# Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications
## 5 Basic Behaviors and Tasks in Swarm Robotics
### Source Search  
Swarm robotics can be extremely helpful in search tasks, mainly those in that the source's spatial model can be complicated as in the case of sound and odor. To determine the miniature robot's orientation with many infrared sensors in a swarm robotic system, [PERSON] et al. [125] presented a mathematical model of the behavior of orientation supported on existing positioning models, aiming to improve positioning precision when employed in the practical location. The resultant mathematical model is based on optimization to enhance exactness. The characteristic structure is to order the weights for particular independent variables. Later, use the genetic algorithm (GA) to obtain the optimal values of those proper to remove the positioning error. The results demonstrated that the suggested location model is efficient and thriving to discover a neighboring robot's exact orientation in a system with several robots.  
[PERSON] e [PERSON] [126] presented a new technique for researching a feeble and rowdy signal and finding its found using a group of mobile robots. According to the authors, the robots move in a circular composition and swap knowledge to collaboratively predict the signal gradient grounded on noisy mensuration, considering the communication limitations. The training center follows an associated random walk, which guarantees excellent space-filling qualities to investigate a reliable signal strength region successfully. To solve the exploration and the exploitation problem, the assumed gradient plays acts as a trend of distribution probability of the unusual guidance walk, permitting the group a soft transition amid the two techniques and improving the robustness concerning a certain change. The authors claim results in experiments that prove the success of the approach. [PERSON] et al. (2017) employed the repulsion method between homogeneous ions as motivation to define a recent robotic target search technique that was analyzed using the RDPSO algorithm (Robotic Darwin PSO). The suggested procedure presents various significant benefits for the robotic research of targets and the ability to offer a great heterogeneity among robots; thus, the suggested procedure can solve the problem of slow convergence rate and, in complement, it can prevent local optima. The suggested technique aims to restrict the converging search region; this conducts to an excessive exploiting ratio. These characteristics conduct to a powerful robotic search algorithm. Several experiments were carried out to investigate the performance of the proposed method. Experimental results show the supremacy of the suggested method in contrast to other research techniques, particularly when there is a small group of robots.  
The problem of searching multiple odor sources is addressed in the paper of [PERSON] et al. (2018). In this work, the authors presented an approach based on div-PSO. To obtain superior coordination, actions such as group formation, disassembly group, fusing group and dimensions constraints were used. According to the authors, the results indicate the performance improved even more with the approach supported by div-PSO. The plume division is simulated in the 3D environment by the Ansys Fluent software. The simulated responses are introduced into MATLAB and used to quantify the convergence of odor source. The performance of the suggested div-PSO method is superior to the niche-PSO, R-PSO, M-PSO methods in the three divergent environments, as it supports preserving variety and decreases the possibility of stagnancy in local optima. Furthermore, the use of artifices in the proposed div-PSO approach is more satisfactory since it uses the group destruction operator. In addition, if the robots get stuck in the optimal location, the search counter's design will help them escape from the optimal location area.