*4.2. Comparisons with the Existing Algorithms Based on the Shape of the Area of Interest*

Apart from the obstacle's densities, the shape of the area of intreat is also a relevant factor that can significantly impact the CPP algorithm performance. CPP over the regular shaped AOI is relatively easy compared to the irregular shaped AOI. The regular shaped AOI coverage can be obtained with the simple BF pattern. Meanwhile, the irregular shaped AOI coverage requires the concavities modification and hybrid motion patterns to achieve the full coverage. The CPP complexity varies with the shape of the target area and it requires obstacles' geometry knowledge to find the low cost solution for the coverage missions. To address this concern, we compared the proposed algorithm performance with the existing methods over five different types of the AOI to support generality. We performed rigorous experiments to verify the algorithm performance in relation with the shape of the AOI. The average computation time, path length and path overlapping are shown in Table 3.


**Table 3.** Proposed algorithm performance comparisons with varying shapes of the area of interest.

Through simulations and comparison with the two existing algorithms using five different shapes of the AOI, on average, our proposed algorithm reduces the computing time of pathfinding by 21.34%. From a path length point of view, it reduces path length by 8.98%. Additionally, the proposed algorithm has less path overlapping compared to the existing algorithms. We further compare our approach results of turning maneuvers with two existing methods in ten representative scenarios. Figure 9 shows the number of turns' comparisons between proposed and two existing algorithms. The proposed method shows 12.1% and 7.03% improvements in the number of turns as compared to BCDH-CPP and CA-CPP algorithm, respectively.

**Figure 9.** Turns comparisons: proposed algorithm versus BCDH-CPP and CA-CPP algorithms.

Apart from the numerical result, the proposed algorithm uses less number of sweeps compared to the decomposition based methods. Figure 10 shows the number of sweeps used by the proposed algorithm and decomposition based methods for the coverage of three cells (i.e., 5 to 7) of the AOI.

**Figure 10.** Footprints sweeps' comparisons with existing algorithms.

From the results, it can be observed that when the cell size is small and sufficient attention has not been paid to sensor/camera footprints size in area decomposition, the BCDH-CPP [53] and CA-CPP [54] algorithms (Figure 10b) need more sweeps compared to the proposed algorithm which can increase the path length in complex scenarios. In contrast, the proposed algorithm (Figure 10c) considers the sensor footprint as a coverage unit in the CPP and uses less sweeps to guarantee the perfect coverage.
