## 4 Results and Discussion
### Simulation Results  
We compare the greedy implementation of the proposed composite metric against the energy-cost minimization approach of [22], over multiple paths on the terrain under consideration to cover multiple UGV crossing scenarios. The greedy implementation of the proposed composite metric is deterministic in nature and produces solutions that do not change after running different simulations over the same input data. In fact, the obtained solution is optimal in the majority of the experiments. Simulation results for this part of the experiments are placed in Table 1. We consider running and comparing the proposed algorithm over four different UGV way points. Each path is represented by denoting its starting and finishing nodes as pairs of letters \([\,start\,,finish\,]\). It should be said that the selected pairs are randomly chosen and vary significantly in path distance to test our proposed algorithm under different scenarios. Figure 4: The area under study represented as a DEM map with information about soil type. The red dots represent the nodes visited by the UGV along its movement to the destination. Figure 5: 2D satellite imagery of the area under study with 4 UGV paths defined by their starting and ending positions as letters (pairs of two letters). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Path Length (m)**} & \multicolumn{2}{c}{**Path Length (m)**} & \multicolumn{2}{c}{**Path Length**} & \multicolumn{2}{c}{**Additional**} & & & \\ \multicolumn{1}{c}{**Route**} & \multicolumn{1}{c}{**and Energy (kJ)**} & \multicolumn{1}{c}{**and Energy (kJ)**} & \multicolumn{1}{c}{**Reduction**} & \multicolumn{1}{c}{**Energy Cost**} & \multicolumn{1}{c}{**Energy Cost**} & \multicolumn{1}{c}{**Elapsed Time**} & \multicolumn{1}{c}{**Elapsed Time**} \\ \multicolumn{1}{c}{**Pair**} & \multicolumn{1}{c}{**Resulting from**} & \multicolumn{1}{c}{**Resulting from**} & \multicolumn{1}{c}{**(Due to**} & \multicolumn{1}{c}{**(Due to**} & \multicolumn{1}{c}{**for [22]**} & \multicolumn{1}{c}{**for Algorithm 1**} \\ \multicolumn{1}{c}{\([s,f]\)**} & \multicolumn{1}{c}{**Algorithm 1**} & \multicolumn{1}{c}{**Algorithm 1**} & \multicolumn{1}{c}{**Algorithm 1)**} & \multicolumn{1}{c}{**Algorithm 1)**} & & \\ \multicolumn{1}{c}{\([\)**} & \multicolumn{1}{c}{**(m)**} & \multicolumn{1}{c}{**(kJ)**} & \multicolumn{1}{c}{**(m)**} & \multicolumn{1}{c}{**(kJ)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} & \multicolumn{1}{c}{**(\%)**} \\ \hline \([\)A,B\(]\) & 1989.2 & 97.7 & 1828.9 & 98.5 & 8.06 & 0.80 & 3.35 & 3.42 \\ \([\)C,D\(]\) & 1024.2 & 579.4 & 911 & 601.3 & 11.05 & 3.65 & 3.16 & 3.22 \\ \([\)E,F\(]\) & 806.6 & 51 & 663.6 & 52.8 & 17.73 & 3.45 & 3.24 & 3.43 \\ \([\)G,H\(]\) & 526.3 & 274 & 490.9 & 279.2 & 6.73 & 1.86 & 3.30 & 3.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison against energy-cost minimization of [22]. The metrics used to assess the quality of each obtained path are the path length representing the terrain distance crossed by the UGV in meters (m) and the energy consumed by UGV batteries to traverse that path in kilo Joules (k). We also calculated the percentage difference between the length and energy consumption of the paths obtained using the energy-cost metric of [22] relative to those obtained using the proposed composite metric. Finally, we also assessed the algorithm running time for both approaches. Table 1 clearly indicates the superiority of our proposed composite metric-based algorithm over the pure energy minimization algorithm in terms of the total path length reduction. Moreover, the cost of this distance reduction is barely noticeable as a small increase in the energy consumption of the proposed algorithm. This is best demonstrated in the [E,F] route pair, where a noticeable 17.7% distance reduction was achieved via the proposed algorithm with a marginal added energy consumption of less than 3.5%.