# UAV Deployment Using Two Levels of Stigmergy for Unstructured Environments
## 3 Methods
### Slant Beacon Deployment (SLABE)
#### 3.2.4 Example of Operation  
As noted above, several parameters must be established for the performance of the presented behavior. Most of the parameters can be established in a simple way based on the environment or the type of sensors and actuators available on the robots. In all the experiments that we will perform, we will use the fixed parameters that we present next, which are chosen for their versatility and good performance in various environments. It is important to emphasize that these parameters will not be altered, even for different types of maps (simple or complex) on which the behavior will be tested.  
Figure 6: Discrete kernel \(P(\Lambda_{K})\) used for the convolution process of the distribution of _beacon_ robots.  
The tuple of behaviors that define the \(SLABE_{a}\) behavior used for all the experiments in this article is defined in Equation (7).  
\[SLABE_{a}=\left\{\begin{array}{l}\alpha_{1}=0.26\\ \alpha_{2}=0.52\\ \alpha_{3}=0.21\\ \Lambda_{h_{0}}=1\\ \mu=1\\ \sigma=0.25\\ \beta_{1}:\text{obtained for each $t$ from $P(\Lambda_{B_{t}}^{b})$}\\ \beta_{2}=0.7\\ \beta_{3}=0.999\\ P(\Lambda_{K})\\ \|\mathbf{f}_{0}\|=0\end{array}\right. \tag{7}\]  
It should be mentioned that the normalizing term \(\beta_{1}\) will be calculated for each instant of time \(t\), in a simple way from the current data of the distribution \(P(\Lambda_{B_{t}}^{b})\) for each beacon \(b\). As discussed earlier, the kernel \(P(\Lambda_{K})\) is discretely specified in Figure 6.  
\[Robot_{SLABE}=\left\{\begin{array}{l}RF_{rmax}=6\;\text{m}=\text{uav.exit\_ range}\\ RF_{ibacon}=3\;\text{m}=\text{uav.in\_range}\\ Obs_{rmax}=2.5\;\text{m}\\ vel_{max}=1\;\text{m/s}\\ r_{coverage}=10\;\text{m}\end{array}\right. \tag{8}\]  
A robot that implements SLABE behavior must also define its sensory and motor characteristics, as established in Equation (8). The maximum coverage range \(RF_{rmax}\) to guarantee our radio frequency system is set to 6 m, and a robot is considered to be within the margin of influence of a beacon \(RF_{ibacon}\) if the robot is within a 3 m radius of the beacon's position. The detection range of the obstacle detection system of the robot \(Obs_{rmax}\), its maximum velocity \(vel_{max}\), and the area that is considered to be covered by its position \(r_{coverage}\) are detailed in Figure 6.  
Finally, we will use an energy model based on [27], where a common energy consumption for both types of drones will be used to facilitate the comparisons. A consumption of 120 W will be assumed for a drone in the _active_ state, and 1 W will be assumed for a drone in the _beacon_ state. It should be noted that microdrones with an appropriate configuration can achieve a much lower consumption (mainly due to their reduced weight and therefore lower energy requirements for takeoff).  
An example of how this behavior works for a simple-type map is provided in Figure 7. Various states have been selected, from the initial step to 95% deployment of the map area. Each robot has a viewing area that is used to calculate the percentage coverage of the map (area represented in blue). This figure shows how the behavior and intensity of the recommendation of each _beacon_ robot evolve (the higher the yellow tone of the drone is, the greater intensity of the recommendation). The communication area \(RF_{rmax}\) of the drone can vary from white to yellow to represent this intensity. Finally, the recommended angles for each _beacon_ robot are also displayed.