## 6 Experiments and Results
### Experiments  
Three experiments were performed to analyze the effectiveness and robustness of the proposed algorithm. Figures 9 and 10 show the results of the experiments 1 and 2, respectively. In both experiments, the UAV starts with a deterministic navigation from the point \(P0\) to \(P1\) and uses the information of the binary sensor and winds effects to construct and update the LMLS, which was initiated as uniform. In these trajectories, the binary sensor did not detect the pollutant concentrations, but the information is useful to generate the initial heuristic position of the source \(h_{0}\). From the position \(P1\), the UAV is guided by the proposed strategy combining the metaheuristic component, to trace the increasing direction of the pollutant concentration, and the probabilistic component which helps the UAV to remain in the areas with high probability to find the pollutant source. In the experiment 1, the pollutant is released in the position \((Z,X)=(180,180)\) cm and the algorithm calculates the source position at \((Z,X)=(179.1,180.1)\) cm. In the experiment 2, the pollutant is released in the position \((Z,X)=(20,180)\) cm and the algorithm determines the source position at \((Z,X)=(20.29,180.7)\) cm. Figure 8: Indoor experimental test environment. Figure 11 shows experiment 3. In this experiment, the winds effects were increased, mimicking more complex environment. The pollutant was released in the position \((Z,X)=(170,50)\) cm and the algorithm calculated the source position at \((Z,X)=(168.2,45.62)\) cm. As shown, the algorithm has high effectiveness. In addition, the parameters of the algorithm have not been changed for the different experiments, that is, the algorithm is robust. The real UAV showed high variation in the path while the vehicle was driven among the waypoints. Despite this fact, the vehicle was able to approach very close to the source. Figure 11: Experiment 3. Mission for the pollutant source localization with high wind effects. At the left are the LMLS updated at the times \([123.44,127.04,136.64,137.44,146.04,158.44]\) seconds (left to right and top to bottom). Figure 10: Experiment 2. Mission for the pollutant source localization with different source position. At the left is the source localization mission using the Qball. At the right are the Likelihood Map for the Location of the Source (LMLS) updated at the times \([104.9,111.1,121.7,126.7,126.4,142.3]\) seconds (left to right and top to bottom). Figure 9: Experiment 1. Mission for the pollutant source localization. At the left is the source localization mission using the Qball. At the right are the LMLS updated at the times \([105.4,112.6,122.3,122.9,126.1,139.3]\) seconds (left to right and top to bottom). The source location was estimated on each time, using the information of the visited positions and the heuristic calculated from the LMLS. This is useful for applications in environments where the source position is not reachable by the UAV but could be estimated by the algorithm. The waypoints resolution was set to 30 cm, given the environment and vehicle dimensions. This restriction complicates the searching since when the metaheuristic algorithm finds the direction of the positive gradient of pollutant, it follows this direction to calculate the new waypoint with the defined resolution and the vehicle has high probabilities of leaving the plume of pollutant. On the other hand, this minimum resolution is useful since a path planning algorithm can be used to operate the UAV in an environment with static or dynamic obstacles. As the algorithm was based on two independent approaches, the minimum waypoints resolution was outperformed successfully. The heuristic position aided maintaining the exploration in the most promising areas while the metaheuristic facilitated the exploration the increasing gradient of pollutants in these areas. The algorithm implementation requires the tuning of some parameters. The calculus of the LMLS includes the parameters: \(L_{x}\), \(L_{z}\), \(\sigma_{x}\), \(\sigma_{z}\) and \(\mu\).