# UAV Deployment Using Two Levels of Stigmergy for Unstructured Environments
###### Abstract  
This article will present two swarming behaviors for deployment in unstructured environments using unmanned aerial vehicles (UAVs). These behaviors will use stigmergy for communication. We found that there are currently few realistic deployment approaches that use stigmergy, due mainly to the difficulty of building transmitters and receivers for this type of communication. In this paper, we will provide the microscopic design of two behaviors with different technological and information requirements. We will compare them and also investigate how the number of agents influences the deployment. In this work, these behaviors will be exhaustively analyzed, taking into account different take-off time interval strategies, the number of collisions, and the time and energy required by the swarm. Numerous simulations will be conducted using unstructured maps generated at random, which will enable the establishment of the general functioning of the behaviors independently of the map used. Finally, we will show how both behaviors are capable of achieving the required deployment task in terms of covering time and energy consumed by the swarm. We will discuss how, depending on the type of map used, this task can be performed at a lower cost without using a more informed (but expensive) robotic swarm. s 2020 10 7696 2  
We are currently experiencing a time of miniaturization and cost reduction for these devices, going from drones weighing 20 g (autonomous of 10 min, with costs close to 25$ per individual) to drones that are more autonomous, weighing less than 200 g (with greater load capacity and automocoverage of terrestrial robots. Then, we will review some applications designed to be executed using aerial vehicles and comment on some examples of stigmergy communication in swarm systems. Although many applications are related to multirobot deployment, many of them rely on a preestablished communication network or external localization service. This precludes application to emergency tasks, where we are not able to assume the existence of communication networks or GPS. Therefore, there are several studies where the external communication network is substituted by the deployment of a new network of sensor nodes or radio beacons. For example, in [5] uses a distributed algorithm to guide a group of robots through a network of sensors, in [6] defines an artificial pheromone map which consists of arbitrary graph of special devices called \"place agents\". However, this and other similar strategies, such as [7], define the prior deployment of the sensor network, which may be difficult or not practical in emergency situations. Another alternative is to include the deployment process in the behavior. In this case, the behavior consists of two phases: beacon deployment and robot deployment. [8] deploys a network of static radio beacons to form a long-range communication network to aid robot exploration. However, in a general way, this strategy cannot be used with micro or small-sized UAVs (the most commonly used type of UAVs in indoor environments) because of their low load capacity and limited autonomy [9]. In [10; 11], a different strategy is presented, where each individual integrates a beacon, creating their own communications network. However, the design of this behavior is intended for the physical characteristics of terrestrial robots s-bot, which makes it very difficult to generalize to other types of vehicles due to their different types of sensors and actuators. In our case, we are interested in developing behavior that uses the advantages of indoor flying vehicles, rather than terrestrial ones, in emergency situations. For example, these vehicles can avoid many obstacles that limit ground vehicles and enable sensor data to be obtained from a vantage point [12; 13; 14; 15; 16]. This type of device requires specific considerations and algorithms to ensure navigability [4; 17]. In outdoor UAVs, GPS, which can be adjusted by using onboard inertial sensors, is the most common localization system.