## 2 Related Work
### Manual Design of Robot/LAV Swarms  
The literature contains a variety of studies based on path planning to manually design the behaviour of robots or UAVs, including in the context of coverage as surveyed in [PERSON] et al. [7]. The idea is to compute the optimal path of robots/UAVs offline, i.e., before starting the mission. In some works, these precomputed paths may be updated or recomputed. For example, [PERSON] and [PERSON] [8] recalculates Vehicle Routing Planning (VRP) when UAVs detect a threat on their way. Additionally, the problem of path planning is often formulated as a variant of VRP. It is also the case of [PERSON] and [PERSON] [9] who solves a problem of area coverage (differently from the problem tackled in this work). These path planning techniques are not comparable to the work proposed in this article. Indeed, most of these studies do not fit the above definition of a swarm. Nevertheless, with path planning techniques, UAVs follow the path calculated beforehand, which implies strong control from a human operator. Moreover, these techniques are usually exact methods and/or metaheuristics that are too expensive for online usage.  
Another way to manually design robot/UAV swarms considers online techniques based on direct or indirect communication between robots/UAVs. Some of those are based on line-of-sight communication. The work of [PERSON] et al. [10] consists in chaining a swarm of robots between two points where the robots have different colours according to the direction of the chain. [PERSON] et al. [11] propose a communication system for making a swarm of robots reach an unknown target point. However, when dealing with collective exploration, the vast majority relies on a stigmergy process such as pheromone-based systems. Some work focus on the design of the swarm system [12; 13], which abstracts from the application, i.e., the collective task. The idea is to design a pheromone system as close as possible to how pheromones would evolve in nature. These pheromone systems are used to investigate the impact of using multiple pheromones in the context of a collective task [14; 15].  
Most of the literature, meanwhile, deals with task-specific systems, i.e., how to use the pheromone for the desired collective task. [PERSON] and [PERSON] [16] first introduced a pheromone-based system to optimise the coverage of an area by a swarm of UAVs. In that work, UAVs have to choose a direction according to the amount of repulsive pheromones left by other UAVs. [PERSON] et al. [17] proposed an extension of the latter work where the random aspect is replaced by a chaotic system. The objective is to obtain a movement that seems unpredictable from the outside while being deterministic. [PERSON] et al. [18] combined the work of [PERSON] and [PERSON] [16] with a multi-hop clustering approach to consider the connectivity of the swarm during the coverage task. The added connectivity objective aims to maintain a good exchange of information within the swarm and, thus, improve its performance. [PERSON] et al. [19] extended the previous work with a dual-pheromone model to tackle three objectives of the swarm: area coverage, swarm connectivity and target tracking.  
Figure 1: Classification of swarm behaviours proposed by [PERSON] et al. [5] and extended by [PERSON] et al. [6].  
Although such pheromone-based approaches have shown good results, [PERSON] et al. [20] show that they face limitations, particularly when the density of the swarm is very high (the number of robots/UAVs relative to the size of the area). Added to this limitation, such a pheromone-based behaviour has to be designed manually, which can be very time-consuming and specific to an application. For instance, the pheromone evaporation rate or the balance between different pheromones (in the case of using multiple types) may differ according to the emergent task of the swarm.