## 4 Experimental Result and Evaluation
### Evaluation
#### 4.3.1 A Comparison of the Repetition Rate for Evaluation of the Methods  
In testing of robots in dynamic environments, the robot visits multiple-static obstacles in complex situations such as regular, irregular, multiple regular, and multiple irregular obstacles. Note that a regular obstacle can be defined a common object in frequently appeared basic obstacles. The testing experiment was in the range between 80-150 nodes (cells), with the performance of the robot coverage path about 90-100%. In the experimental results, the repetition rate for the proposed approach is lower than those of the MRN, BA* and A*-CPP methods, with respect to simple regular and irregular obstacle environments, as depicted in Table 3.  
As shown in Table 3, the repetition rate of the proposed model is lower than achieved for the MNR, BA* and A*-CPP methods in regular obstacles, irregular obstacles, multiple obstacles and multiple irregular obstacles. To do a similar scenario in the simulation experiment, a real robot was applied in the complex obstacles during the coverage path planning process. The complete CPP performance for the methods in terms of the repetition rate in the real robot are as follows: Regular obstacles, irregular obstacles, multiple obstacles, and multiple irregular obstacles, as depicted in Table 4.  
As shown in Table 4, experimental results of the real robot show that our proposed model achieves the highest coverage rate with a low repetition rate when compared to the MNR, BA* and A*-CPP methods evaluated. The proposed model has achieved complete coverage of the environmental area (the operational space) with a low repetitive coverage rate. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Obstacles in Static Environments**} \\ \cline{2-5}  & **Covered Area** & **Time (Minutes)** & **Coverage Rate** & **Repetition Rate** \\ \hline MRN & 70–90 m\({}^{2}\) & 6.00 & 75\% & 22.50\% \\ BA* & 70–90 m\({}^{2}\) & 5.00 & 95\% & 23.10\% \\ A*-CPP & 70–90 m\({}^{2}\) & 5.25 & 93\% & 13.40\% \\ Proposed model & 70–90 m\({}^{2}\) & **4.00** & **100\%** & **4.30\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results in a real robot. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Obstacles in Static Environments**} \\ \cline{2-5}  & **Irregular** & **Regular** & **Multiple Irregular** & **Multiple Regular** \\ \hline MRN & 4.00\% & 4.10\% & 26.50\% & 22.50\% \\ BA* & 5.30\% & 5.50\% & 16.50\% & 23.10\% \\ A*-CPP & 5.00\% & 5.40\% & 9.80\% & 23.40\% \\ Proposed model & **2.20\%** & **0.00\%** & **2.50\%** & **5.30\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The repetition rate in experimental result simulations in various environments. 3.2 Comparison of Repetition Rate and Duration in Robot Coverage Path Planning with Moving Obstacles  
In the experimental testing, we compared the proposed model with the other latest methods considered, these methods were: MNR, BA* and A*-CPP methods. The evaluation compared, when a robot faces with moving obstacles: (i) The duration, awareness, and ability [of the robot] to cover both static and dynamic environments, and (ii) the repetition rate where a robot faces obstacles in uncertain and dynamic operating environments. The tests evaluated in the operating environment with moving obstacles, which are randomly running in simulations. The experiments are done with the numbers of nodes (cells) in the range (200-550), the average results derived from the experiments are shown in Figure 10.  
From Figure 10 it may be seen that in terms of the duration times and repetition rate (for a robot to traverse an operating environment effectively the search space), our proposed model outperforms lower than those of the MNR, BA* and A*-CPP methods.