## 5 Experimental Results
About the response time variable, the three proposed exploration algorithms take a longer time to perform the decision-making process compared to the non-intelligent approaches, as seen in Figure 19. This proves the stated regarding the information required  
Figure 18: Legend employed for the results of each algorithm testing. for the execution of each algorithm. Based on Figure 19, all the non-intelligent approaches are not different statistically; but, according to the time response; correlation filter, neural networks, and genetic algorithm are statistically different. The decision cost measures the average time needed to complete the task assigned by the exploration algorithm. Based on Figure 20, it is interesting to notice how the utility function optimized with a genetic algorithm is similar to nearest cell exploration. Furthermore, it is proved that the farthest cell exploration, and less covered and farthest cell exploration are the costliest decisions in terms of time and power consumption. The decision gain measures the average coverage area obtained when the robotic agent completes the task assigned by the exploration algorithm. Based on Figure 21, it is clear to see how the decisions taken using the correlation filter algorithm obtain the most coverage gain compared to the other algorithms, proving its capacity to maximize the use of global coverage area construction by each robotic agent. Figure 19: Cumulative average for response-time experiments. Box plot for response time experiments. Multiple comparison test for response time experiments. Response time ranking. The results obtained in the social interactions section define that an algorithm with a high rate of collision is not very sociable because their decisions affect their teammates. The utility function optimized with the genetic algorithm presents the lowest collision frequency, demonstrating that is it a sociable algorithm. The results presented in Figure 22 allow classifying the exploration algorithms by social levels. Figure 20: Cumulative average for decision cost experiments. Box plot for decision cost experiments. Multiple comparison test for decision cost experiments. Decision cost ranking. The coordination level (individual cost-time to complete the tasks) measures an algorithm ability to distribute the work evenly among robotic agents. Figure 23 shows that the algorithms that reach the farthest cell are the ones that present lower coordination. Again, the utility function optimized with a genetic algorithm and the nearest cell and frontier-based exploration presents the best coordination levels. Figure 21: Cumulative average for decision gain experiments. Box plot for decision gain experiments. Multiple comparison test for decision gain experiments. Decision gain ranking. Figure 22: Collision frequency for each exploration algorithm. Social interaction ranking. One of the most important features of exploration algorithms is avoiding re-exploration, because this is considered a loss of power and time by the robot team. Based on Figure 24, the supremacy of the utility function optimized with a genetic algorithm over the other algorithms is clear. Figure 23: Cumulative average for coordination level experiments. Box plot for coordination level experiments. Multiple comparison test for coordination level experiments. Coordination ranking. The most important goal in an exploration problem is time. If high coordination with three agents in the environment is required, Figure 25 shows that exploration algorithms that trade-off coverage gain and time cost to reach a specific grid cell present better performance. Moreover, farthest and random cell algorithms make re-exploration of the environment increasing the exploration time. Figure 24: Cumulative average for re-exploration level experiments. Box plot for re-exploration level experiments. Multiple comparison test for re-exploration level experiments. Re-exploration ranking.