# Real-Time Swarm Search Method for Real-World Quadcopter Drones
## 2 The Proposed Swarm Search Method
### The Drone Position Update Mechanism
#### 2.1.2 The Dynamic Model of the Drones for the Mechanism
As shown in the figure, the drones successfully found the target position at which the pollutant was being emitting within 30 iterations. Note that the drones had no knowledge of the dispersion model, and could simply measure the air pollution concentrations at their positions. Figure 4: Screenshots of the simulation of Test Problem 1 by the proposed method. The full simulation videos for Test Problems 1 and 2 are provided through the YouTube links \"[[https://youtu.be/fIUmsO5B4](https://youtu.be/fIUmsO5B4) CA]([https://youtu.be/fIUmsO5B4](https://youtu.be/fIUmsO5B4) CA)\" and \"[[https://youtu.be/cdICZQeN-Bo](https://youtu.be/cdICZQeN-Bo)]([https://youtu.be/cdICZQeN-Bo](https://youtu.be/cdICZQeN-Bo))\", respectively. The videos show that the drones could find the global minimum or maximum under non-convex or near non-convex environments. The videos also show that the drones could be controlled stably in real-time, and the dynamic model was well-applied considering the real-world environment. Moreover, Table 2 shows the averages (\(AV\)Gs) and standard deviations (\(STD\)s) of the number of iterations \(n\) for the proposed and conventional methods to satisfy the corresponding termination condition (\(TC\)) about error distance \(d_{err}\). Since the search space was based on a real-world environment, the unit of distance was meters. If we consider the size of commonly-used drones (approximately 1.0 m), the minimum grid size for the conventional grid search should be greater than 1.0 m. Based on this condition, we can approve that the drone is close enough to the goal if \(d_{err}\) is less than 2.0, which is double the minimum grid size. Additionally, the \(AV\)Gs and \(STD\)s of the final \(d_{err}\) for the proposed and conventional methods with the limited number of iterations \(n_{limit}\) are shown in Table 3. Smaller values of both \(n\) and \(d_{err}\) are desirable, where \(n\) and \(d_{err}\) imply the speed and the accuracy of the methods, respectively. As displayed in the tables, the proposed method could find the target more quickly and more accurately and robustly than the conventional method. Note that the proposed method showed a more powerful result in the real-world problem (e.g., Test Problem 2) than the virtual problem (e.g., Test Problem 1). Figure 5: Screenshots of the simulation of Test Problem 2 by the proposed method. ## 4 Conclusions

In this paper, a novel search method for a swarm of quadcopter drones was proposed. In the proposed method, inspired by the phenomena of swarms in nature, drones could effectively look for better locations by investigating the evidence from the surroundings and communicating with each other. The position update mechanism was implemented based on the particle swarm optimization algorithm (a well-known swarm-based optimization algorithm), as well as the dynamic model of the drones, which was used to take the real-world environment into account. In addition, the mechanism could be processed in real-time along with the movements of the drones. The experimental results showed that through the proposed method, the drones could find the target more quickly and accurately than by the conventional algorithm. Most importantly, the proposed method has high practical potential, considering that the drones were simulated in real-time and the dynamic model sufficiently reflected the real-world environment. [PERSON] conceived and designed the methodology and experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] wrote the paper; [PERSON] and [PERSON] reviewed and edited the paper. This research received no external funding. This work was supported by the \"Research Grant of Kwangwoon University\" in 2017 and \"Human Resources Program in Energy Technology\" of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry&Energy, Republic of Korea (No. 20174010201620). The authors declare no conflict of interest. ## References

* [PERSON] et al.