## 1 Introduction and Related Works  
The availability of cheap mobile robots that can be deployed in large quantities has dramatically increased over the last decade. Robust and scalable algorithms are needed to control large groups of robots. The field of swarm robotics studies the coordination of large groups of relatively simple robots using simple local rules. It is inspired by the behavior of social insects that can accomplish tasks that are beyond the capabilities of any individual (see, e.g., in [1; 2; 3]). [PERSON] et al. presented a taxonomy of swarm robotic systems structured into several levels in [4]. According to this taxonomy, swarm robotic systems are cooperative, aware, strongly coordinated or weakly coordinated, and distributed. [PERSON] and [PERSON] summarized the basic behaviors and tasks solved in swarm robotics in [5] aggregation, dispersion, pattern formation, collective movement, task allocation, source search, the collective transport of objects, and collective mapping. Swarm robot behaviors can be categorized according to spatially distributed multi-robot movements [6]. Although there are some works dealing with heterogenous agent swarms [7], most of the algorithms operate with robots, which are of single type; therefore, the group or swarm is homogeneous and operates in a single environment with faultless communication channels. The ambition of the study is to create a unified algorithm, which will be able to operate in a set of environments using a custom swarm strategy to solve the coverage problem even with limited communication. The space to be covered is divided into cells and solving the coverage problem means visiting each cell by a robot at least once. Contrary to traditional approaches in swarm robotics and their control algorithms, we assume that the space to be covered comprises different environments, that the robots are of different types, and that each robot type has different capabilities. Covering an unfamiliar space is difficult and usually not solved by other algorithms, especially if the environment is dynamic. We also assume that the trajectories of the individual robots should not be easily predictable. Difficult predictability for the robots' trajectories is greatly advantageous for applications, such as patrolling. A combination of all these features in a single framework algorithm is a very perspective and novel approach, so far not published or developed in the known literature. The aim of the study is to prove that such algorithm can be designed and is able to explore different environments in finite time. A large body of research has investigated robot or multi-robot coverage and exploration problems. Many experimental and real life applications have been implemented in 2D [8; 9; 10], 2.5D [11; 12; 13], and 3D [14; 15] environments. Applications of the coverage path planning in domains, such as agricultural robotics [16] and unmanned aerial vehicles (UAVs) [11; 17; 18; 19], have also been described. Another approach that could be pointed out here is the centralized vs. the decentralized mode of the swarm with negotiation process applied to a survey task [7; 20]. The negotiation process enables each robot to find the best solution by reassigning subtasks through the process of finding the utility value of the specific sub-task. This solution used the Robot Utility Based Task Assignment (RUTA) algorithm. While in the case of decentralized control the robots broadcast their utility values for the sub-tasks and the best robots are selected [7], our solution use only the pheromone value to control all processes. A more comprehensive description and explanation of the existing methods together with a detailed classification of the existing approaches is given in [21]. It can be said that many approaches deal with the optimal space coverage methods. [PERSON] and [PERSON] used a spanning tree coverage algorithm for optimal 2D space exploration in [10].