## 5 Experimental Results
Given the physical characteristics of the environment used to obtain these results, the analysis of the next variables aims to determine the coverage and searching effectiveness of each algorithm regardless of the environment. As a first step, both variables increase their rate to increase the number of robotic agents in the environment as shown in Figures 26 and 27. Figure 17:0 Cumulative average for exploration time experiments. Box plot for exploration time experiments. Multiple comparison test for exploration time experiments. Exploration time ranking. Additionally, Figure 28 shows the frequency of collision avoidance performance for each negotiation algorithm in the experiments. The results show how the algorithms actually select the most appropriate according to the collision scene. Figure 29 shows a radar chart or spider plot that permits selecting of the appropriate exploration algorithm for any restriction or mission goal represented with the response variables analyzed for the indoor environment. This presents normalized values for each variable response and results for three-agent exploration. Before starting the analysis of results, it is essential to define an exploration algorithm intelligence, such as its ability to generate important decisions (in terms of environment coverage) in minimum time with the least amount and quality of information from teammates and the environment. About the response time, the three proposed exploration algorithms take a longer time to perform the decision-making process compared to the non-intelligent approaches. This proves the stated above regarding the information necessary for the execution of each algorithm. Figure 17:1 Cumulative average for coverage effectiveness experiments. Box plot for coverage effectiveness experiments. Multiple comparison test for coverage effectiveness experiments. Coverage effectiveness ranking. Figure 17:2 Cumulative average for search effectiveness experiments. Box plot for search effectiveness experiments. Multiple comparison test for search effectiveness experiments. Search effectiveness ranking. Figure 17:3 Frequency of collision avoidance solutions for each negotiation algorithm. The decision cost measures the average time needed to complete the task assigned by the exploration algorithm. Based on Figure 29, it is interesting to notice how the utility function optimized with GA is similar to the nearest cell exploration. Furthermore, it is proved that the farthest cell exploration and less covered and farthest cell exploration take the most expensive decisions (in time and power consumption). The decision gain measures the average coverage area obtained when the agent completes the task assigned by the exploration algorithm. Based on Figure 29, it is clear to note how the correlation filter algorithm decisions obtain more coverage gain than the other algorithms, proving its capacity to maximize the use of global coverage area construction by each agent. The results obtained in the social interactions section conclude that an algorithm with a high rate of collision is not very sociable, since agents' decisions affect their teammates. The utility function optimized with a GA had the lowest collision frequency, demonstrating a good sociable algorithm. The coordination level measures the ability to distribute the work evenly among agents. Clearly, the algorithms that reach to farthest cell present the lower coordination. Again, the utility function optimized with a GA together with the nearest cell and frontier-based exploration shows the best coordination levels. One of the most important exploration algorithms features is re-exploration avoidance because this is considered a loss of power consumption and time by the agent team. Figure 29 shows the supremacy of the utility function optimized with a GA over the other algorithms. The most important goal in the exploration problem is the exploration time. In mono-agent systems, the nearest cell exploration presents the best performance.