## 4 Results and Discussion
### Simulation Results
In some cases, the increase in energy was next to zero as in the [A,B] route pair, where there is a pure 8% distance reduction, with less than 1% additional energy cost. We can also notice that the gain in distance reduction is not linearly proportionate with the increase in energy consumption. For example, in both [C,D] and [E,F] route pairs, the additional energy costs are almost the same, while the reduction in path length varied from 11% to 17.7%, respectively. Finally, we show that the running time of both algorithms is almost matching and relatively small, with an average higher run time of 3.9% for the proposed algorithm. We can deduce from this the fact that the introduced composite metric barely adds to the overall computational complexity. Table 2 summarizes the results obtained using the ACO approach of [21] and how these results compare against the proposed greedy algorithm (i.e., Algorithm 1). It is clearly seen that our proposed greedy composite metric approach provides superior results, in terms of the distance and energy consumption of the obtained paths, and also in terms of the algorithm running time. In particular, the proposed greedy approach resulted in paths with up to 23% shorter distances, and up to staggering 67% lower energy consumption, with a reduction one to two orders of magnitude in the algorithm running time. It is worth noting that the benchmark ACO implementation also uses the same composite metric as a routing objective. This justifies why the greedy algorithm performs better on both fronts, i.e., the path distance and energy consumption. The results in Table 1, however, compare the composite metric against pure energy minimization. The latter leads to the tradeoff of reducing the resulting path distance at the expense of a slight increase in the energy consumption. Finally, Figures 6 and 7 depict the convergence of the ACO algorithms in terms of the path distance and energy consumption, respectively. Moreover, Figure 8 shows the final constructed paths from the greedy and ACO implementations for the [C,D] routing pair projected on a 3D terrain that corresponds to the real-life mountainous area under consideration in this work. By analyzing Figures 6 and 7, we can clearly see that the number of ACO iterations required for path planning convergence to a final or near-final path length and energy cost values is roughly between 100-200 iterations, dep \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Route Pair**} & \multicolumn{2}{c}{**Path Length (m) and**} & \multirow{3}{*}{**Path Length Reduction**} & \multirow{3}{*}{**Energy-Cost Reduction**} & \multirow{3}{*}{**Elapsed Time for ACO**} \\  & \multicolumn{2}{c}{**Energy (kJ)**} & & & & \\ \cline{1-1} \cline{5-6}  & **Resulting from ACO** & & & & **(Due to Algorithm 1)** & \\ \cline{1-1} \cline{5-6}  & **(m)** & **(kJ)** & & & **(\%)** & **After 100 tie.** & **After 500 tie.** \\ \hline
[A,B] & 2388.3 & 296.8 & 23.38 & 66.81 & 58 & 230 \\
[C,D] & 1058.7 & 666.7 & 13.95 & 9.81 & 33 & 105 \\
[E,F] & 699.6 & 68.9 & 5.15 & 23.37 & 30 & 90 \\
[G,H] & 558.1 & 287.5 & 12.04 & 2.89 & 23 & 55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ant Colony Optimization (ACO) results and comparison. under planning. To achieve the best possible ACO results for comparison purposes, we consider 500 iterations for all ACO runs, which under some paths can produce slightly better results over the first 100-200 iterations. It should be also noted that we performed tests up to 1000 iterations, and we did not record any improvements as compared to the 500 th iteration and onwards. It is intelligible from Figure 8 that all the constructed paths are avoiding the nontraversable soil (upper side to the constructed routes) and only consider reaching the final destination (D) node by moving forward and descending. Figure 6: Convergence of the path distance vs. the ACO iterations. Figure 7: Convergence of the path energy vs. the ACO iterations.