission described in the previous section can be addressed by several types of aerial robot systems. The first approach is using a heterogeneous drone fleet, so different types of drones can adapt to different types of tasks, increasing the efficiency of the whole mission. For instance, fixed-wing drones can do the tasks that require covering large areasas surveillance and mapping, whereas rotary-wing drones can do the tasks that require stationary flights as monitoring and support. However, our proposal involves the use of a homogeneous drone swarm to solve this mission. This system relies on the cooperation between drones to accomplish the tasks and not on the adaptation of them to specific tasks. In this case, the same type of drones can perform surveillance and monitoring, but in a different number. Both systems have advantages and disadvantages in the defined scenario. As already mentioned, heterogeneous fleets can optimize the missions by allocating their different resources to different tasks. Additionally, these systems are easier to control because the drones have more capabilities and need less coordination. On the other hand, drone swarms are more scalable and have more flexibility to adapt to the changes in the scenario. Besides, these systems have better fault tolerance because they can recover from losing one or more members. We contemplate the definition of robot swarm drawn from [53]: a robot swarm is a group of simple robots, which individually can only perform rudimentary actions, but collectively form an intelligent system and can perform complex tasks. Therefore, we consider a quadcopter fleet as a robot swarm when the fleet consists of a dozen robots, single robots cannot cover the target scenarios, and individual robots are not able to perform the considered tasks. Firefighting missions involve large and complex terrains, where single quadcopters can only collect local information and perform simple actions. The quadcopters considered for this application shall have the following features:  
* Size and weight: No more than 1600 \(\times\) 1600 \(\times\) 800 mm unfolded and 15 kg including drone and payload. * Autonomy: A minimum of 30 min of flight. * Navigation: Fusion of IMU measurements, visual odometry and GPS/GLONASS/GALILEO signal. * Control: Capability of reaching and hovering on waypoints. * Communications: Telemetry and video links in a range of 5 km. * Payload: Conventional, thermal, and multispectral cameras, as well as temperature, humidity, and gas sensors. The size and weight were established looking for a compromise between versatility and load capacity. On the one hand, the drones must be light enough to be transported to the fire area in a vehicle and deployed in the field by a person. On the other hand, they must carry up to three cameras, environmental sensors, and communication devices. Finally, we have taken into account the impact of these parameters on flight range and maneuverability. Furthermore, autonomy is an essential aspect of the system: practically, the longer the flight time of the drones, the better the viability of the system in real missions. Current high-performance commercial drones offer around 30 min of continuous flight, but this figure may increase in the following years. The navigation capabilities of the drones are another relevant aspect of the operation of the system. We have chosen to combine multiple sources to get high accuracy and fault tolerance. Specifically, we consider a high-performance IMU to provide linear acceleration, rotation speed, and orientation, as well as a GNSS receiver to obtain the position, velocity, and time with high frequency. Additionally, on-board cameras can get terrain features, which allow estimating drone motion. Multiple models can integrate the data provided by these sources to obtain the accurate location of the drone, such as Kalman [54] and particle filters [55]. In this way, the drones can preserve enough autonomy to perform their tasks even in GNSS denied or limited environments.