# Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications
## 2 Swarm Behaviors  
In the previous section, we presented the context and primary objective of this work. In this section, we outline the concepts which inspire the creation of SR appliances. From termite mounds to fish schools, many social groups in nature live unitedly to survive and thrive. Similar swarm behaviors influence various computational procedures to answer problems and coordinate strategies for collective robotics. With the constant search for intelligent prototypes motivated by real-world methods, it was found that simple agents, who alone are unable to carry out trivial tasks, perform highly complex works when they act mutually as an organized automatic system [16]. In nature, this pattern is observed in several species such as bees in the composition, appears and building a hive, termites that make complicated tunnel systems, ants that can find paths when looking for food, birds flying in line in search of food, among others [17; 18; 19; 20]. Collective behaviors in swarms happen in almost each biological measure degree: they can be diverse from single-celled organisms [21] to the largest animals on Earth [22]. An essential feature of swarms is decentralization. A decentralized swarm is one in which complex behavior arises through the labor of autonomous agents acting on local information, not briefings of any imposing dominion [23]. In other words, there is no head in the swarm directing other members to perform the planned tasks.  
Usually, a swarm consists of a set of identical (or similar) members belonging to progressing in an asynchronous manner [1]. These individuals own single competencies compared to the whole group: they have restricted cleverness and cannot conclude the swarm aims without the rest of the group. Additionally, it has been proven that group members do not need any illustration or global understanding of the swarm to reproduce complicated collective behaviors. Surprisingly, the complexity of these collective behaviorsand structures does not reflect the relative simplicity of an insect's individual behaviors of an insect [24]. Swarm members do not know about the swarm's overall status of the swarm [25]. Usually, the entities that compose the swarm have small or simple individual capabilities. Communication among members is achieved only on a local basis. A descriptive instance is a flock of birds: birds in the flock can accompany a communal orientation in their displacement to travel thousands of kilometers to a defined target location. However, each bird is concentrated purely on its local neighbors.  
Decentralization is closely associated with another important feature: self-organization. Self-organization relies on combining the following four basic rules: positive feedback, negative feedback, randomness, and multiple interactions [4]. Nevertheless, when an ant discovers a possible count of food, it comes back to the colony, releasing some pheromone trace on the way back. Assuming other ants notice the pheromones, they also pursue the track to the food count and come back to the colony, dropping new pheromones, therefore consolidating this specific path. These pheromones' traces will disappear through time, decreasing the attractive strength. Shorter trails are less reached in brief periods by this evaporation procedure. Hence, they probably go through more repeatedly than extensive ones. Consequently, nature offers a remedy to the problem of discovering the shortest path between colony and food count. These collaborative behaviors provide means for computer scientists and engineers to create methods to resolve practical issues, working as \"bio-inspiration\".