# Real-Time Swarm Search Method for Real-World Quadcopter Drones
## 1 Introduction  
The demand for autonomous aerial vehicles, commonly called drones, has largely increased in recent years due to their compactness and mobility, which enable them to carry out various tasks that are economically inefficient or potentially dangerous to humans. For example, it is not easy for humans to explore rugged mountain terrains, flooded areas, or air pollution regions without drones. Consequently, they have been extensively employed in various search applications, such as industrial building inspections [1; 2], search and rescue operations [3; 4; 5], and post-disaster area exploration [6; 7; 8].  
The search applications have one important factor in common: search efficiency. Previous research has focused on improving the stand-alone performance of each drone, such as localization accuracy, communication robustness, and various sensors [9]. However, it is relatively expensive to employ a group of such high-end drones. Additionally, it takes a long time for a drone or a few drones to cover a broad search space. Thus, previous studies have tried to decompose the search space [10] or control a number of low-cost drones into several formation patterns [11; 12].  
Despite the previous research successfully demonstrating the feasibility of search-by-drones, there is still room for improvement. Most of all, considering time and cost, it is not the best strategy to thoroughly scan every available location in the search space. In other words, it is more effective for drones to conduct a brief survey first and successively progress to better locations by investigating the evidence of the surroundings and communicating with each other. We can easily find examples of this kind of strategy from nature, such as ants, bees, fish, birds, and so on. They show cooperativeand intelligent behaviors to achieve complex goals, which is called swarm intelligence [13; 14; 15; 16]. In fact, in the area of multi-robot path planning in 2-\(D\) space, there have been several studies of approaches based on swarm intelligence [17; 18]. However, there is a crucial difference between mobile robots in 2-\(D\) space and drones in 3-\(D\) space. Whereas mobile robots can stand stably without any posture control and only need to be controlled by position feedback, the postures and positions of drones should be carefully controlled based on a certain dynamic model in order to hover stably.  
Therefore, in this paper, a novel swarm search method for quadcopter drones is proposed by integrating the position update rule of the swarm intelligence algorithm and the motion controller using a dynamic model of the drones. In the proposed method, a swarm of more than 10 drones was employed for a search mission. The swarm was controlled by a position update mechanism which included the swarm intelligence inspired from a well-known swarm-based optimization algorithm. In addition, a dynamic model for the drones was applied to the mechanism since real-world drones, in contrast to the individuals in the optimization algorithm, have physical limitations such as maximum speed and maximum acceleration. Moreover, the overall mechanism was processed in real-time along with the movements of the drones.  
To verify the effectiveness of the proposed method, the overall procedure was implemented as a simulation and repeatedly tested. As the test problems, Rosenbrock function optimization and air pollutant search problems were employed. The Rosenbrock function is a well-known benchmark function for numerical optimization. The air pollutant search problem was designed by modeling atmospheric dispersion though a Gaussian air pollutant dispersion equation. Additionally, the results of the proposed method were compared to those of a conventional grid search method.  
This paper is organized as follows. Section 2 explains the proposed methodology in detail. In Section 3, the experimental results are demonstrated. Finally, Section 4 presents conclusions.