Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Pheromone-Based Methods <s> In this paper, we study a simple means for coordinating teams of simple agents. In particular, we study ant robots and how they can cover terrain once or repeatedly by leaving markings in the terrain, similar to what ants do. These markings can be sensed by all robots and allow them to cover terrain even if they do not communicate with each other except via the markings, do not have any kind of memory, do not know the terrain, cannot maintain maps of the terrain, nor plan complete paths. The robots do not even need to be localized, which completely eliminates solving difficult and time-consuming localization problems. In this paper, we use real-time heuristic search methods to implement ant robots and present a simulation study with several real-time heuristic search methods to study their properties for terrain coverage. Our experiments show that all of the real-time heuristic search methods robustly cover terrain even if the robots are moved without realizing this, some robots fail, and some markings get destroyed. These results demonstrate that terrain coverage with real-time heuristic search methods is an interesting alternative to more conventional terrain coverage methods. <s> BIB001 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Pheromone-Based Methods <s> The use of digital pheromones for controlling and coordinating swarms of unmanned vehicles is studied under various conditions to determine their effectiveness in multiple military scenarios. The study demonstrates the effectiveness of these pheromone algorithms for surveillance, target acquisition, and tracking. The algorithms were demonstrated on hardware platforms and the results from the demonstration are reported <s> BIB002 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Pheromone-Based Methods <s> Insect colony inspires scientists for years to create similar behavior in the robotic application. The main goal of our work was to develop simple and powerful algorithm which will accept dynamically changes in the size of a robot swarm due the mission. This algorithm is suitable for situations where unpredictable conditions may lead to robot fault in multi-robotics system and mission completion is endangered. In this article we would like to investigate properties of a simple pheromone based algorithm. The algorithm operates as cellular automata and partially uses an insect pheromone strategy for the robots coordination. Our abstract model is a decentralized adaptive system with a shared memory which represents the environment. <s> BIB003 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Pheromone-Based Methods <s> Ant meeting problem is a distributed problem that its purpose is to find an efficient path for ants to meet each other. Actually, ant robots suffer from the low memory and their restricted computational power. They communicate with their environment by leaving some symptoms like pheromones in their surroundings. In the problem of ants meeting, the ants should reach together in order to cooperate in an intelligent algorithm. In the common type of this problem, they start to search the environment without any knowledge about their locations, directions, obstacles and any other information. They can just leave some signs on the environment which shows their tracks. These marks are the only way of communication between ants. Also, these marks show the information about the ant which has passed the track, and every ant knows whose mark is that. In this paper, a new heuristic approach is proposed for the distributed ants meeting problem based on A* algorithm which guaranties the ants meeting in a finite time. Experimental results show that the presented algorithm is more efficient than previous ones. <s> BIB004 </s> Survey on Coverage Path Planning with Unmanned Aerial Vehicles <s> Pheromone-Based Methods <s> This paper presents control strategy for a group of multi-rotor unmanned aerial vehicles (UAV). Descripted biologically inspired method was developed to solve exploration or monitoring tasks. The proposed solution was tested in real environment on a quad-rotor UAV platform. Our abstract model is a decentralized adaptive system with a shared memory which represents the environment. <s> BIB005
Pheromone-based methods alternate between planning and execution, allowing a fine adjustment when performing such operations. They are inspired by the actions of real ants that employ chemical tracks to orientate navigation. These methods represent the workspace as a grid and use an u-value associated to each environment cell. This value represents the pheromone marks left by the vehicles moving through the scenario, i.e., the quantity of visits on each location BIB001 . Pheromone can be inserted in and/or taken off from a local, evaporate through time and/or be propagated to the neighborhood, according to the adopted strategy. Different flavors of pheromone can represent different kinds of information, while some types of pheromone can attract or repel vehicles. The field of view of the vehicles is usually restricted to the adjacent cells, enabling sensing and motion actions only in the cells immediately orthogonal, as shown in Figure 25a . The scenario may be discretized as a connected graph during the search, as illustrated in Figure 25b . The versatility of some of these approaches to deal with different applications in a military context is discussed by Sauter et al. BIB002 . These methods have been proposed to solve CPP problem with land vehicles. As these methods present a low computational cost, any autonomous vehicle may employ them appropriately BIB001 . However, most of the previous studies are limited only to simulations BIB003 BIB001 BIB004 and just a few methods have addressed contexts with aerial vehicles in real-world scenarios  BIB005 . 