# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Metaheuristic Algorithms-Swarm Intelligence
#### 3.1.2 Particle Swarm Optimisation (PSO)  
Particle Swarm Optimisation is one of the swarm algorithms in artificial intelligence developed by [PERSON] and [PERSON] to simulate birds' flocking behaviour [25] graphically. It is a stochastic technique that facilitates robot movement in a swarm [26]. The Particle Swarm Optimisation algorithm differs from others because its functionality solely depends on the objective function. It does not rely on differential objectives or gradients in flying formation [27]. A flock of birds that move in groups benefits from the experience of each flock member. For instance, when a flock of birds flies randomly in search of food, they increase the chances of each bird in the community getting the best hunt [28; 29]. The Particle Swarm Optimisation algorithm was designed to assist in solving optimisation problems in both the local and global space [30; 31]. However, each robot utilises the Particle Swarm Optimisation algorithm to navigate the terrain or area in a semi-autonomous manner that is distinct from other robots in the swarm. This allows the robot to solve the problem and navigate independently while functioning as a swarm [32; 33]. PSO is a control strategy for a swarm robotic system. Many developed PSO algorithms have been presented and proposed for this purpose and used for area coverage problems as follows:Ref. [34] presented five robots with an XBee module for the problem of seeking electromagnetic sources by area coverage by modifying and implementing the PSO algorithm on mobile robots according to the physical constraints presented by both robots and the environment. Three different PSOs were evaluated by simulations encompassing PSO with Inertia Weight, PSO with Constriction Factor, and Standard Particle Swarm Optimization (SPSO). The area covered was 5 \(\times\) 5 m\({}^{2}\), and applying the Vicon tracking system gave the information of each robot's precise position via the support of an indoor GPS system to recognise markers on the robots. The type of robots used with an XBee module in experiments improved from the Parallax Shield-Bot, which Arduino controls. These robots search for the target source, a module of XBee hanging above a floor at a height of 20 cm in the middle of the area, to avoid a probability collision with the Robot. Also, the avoidance strategies for static and dynamic obstacles occurred in PSO in this experiment. The results proved that the best and most convenient diversity of PSO is the Inertia Weight PSO for this work. Ref. [35] presented a novel method approach that depends on Robotic Darwinian Particle Swarm Optimization (RDPSO), Probabilistic Finite State Machine (PFSM), and Depth First Search (DFS). This new approach was proven to decrease the time needed by the AntBOT swarm to explore the area coverage environment. The simulator is V-REP, with different layouts, and three various environment areas are used (25 m\({}^{2}\), 100 m\({}^{2}\), and 400 m\({}^{2}\)). The results indicated that the speed of exploration increases with a combination between RDPSO and PFSM by at least 1.4x the rate of the individual algorithms. In addition, it enhances the motion of robots in the environment by allowing smaller sizes for swarm robots to process exploration and rescue at a minimum cost. Ref. [28] dwelled upon the description of implementing Robotic Darwinian Particle Swarm Optimization (RDPSO) for search and rescue in a particular area coverage. A Robot Operating System (ROS) and Gazebo simulator with Rviz were used. Rviz is a ROS visualisation tool for visualising sensory data, such as camera data, distance measuring device data, GPS information, etc. The experiments were performed for the rescue operation in two cases, one for two static victims by four robots and another for four static victims, using eight robots, with 30 trials for each patient. The simulation results indicated that RDPSO, compared to RPSO, performs better for multiple target search and rescue (SAR) operations. Ref.