## 6 Experiment
### Simulation Experiment B  
In this experiment, six maps of 30 \(\times\) 30 m were selected for simulation, which differed in the number of obstacles and the shapes of obstacles. Start and end positions were given randomly, as shown in Table 3. Mul-ACO was compared with ACO, MH-ACO, and MF-ACO in larger and more complex uneven environments. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Map NO.** & **Length** & **Iterations** & **Turns** &
\begin{tabular}{c} **Height** \\ **Difference** \\ \end{tabular} & **Time (s)** \\ \hline \multirow{4}{*}{GA} & a & 28.7 & 32 & 10 & 5.6 & 9.2 \\  & b & 29.7 & 38 & 9 & 5.4 & 12.5 \\  & c & 28.5 & 37 & 8 & 4.2 & 10.7 \\  & d & 28.1 & 31 & 5 & 5.3 & 10.4 \\  & e & 28.6 & 39 & 7 & 6.7 & 12.6 \\  & f & 29.0 & 34 & 6 & 4.1 & 9.5 \\ \hline \multirow{4}{*}{SA} & a & 29.6 & 28 & 9 & 4.3 & 0.7 \\  & b & 29.9 & 25 & 9 & 5.7 & 0.9 \\  & c & 30.7 & 26 & 7 & 3.1 & 0.5 \\  & d & 31.4 & 37 & 6 & 2.8 & 0.8 \\  & e & 28.9 & 21 & 10 & 7.2 & 0.6 \\  & f & 29.3 & 23 & 6 & 7.5 & 0.6 \\ \hline \multirow{4}{*}{Mul-ACO} & a & 32.4 & 7 & 5 & 0.9 & 1.4 \\  & b & 29.8 & 6 & 6 & 1.2 & 1.3 \\ \cline{1-1}  & c & 28.0 & 8 & 2 & 0.3 & 1.5 \\ \cline{1-1}  & d & 27.9 & 9 & 3 & 0.5 & 1.7 \\ \cline{1-1}  & e & 28.7 & 6 & 3 & 0.4 & 1.3 \\ \cline{1-1}  & f & 28.2 & 7 & 2 & 2.1 & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation results of GA, SSA, and Mul-ACO. Figure 9 shows the best trajectory planned by each method. It is obvious that the trajectory planned by the method proposed in this paper had fewer turns, good smoothness, and short length, while bypassing steep or concave terrain. In simple scenes, as shown in Figure 9c,d,e, the trajectory planned by Mul-ACO was smoother and bypassed steep or concave areas as much as possible. In more complex scenes (Map 1 and Map 6), the difference in the quality of the trajectories was more obvious. As shown in Figure 9a, the trajectories planned by ACO and MH-ACO extended along the edge of the obstacle resulting in multiple turns. Although the MF-ACO algorithm avoided local traps when planning trajectories, its trajectory height and smoothness were worse than Mul-ACO. Especially in Vortex map 6, as shown in Figure 9f, the traditional ACO failed to generate a trajectory from the starting point to the end point, and MH-ACO often fell into a local trap and could not escape. Although MF-ACO could work normally, the quality of the trajectory was not ideal. Mul-ACO had a good performance due to the improved heuristic function and adaptive pheromone adjustment strategy. Moreover, the trajectory optimization algorithm based on mutual learning is suitable for different maps. Therefore, the scheme proposed in this paper is not affected by changeable environments and can plan the trajectory with a good comprehensive quality. As shown in Table 4, 30 experiments were performed on six different maps to obtain the specific average performance metric of the algorithms. Figure 10 shows the performance comparison of Mul-ACO and the other three algorithms on 6 maps, which more clearly highlights the advantages of the proposed scheme in terms of length, number of iterations, number of turns, and the height difference of trajectory. From the specific data and the line graph, it can be seen that the method proposed in this paper was significantly better than the other algorithms in the number of iterations and the number of turns, reducing them by about 66.7%-87.5% and 80%-94.7%. Moreover, compared with other algorithms, the trajectory length was reduced by about 6%-23.3%. Furthermore, the Mul-ACO scheme did not choose a steep route to reduce the wear of the mobile robot in uneven terrain. The height difference of the trajectory was reduced by 15.9%-68.2%.