# Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications
## 5 Basic Behaviors and Tasks in Swarm Robotics
### Task Allocation  
Task Allocation is a decision method, wherever the agents chose from a set of alternatives, which job each one will perform. The task allocation problem can appear in multi-robotic systems and swarm robotics. [PERSON] et al. [121] studied the use of task allocation in swarm robotics. The authors examined task decomposition graphs on several levels allowing swarms to use available caches and complicated task decomposition graphs, allowing generate/eliminate caches. They presented the task decomposition graph development is connected with swarm intelligence advent and conduct for varied task allocation techniques in an object collecting task. The authors also investigated how the advance of swarm intelligence emerges of knowledge and investigation of graph form instead of graph composition (costs of nodes).  
[PERSON] et al. [122] presented a powerful approach of task allocation algorithms to swarm robots to perform tasks subject to special time limitations. The strategy is a variant of the famous Ant Colony Optimization method for local computing and upgrade of the pheromone routes using knowledge obtain single robot when performing its task. The authors affirm this work is an original approach to use a system to optimize task allocation. The experiences examined in this work prove that an adequate strategy coordinates the swarm to carry out its designated tasks within specific deadlines, both static and dynamic simulated environments. [PERSON] et al. [123] presented a foraging task where a set of robots must collect food particles and transport them to the central nest. Robots transport food to a collective storage region called a cache area (clipboard), and storage robots take food from the collective storage region to the central nest. The authors created a dynamic task assignment method to measure robots' share proportionally to task requests. According to the authors, the collaboration of the article is to make it possible to change the efficient local task of individual robots with the response threshold model, but without communication, performing, in an extreme way, the designation of the tasks desired for a cluster and presenting the convergence mathematics of the assignment of tasks. The authors describe it as a self-structured procedure for dividing a task into sequentially interdependent subtasks and determining individuals in a cluster to perform the subtasks concurrently. They claim that this strategy is intended to decrease interference between individual robots since different worker models are more segregated, and the improved transport efficiency provides a better overall swarm functioning.  
[PERSON] et al. [124] presented a new adjustable, organized distributed control, allowing a little swarm of mobile robots to follow wide target clouds. According to the authors, distributed control was planned by distribution node control and distributed connectivity control to permit a robot flexibly to get along with movement limitations originated by local minimum topologies of local networks. The collaborative observation task, tracking, and launch algorithm of the collaborative workstations were added to the distributed control to render it adjustable to follow more than one target in ample destination clouds, ensuring the entire global network for the assignment of collaborative tasks. The authors confirmed and legitimized the control method using simulations and authentic analysis.