# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 5 Conclusions
### Summary and Future Work  
In this work, we proposed the four mobility models with overlap avoidance for reconnaissance based on multiple UAVs. The proposed models improved the performance of the scan coverage rate and scan uniformity in preventing collisions of UAVs. They are designed by applying overlap avoidance to the existing models of RW, RMP, EGM, and DPR. In the simulations, the 90%-coverage rate reaching times of RWOA, RMPOA, EGMOA, and DPROA are improved by 18.1%, 14.4%, 19.2%, and 10.6%, respectively. We also showed the balanced reconnaissance of proposed models by comparing the number of regions per inter-arrival time and average inter-arrival time. In this paper, we focused on redesigning representative mobility models by adding overlap avoidance schemes. Thus, a novel mobility model is needed to improve performance with consideration of overlap avoidance.  
Our ongoing work will cover the mobility models for reconnaissance with heterogeneous UAVs. When a mission planner performs tasks, it may not have UAVs with the same specification such as camera area, maximum speed, turn radius, etc. We are planning to simulate models by assuming this scenario. Another practical issue is efficient reconnaissance of irregular area. Since the current work considers a rectangular area, we need to analyze the performance in an irregular area and design a suitable mobility model. Moreover, we are planning to design a new mobility model for reconnaissance applied with target tracking, scouting critical area, etc.  
[PERSON]-i.J. and [PERSON] proposed the mobility model. [PERSON]-i.J. implemented the model, conducted the experiments, and wrote manuscript under the supervision of [PERSON]. [PERSON] assisted and performed model comparison experiments. All the authors have reviewed and revised final manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported partly by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Ministry of Trade, Industry and Energy (No.20194030202430), and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant NRF-2018R1D1A1B07050093).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**Symbol** & **Description** \\ \(p_{c}\) & the center point \\ \(x_{c}\) & the x-coordinate of the center point \\ \(y_{c}\) & the y-coordinate of the center point \\ \(u_{i}\) & the location of a UAV \\ \(U_{o}\) & the set of UAVs in an overlapped area \\ \end{tabular}  
the number of UAVs in \(U_{o}\)  
\(\theta_{i}\) the angle from the \(u_{i}\) toward central point  
\(s_{t}\) the speed of the UAV  
a \(x\) the weighted value between 0 and 1  
\(\overline{s}\) the mean speed deviation  
\(s_{x_{t-1}}\) the random Gaussian distributions by \(N(0,\sigma^{2})\) for speed  
\(d\_{dev_{t}}\) the direction deviation  
\(d_{t}\) the next direction  
\(d\_{dev_{x_{t-1}}}\) the random Gaussian distributions by \(N(0,\sigma^{2})\) for direction  
\(L_{p}\) the number of pheromones in left scan range  
\(C_{p}\) the number of pheromones in center scan range  
\(R_{p}\) the number of pheromones in right scan range  
\(T_{p}\) the sum of \(L_{p}\), \(C_{p}\), and \(R_{p}\)  
\(\psi_{i}\) the angle between the current direction and the guidance vector  
\(\beta_{L}\) the weighted value added to straight ahead and turn right  
\(\beta_{R}\) the weighted value added to straight ahead and turn left