## 6 Conclusions and Future Work  
An algorithm capable of performing some of the main tasks in search and rescue missions using aerial robotics has been proposed, based on swarm concepts. The algorithm uses the attraction and repulsion forces approach in order to keep the swarm as compact as possible or to be attracted by victims and at the same time avoid collisions or keep a minimum distance between agents, respectively. The algorithm uses the graph theory as the main tool to model and work with the robots' swarm. On this approach, the nodes represent the robots or sensor estimations and links are the distance between them or the weighted distance assigned to their location respect to the victim. Once that behaviors of navigation are achieved in the swarm, the next step is to create the policy that allows the swarm to generate a sub-swarm and disconnect the agents in the sub-swarm from the main one. As explained before, the generation of sub-swarm allows this algorithm to perform two tasks at the same time, which are the classification and assistance to a victim with the sub-swarm and continue the navigation seeking for more victims using the rest of the swarm. As expected, the navigation process in a non-convex environment using artificial potential functions was successful as well as the implementation of sub-swarm generation in order to classify victims. The use of the k-nearest approach to a weighted graph has a good performance in the generation of sub-swarms close to a target location which could be a possible victim. The sub-swarm allows the system to perform a consensus algorithm at the same time that the other agents from the main swarm can still be navigating looking for more victims. This consensus in order to get closer to the victim and then through formation control surround it, improving in this way the probability of well victim detection. Once the robots get better location around the victim, they accomplish an estimator distributed consensus modeling the sensor by least square approach allowing sub-swarm to evaluate if they are sensing a victim or not. Several experiments were carried out by using a robot simulator. This system allows using virtual models of real robots and the creation of environments with obstacles and people as victims whit-in. The simulation exposes how the algorithm generates a swarm behavior by allocating each robot behavior. The robots' swarm were capable of navigating without colliding with obstacles in the environment or with themselves and generates sub-swarms when they find a victim. Additionally, when a sub-swarm found a victim is showed how the formation control allows the robots to locate around the victims which is crucial in order to estimate if there is or not a victim in the place. As future work, we intend to give more attention to the time-varying dynamical graphs in order to model extra behaviors concerning to the sub-swarm generation and the inclusion of heterogeneous agents. On the other hand, it is important to develop an algorithm that allows the system to get information from the detection of a victim, for instance, use of visual information to determine if the robot is in front of a \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Drone Number** & **Uncovered** & **Covered** & **+2 Drones** & **+3 Drones** & **+4 Drones** & **+5 Drones** \\ \hline
7 & 0.0\% & 100\% & 93.14\% & 16.63\% & 0.01\% & 0.0\% \\
12 & 1.02\% & 98.87\% & 83.88\% & 29.8\% & 5.03\% & 0.0\% \\
17 & 0.0\% & 100\% & 89.10\% & 48.48\% & 11.87\% & 0.0\% \\
23 & 0.0\% & 100\% & 87.77\% & 54.55\% & 20.53\% & 0.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentage of the covered area according to different swarm size and \(r=d/4\). victim. Taking this into account, the next step is to apply this information coming from the sensors in each robot close to the victim in the distributed estimation consensus algorithm exposed in this work.