# UAV Deployment Using Two Levels of Stigmergy for Unstructured Environments
## 4 Results  
### Purpose and Design of Experiments  
In this section, several tests will be conducted to verify the behaviors presented in the previous section. Several experiments will be performed to analyze the number of collisions (if a collision occurs, even if it is recoverable, it is counted for each of the agents involved), the time required to cover 95% of the area to be explored, the energy consumed and the total flight time of the swarm. We will use two main types of maps to compare, in a general way, the deployment strategy used as well as the functioning of unstructured behaviors. We will add a type of map that is more oriented to the comparison of the behaviors in complex environments of very difficult access. For each test, a random map will be generated with the characteristics listed below using cellular automata (as briefly described in the discussion of the simulator). In this way, we will check the robustness of the behaviors in unstructured environments. The free points from which vehicles could be introduced into the system will also be calculated. In this way, random behavior start points will also be used. The first type of maps will be called _easy_ and will be maps with a walkable zone of approximately 75 m\({}^{2}\). Easy maps will be unstructured maps with few ramifications and obstacles. The second type will be called _hard_. Hard maps will be much more extensive maps with an approximately 200 m\({}^{2}\) walkable zone. These maps will also have much higher entropy, with obstacles that will make it difficult to navigate and with a multitude of paths and variants that will make it difficult to perform the desired behavior. To verify exhaustively the behaviors presented in complex environments, we will perform some tests in _hard_ environments with very difficult access, where there will be a central wall on the map that will divide it into two parts with a single access point of 10 m. To completely cover these maps, at least half of the swarm must pass through the door. These maps will be called _wall_. Figure 11 shows an example of each of type of map. In this section, we want to provide a set of tests that allow us to verify several aspects. On the one hand, the take-off time interval is important in terms of the number of collisions and the total energy required by the swarm. We are interested in validating two strategies (LTID and FGID), taking into account a new parameter, the maximum number of active vehicles that may be navigating at a specific moment. In addition, we want to validate the importance of the take-off time interval is maintained in all types of maps. We also want to test the energy efficiency of our two behavior patterns. The implementation requirements are the same, so we assume that there will be variation in both with respect to the energy required, deployment times and number of collisions. It is also important to verify how different types of maps affect the behavior. We will verify the performance of both types of behavior with all three types of maps. Figure 11: Example of maps types. A 15 m \(\times\) 15 m area is shown. (**a**) _Easy_, with approximately 75 m\({}^{2}\) of walkable space; (**b**) _hard_, with approximately 200 m\({}^{2}\) of walkable space and with a greater entropy; (**c**) _wall_, the same as _hard_, but with a wall that divides the map into two zones. To test the LTID deployment strategy, 50 tests will be developed for different take-off time intervals (6, 8, 10, 12, 18, and 24 s). Each test will have a randomly generated map of the type to be tested, as well as a different starting point. By not considering redeployment, we will assume that we have a sufficient number of individuals to completely cover the environment (given their area of perception, in our case, set to \(r_{coverage}\)). In these tests, we will assume an upper limit of 100 individuals, which allows us to cover a maximum area of 1000 m\({}^{2}\).