## 5 Experiment and Results  
In this section, we present the experimental validation of the feasibility and effectiveness of the proposed OGM-IRM base placement optimization algorithm in a household environment. The user selects the task target through the touch screen on the WMRA, which automatically selects the optimal base position and moves it to that position. We tested the algorithm on three objectives, namely, grasping a globe located at the edge of the table, grasping a cup located above the table on the opposite side of the table's edge, and grasping a book located above the table adjacent to the wall, as shown in Figure 9. The experiments were repeated ten times in the given environment, and experiments were completed on the WMRA using the proposed OGM-IRM algorithm, the handle remote control method, and the process-structured navigation method of the EL-E robot. The total time to complete the task was recorded and compared to evaluate the performance of the OGM-IRM algorithm. Figure 9: The task of objects on the desktop: (**a**) WMRA starting position; (**b**) task result for the globe. The experiment at position 1 validated the feasibility of the algorithm and measured the level of efficiency enhancement when using the OGM-IRM algorithm on the WMRA. The experiment at position 2 tested the reliability of the IRM information and evaluated the results of the base optimization, while the experiment at position 3 tested the algorithm's ability to consider the interference of environmental information while calculating the base optimization and maximizing its function, as shown in Figure 10.  
The specific experimental results are presented in Table 1, which indicates that the OGM-IRM algorithm achieved superior results compared with the process-structured navigation method used by the EL-E robot. The EL-E robot's selection of a target navigation point that was far from the task target made it impossible for it to complete the third task. The second experiment involved operating in a vertical environment to test the algorithm's performance when the optimal base placement overlaps with obstacle information, as shown in Figures 11 and 12. Specifically, the experiment involved completing tasks on a table drawer and an elevator button, with obstacles added to the latter to test the algorithm's obstacle recognition ability. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Task**} & **Joystick** & **Algorithm of EL-E Robot** & \multicolumn{2}{c}{**OGM-IRM**} \\ \cline{3-5}  & **Total Time (s)** & **Total Time (s)** & **Total Time (s)** & **Interaction Time (s)** \\ \hline
1 & 87.66 & 66.63 & 48.05 & 1.25 \\
2 & 95.21 & 78.50 & 62.77 & 1.27 \\
3 & 90.54 & Fail & 50.01 & 1.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The experiment results of the desktop task. Figure 11: The task of calling the elevator: (**a**) WMRA starting position; (**b**) task result for the elevator. Figure 10: Optimization results of base placement for the object on the desk. For the drawer task, the EL-E robot's process-structured navigation method failed as a result of the TCP being too far from the task target, a problem that was successfully overcome using the OGM-IRM algorithm. The second task involved obstacle avoidance in an elevator shaft, which demonstrated the algorithm's ability to optimize the robot's position and avoid obstacles, as shown in Figure 13. The experimental results in Table 2 indicate that the proposed OGM-IRM algorithm significantly reduces the task completion time and human-machine interaction time, enhancing the efficiency of completing high-difficulty tasks and expanding the range of executable tasks for the WMRA. Figure 12: The task of grabbing the drawer: (**a**) WMRA starting position; (**b**) task result for the drawer. When performing a task, the WMRA starts moving from the location in (**a**) and reaches the location in (**b**). Figure 13: The task of calling the elevator base placement optimization results.