## 1 Introduction  
In recent years, the use of Unmanned Aerial Vehicles (UAVs) has become important in numerous tasks, such as security surveillance, transportation [1], rescue and environmental monitoring. An outstanding capacity of these vehicles is that they can allow not only monitor environments with harsh conditions where humans cannot have access, but they can also monitor scenarios that change periodically, with a high spatial and temporal resolution [2]. For example, in flood monitoring it is necessary to identify the increase in water levels of certain regions in the environment. The water level can move in an uncontrolled way and change with a high frequency. Therefore, to identify these changes it is necessary to perform reconnaissance (Reconnaissance refers to the task of traveling with the purpose of discovering new territories, unknown spaces, roads and routes.) tasks over an area. This implies frequently collecting and selecting the most relevant data about the current status of the environment. Once the reconnaissance task has been done, oversampling some regions is necessary to determine which regions are relevant despite changes in the environmental conditions. A feasible solution is to employ UAVs to perform the reconnaissance task. In this sense, some UAVs can sample the area through various flights, exchanging partial views to determine the regions with relevant environmental data. However, due to their mobility, UAVs have inherent constraints in terms of energy and transmission range. Thereby, it is necessary not only to perform the communication among these vehicles even with a lack of direct coupling among senders and receivers, but also to tackle the problem of monitoring a changing environment. In nature, some animals face similar problems when foraging for food, and the main objective is to retrieve the most profitable food resources by considering various restrictions, including energy. In order to communicate the findings obtained through reconnaissance to other animals, several species use indirect communication such as segregation of pheromones. Data Foraging is related to the selection of profitable data sources in a dynamic environment with mobile sensors. Many approaches related to reconnaissance with mobile sensors have been proposed, especially in robotics, where the main objective is to find an optimal path to maximize the knowledge over a particular area [3; 4; 5; 6; 7; 8]. Finding a single optimal reconnaissance path is not suitable for data foraging, particularly when an operational environment with highly changing attributes is considered, and where the objectives may change dynamically. In this sense, Data Foraging-Oriented Reconnaissance (DFORE) requires establishing multiple dynamic paths to ensure that a profitable data source can be identified. Figure 1 depicts how a group of ants performs the exploration and the exploitation of their environment. In some species, food collection is achieved by thousands of workers travelling along well-defined foraging trails. These trails emerge from a succession of pheromone deposits that can result in a complex network of interconnected routes [9]. To perform DFORE, a UAV searches for points of interest in an unknown environment, as depicted in Figure 1. Through various trips, the UAV can identify a region which has something of interest to the application. Both systems are dynamic; therefore, several paths must be explored in order to exploit useful resources. In this work, we propose a Data Foraging-Oriented Reconnaissance algorithm for a single aerial vehicle. Inspired by the stigmergy principle, aerial vehicles communicate indirectly through an artificial pheromone to create several paths and to explore the operational environment with limited movement capabilities; thus, the focus of the research is how these devices through indirect communication can do a reconnaissance task. A hexagonal grid to represent the operational environment is used.