# UAV Deployment Using Two Levels of Stigmergy for Unstructured Environments
## 3 Methods
### Slant Beacon Deployment (SLABE)  
#### 3.2.1 Behavior Description  
Quantitative marker-based stigmergy is the most common choice in designing swarm intelligence systems. Its simplicity, scalability, robustness, and integration with the environment make it an ideal mechanism for communication in this type of system. However, the main difficulty is the lack of functional systems that allow it to be implemented in real environments.  
Sematectonic stigmergy has the advantage in this sense because it does not require any external elements to be returned for communication: the same individuals may be deposited in the environment to communicate. In nature, we can see several examples, such as ant cemetery clustering and wasp nest construction.  
Slant beacon deployment (SLABE) behavior aims to cover an unstructured and a priori complex environment. It assumes the existence of a sufficiently large set of simple individuals that can cover the environment without redeployment. As in [27], we propose the existence of two distinct states of energy consumption. In UAVs, the highest consumption is established in flight, so this energy consumption must be correctly modeled to increase the autonomy of these aircraft, which is a priori very limited. As mentioned above, an initial launch area from which all drones will take off and will be defined. The first drone will be placed in the immediate neighborhood of the launch area and will go into the _beacon_ state (also called _repeater_). Then, a new drone will take off and travel, simply avoiding obstacles, through the interior of the area to be explored. As soon as it detects that it is near the limit of the communication safety zone, it will stop the flight and return to the _beacon_ status. This process will be repeated until the environment is fully covered.  
The role of _beacon_ (_repeater_) drones is to recommend \"the best address\" they have observed so far to all active drones that pass by. To do this, they will ask the drones to pass through their immediate vicinity in the direction they were heading just before leaving their area of influence. This direction will  
Figure 2: (**a**) Example of environment obtained by Algorithm 1, representing an area of 15 m \(\times\) 15 m. (**b**) 3D visualization of this environment. Several bottom-right walls have been removed to highlight the access point to the environment. (**c**,**d**) Example of drone entry process and first steps of deployment behavior.  
be absolute and obtained from the compass of the vehicles. The drones that do not pass through other vehicle again will be assumed to have managed to reach remote areas in the direction they took as they passed through this particular _beacon_. However, the directions recommended by the drones that repeatedly pass near the _beacon_ should not be considered with the same importance since they do not lead to the vehicle moving away from the drone's zone of influence. There will also be a mechanism for local communication between the _beacons_ to prioritize promising paths, but this will be described later.  
The active drones will navigate while considering several factors, including the obstacles in the environment and the proximity to other active vehicles with which they can coexist at any given moment.They will also take into account the recommendations made by _beacon_ drones.  
This behavior does not require local localization, which makes it especially suitable for implementation with microdrones. In addition, the stigmergy used does not require any sensors that are not currently available: we simply need to determine whether a vehicle is in a certain range of action. For example, the iBeacons protocol provides the necessary technical requirements to implement this behavior. This makes it possible to detect the location of two devices in a discreet way (far, near, immediate) with sufficient accuracy, in addition to obtaining the RSSI signal strength.