## 6 Experimental Results and Discussion
The effectiveness of area coverage for tested methods is provided in Figure 22 for visual comparisons and in Tables 1 and 2 for numerical comparisons. Covered areas by hTetro during navigation are marked as green color and the revisited areas which have been covered are marked as red color. Because of optimal the path planning enhanced by shape planning, the proposed method can achieve maximum area coverage and especially cover the area marked by red arrows in Figure 16. The ROS A-star based method and method in Reference [29] uncover some free space regions and revisit some covered areas. According to the Table 1, the coverage ratio of the proposed method archives the highest of 92.55%, while the method in Reference [29] without changing shape yield the second with a performance of 86.15% and the traditional A-star method only covers 72.63% of the total area and significantly lower than the proposed method. Based on Table 2, the ratio of revisiting the area using the conventional A-star method degrades the robot performance by just achieving an average value of 21.72%. Since this method does not have any mechanism to avoid revisiting the covered cells while planning the shortest path by A-star. The segments of trajectory plan have a high probability of being overlapped in the cases of complicated obstacle shapes. While the method [29] which uses A-star for moving to the uncovered sub region have the similar issue of revisiting the covered cells, since the way-out from the last covered cell to first uncovered cell of another uncovered subarea requires the revisiting the covered cells. As per the results, the revisiting rate of this method was 12.26% and it is significantly higher than the proposed method. On the other hand, using modified A-star based zigzag scanning strategy to clear predefined the boundary waypoint and obstacle waypoints, the proposed method archives the lowest revisiting ratio of 5.25%. Figure 21: Areas coverage of the proposed method for different tested bed maps. **(a,d)** path planning and covered areas a small map with simple obstacles, **(b,e)** path planning and covered areas for a large map with moderate obstacles, **(c,f)** path planning and covered areas of for a large map with complicated obstacles. The comparison of computing time to cover testbed environments with different space complexity between different algorithms are added in Figure 20.0 Despite expensing extra time to complete the shapeshifting and to cover the narrow spaces, the computation time of proposed method yields just slightly higher than others do. The optimal path and shape plan for maximizing the area covering and reducing the revisited areas contributes to this result. Table 4 provides time consumption to generate the plan for different testbed maps. The proposed approach takes about 0.16 s on an average to generate the plan, which is feasible for real-time applications. Since we have validated the efficiency of the proposed method in three different scenarios, the average value in Tables 1-4 was calculated for each tested method in order to normalize the performance metrics. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **A-Star ROS Navigation** & **Method [29]** &
\begin{tabular}{c} **Proposed Method with Path** \\ **and Shape Planning** \\ \end{tabular} \\ \hline Small map with simple obstacles & 80.12 & 90.23 & 95.32 \\ Large map with moderate obstacles & 72.21 & 85.68 & 93.12 \\ Large map complicated obstacles & 65.56 & 82.54 & 89.22 \\
**Average** & **72.63** & **86.15** & **92.55** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The covered area ratio comparisons (%).