# Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory
## 4 Simulation Results
### Variation in the Number of Agents
Since these tracker locations are the only available information, it is unknown which animal belongs to which herd and how many animals are there in each herd. The received tracker information is presented in Table 1 for the OT (TV-Gauss) method in Figure 6a-c. For the OT (TV-Gauss) method, the estimated animal herd center and tracked animals that belong to each herd number are given in Table 1 as well according to the proposed policy in Section 3.3. Figure 6: _Cont_. We generated more numbers of sample distributions than the estimated numbers of animal herds. This is mainly because each herd location is unknown to the UAVs when they've arrived at the monitoring region and thus, more sample distributions with the proposed sample propagation method can better estimate the possible location of animal herds. If animals are detected by the UAV, then the sample distribution is relocated (both mean and covariance of the Gaussian) for the correction. Based on the proposed scheme for the OT (TV-Gauss) in Figure 6a-c, the UAVs detected total 53 animals out of 142 (detection rate: 37.32%). For LM (TI-Uni), a time-invariant rectangular monitoring area is obtained from (11) and (12) by using the tracker information. The agents start the monitoring mission from the locations determined by (13) after a 600 s time delay. The agents explored their assigned areas in a zigzag manner and finished the exploration 900 s after the monitoring started. The detection rate for LM (TI-Uni) in Figure 6d-f was 10.56%. In the case of OT (TI-Uni), the detection rate in Figure 6g-i was 25.35%. To better compare the performance of each method, the statistical results for 30 simulation runs are presented in Figure 7, where the initial locations of detected animals were randomly generated in each run. The average detection rates for different numbers of agents are also presented in Table 1 to provide a better understanding of the effect of the number of UAVs on the exploration performance. Although it is observed that the performance of all the monitoring strategies has gradually improved by increasing the number of agents, the performance increase of LM (TI-Uni) and OT (TI-Uni) are less significant than OT (TV-Gauss). For all three scenarios in Figure 7, the proposed method OT (TV-Gauss) outperformed the other two methods. Notice that for all three scenarios, the UAVs had the same energy level (or alternatively the same UAV points) in the beginning for the fair comparison, however, the detection rate for the OT (TV-Gauss) method significantly overwhelmed the other two. Thus, it is verified that the proposed method is able to monitor wildlife effectively as the scheme can take into account the time-varying nature of wildlife locations in the monitoring plan and explore areas accordingly. Figure 6: Snapshots of different monitoring strategies for the 2-agent case: (**a–c**) OT (TV-Gauss); (**d–f**) LM (TI-Uni); (**g–i**) OT (TI-Uni). The negative time indicates the time to travel from the base station to the pre-specified location before the initiation of the monitoring mission. The monitoring mission starts at \(T=1\) s and continues until 900 s.