# 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
Figure 8: Effects of the number of sensors: (**a**) effect on the total energy consumption of UAVs, (**b**) effect on average data the delivery latency, and (**c**) effect on the energy consumption gap (standard deviation) among UAVs. The energy consumption gap among UAVs is illustrated in Figure 9c, for which the number of UAVs is considered from 2 to 10. In Figure 9c, the energy consumption gap trend changes as the number of UAVs increases. The reason is that in the considered environment, there can be large-sized obstacles in some UAV paths, whereas the other UAVs may not need to avoid any obstacles or may avoid some small-sized obstacles. For this reason, some UAVs need to travel more to collect data from a given number of sensors than others, which causes some UAVs to consume more energy. However, the energy consumption gap among UAVs is lower in the case of the GA w/VRAOA among all the algorithms. #### 5.4.3 Effects of the Number of Obstacles

The effects of the number of obstacles for all algorithm variants are analyzed here. The considered obstacles in the environment are numbered 2, 4, 6, 8, and 10, while other simulation values are constant. Figure 10a shows that the energy consumption of UAVs increases as the number of obstacles increases. When the number of obstacles increases, UAVs need to travel more to cross the obstacles in the environment. Therefore, UAVs need more energy to collect data. For instance, when there are 10 obstacles in the environment, the total energy consumption levels for UAVs with GA w/VRAOA, GA w/COA, ACO w/VRAOA, ACO w/COA, Greedy w/VRAOA, and Greedy w/COA are 14,950.82, 15,517.22, 15,774.02, 16,273.22, 16,514.42, and 16,954.82 KJ, respectively. Furthermore, in all cases, the GA w/VRAOA outperformed the other algorithms. In Figure 10b, the average data delivery latency is presented. The average data delivery latency becomes higher as the number of obstacles increases. If there are more obstacles in the UAVs' trajectory, then the UAVs need to spend more time on data collection. Thus, the average data delivery latency increases as the number of obstacles increases. Furthermore, the GA w/VRAOA performs better than all algorithm variants. For example, in the

Figure 9: Effects of the number of UAVs: (**a**) effects on the total energy consumption of UAVs, (**b**) effects on average data delivery latency, and (**c**) effects on energy consumption gap (standard deviation) among UAVs. case of eight obstacles, GA w/VRAOA requires 4.08%, 5.33%, 7.09%, 11.55%, and 19.88% less average data delivery latency than GA w/COA, ACO w/VRAOA, ACO w/COA, Greedy w/VRAOA, and Greedy w/COA, respectively. Figure 10c illustrates the energy consumption gaps among UAVs for a given number of obstacles, for the different algorithms. In the proposed OADC scheme, the size and shape of the obstacles can be very different, and the number of obstacles UAVs encounter also differs. Therefore, it is expected that the traveling distance and energy consumption of UAVs differ from each other, and the energy consumption gap among them increases, which can be seen in Figure 10c. The energy gap is lowest and highest when the number of obstacles is two or ten, respectively. For instance, in the case of GA w/VRAOA, when the number of obstacles is 2, the energy consumption gaps among UAVs are about 17.20%, 18.17%, 31.89%, and 39.90% lower than when the number of obstacles is 4, 6, 8, and 10, respectively. In addition, in the case of GA w/VRAOA, UAVs travel less than with the GA w/COA, which causes less energy consumption of the UAVs for GA w/VRAOA. Furthermore, in Figure 10c, the energy consumption gap between the greedy (Greedy w/VRAOA, Greedy w/COA), ACO (ACO w/VRAOA, ACO w/COA) and GA (GA w/VRAOA, GA w/COA) becomes higher as the number of obstacles increases. When the number of obstacles increases, the solution space for the problem also increases. The greedy method choose the local optimal solution at each step based on the current knowledge, which caused it to fall into a sub-optimal solution.