# OADC: An Obstacle-Avoidance Data Collection Scheme Using Multiple Unmanned Aerial Vehicles
## 4 Algorithms
### A Genetic-Algorithm-Based Path Planning with VRAOA (GA w/VRAOA)
#### 4.2.3 Selection, Crossover, and Mutation Operators
Each UAV needs to travel more when the number of sensors increases. In addition, the average latency gap becomes higher with the increasing number of sensors because UAVs spend more time collecting data from a larger pool of sensors. However, GA w/VRAOA performs better in all situations. For example, in GA w/VRAOA with 80 sensors, the average data delivery latency is approximately 2.92%, 6.62%, 8.05%, 19.45%, and 24.07% lower than in GA w/COA, ACO w/VRAOA, ACO w/COA, Greedy w/VRAOA and Greedy w/COA, respectively. Figure 8c shows the energy consumption gap (standard deviation) among UAVs, where the energy consumption between UAVs is more balanced in the case of GA w/VRAOA than in others. With an increasing number of sensors, the energy consumption gap changes when the number of obstacles is fixed. In the OADC scheme, the energy consumption of UAVs depends primarily on their travel energy consumption. Thus, the traveling energy consumption of UAVs depends on the sensor positions, obstacle positions and size, and the clustering of the sensors. Therefore, some UAVs need to travel more than others. In this case, the energy consumption between UAVs becomes unbalanced. For example, in the case of 100 sensors, the energy consumption gap trend shows higher values than other simulation values. However, in the proposed GA w/VRAOA, the energy consumption gap between UAVs is lower than in other comparing algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Unmanned Aerial** & **Trajectory Length (km) by** & **Trajectory Length (km) by** \\
**Vehicles (UAVs)** & **GA w/VRAOA** & **GA w/COA** \\ \hline UAV1 (Sky blue colored trajectory) & 18.359 & 18.909 \\ UAV2 (Green colored trajectory) & 13.904 & 14.224 \\ \hline Total travel distance & 32.263 & 33.133 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Trajectory length of the UAVs. #### 5.4.2 Effects of the Different Numbers of UAVs

The performance of GA w/VRAOA is analyzed for multiple UAV counts, ranging from 2 to 10. Figure 9a shows that the energy consumption of UAVs is higher with the increasing number of UAVs. When the number of UAVs increases, the total number of clusters in the environment also increases. Thus, the total traveling distance of all UAVs increases because each UAV starts and ends the tour to the BS, whereas the BS can be far away from the sensors. Among all the algorithms, the GA w/VRAOA consumes less energy than the others. For example, in the case of eight UAVs, GA w/VRAOA uses \(3.68\%\), \(4.89\%\), \(10.00\%\), \(19.48\%\), and \(26.19\%\) less energy than GA w/COA, ACO w/VRAOA, ACO w/COA, Greedy w/VRAOA, and Greedy w/COA, respectively. Figure 9b compares the average data delivery latency for all algorithm variants for various numbers of UAVs; the GA w/VRAOA shows better performance compared to the others. Here, the average data delivery latency decreases as the number of UAVs increases. When the number of UAVs increases, each UAV needs to travel less to collect data from the same number of sensors in the same number of obstacle-existential areas, which reduces the average data delivery latency. For instance, when there are four UAVs, the average data delivery latency of GA w/VRAOA is \(475.20\) s, which is lower than those of the others, whereas the values from GA w/COA, ACO w/VRAOA, ACO w/COA, Greedy w/VRAOA, and Greedy w/COA are \(486.06\), \(512.38\), \(516.79\), \(538.51\), and \(568.67\) s, respectively. Moreover, in Figure 9b, the average data delivery latency slope becomes moderate as the number of UAVs or clusters increases. The slope reduces gradually after four UAVs, whereas the slope is acute before four UAVs. When the number of UAVs increases (e.g., from 4 to 6, 4 to 8, or 4 to 10), the number of sensors visited by each UAV and the traveling distance between UAVs become close to each other, resulting in a moderate slope.