# A Semi-Automatic Wheelchair with Navigation Based on Virtual-Real 2D Grid Maps and EEG Signals
## 3 Results and Discussion
### Wheelchair Movement to Reach Map-Based Desired Target  
The experiment was performed in an environment of 126.72 m\({}^{2}\) which was divided into square grids of one map with a size of 8 \(\times\) 11, in which each square has a size of 1.2 m \(\times\) 1.2 m as shown in Figure 11. The wheelchair was installed to be able to move at the speed of 3 km/h for matching the processing speed of the system. An electrical wheelchair was installed with an RGB-D camera system and other equipment as shown in Figure 12. Information about the surrounding environment obtained from the camera system was processed by a computer and then transferred to the motor system of the wheelchair for motion control. In addition, in this research, we performed two experiments, including a self-control user and an automatic control user. In the self-control user model, the user can self-control commands such as going forward, backward, and turning right and left during the wheelchair movement. Meanwhile, the automatic control user mode means that the user can choose one of the targets by using EEG signals which are assigned to the targets to reach [36] and our proposed algorithm in the wheelchair control system is applied so that the wheelchair can automatically reach the chosen target. Figure 13 shows the green real path of the wheelchair, which was controlled by the user during reaching the target. In particular, the discontinuous green path is the desired path in the real environment that the wheelchair needs to follow to reach the target, while the red path of the wheelchair is the path controlled by self-control mode using EEG signals [38] to go straight, turn left and right during reaching the destination. With the experiment using the self-control, the wheelchair moved according to the red path and then turned to the undesired direction shown by the red path and blue dash-dot ellipse. It means that in this case, the wheelchair could very easily have an obstacle collision. In addition, with the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Environment** & **Model** & **Average No.** & **Average** \\  & & **of Episodes** & **Training Time** \\ \hline \multirow{2}{*}{Small (8 \(\times\) 11)} & Traditional Q-Learning [41] & 60.0 & 198.4 s \\  & SARSA [42] & 75.0 & 223.9 s \\ \hline \multirow{2}{*}{Large (11 \(\times\) 33)} & Traditional Q-Learning [41] & 235.0 & 1.45 h \\  & SARSA [42] & 275.0 & 57.23 min \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Relative Performance of Previous Models. mode of the self-control, the movement of the wheelchair is unstable and discontinuous as shown in Figure 13. In particular, the wheelchair went straight, then stopped, turned right, and then was continuously interrupted during the movement time. It is obvious that the user was trying hard to control it to turn right or left and go straight. Figure 11: The experimental environment. (**a**) The 1 st view of the real environment; (**b**) the 2 nd view of the real environment; (**c**) the 3 rd view of the real environment; (**d**) the 2D grid map. Figure 12: The wheelchair navigation system installed with devices. For improving the wheelchair control using the self-control mode, we used the proposed model with the semi-automatic control. With this mode, the user just needs to choose one typical target by using EEG commands and then the wheelchair will automatically move to reach the desired target with high stability and smoothness. In particular, using the environmental map in Figure 11a-c, the actual paths of the wheelchair after moving to reach the target were as shown in Figure 14b. Therefore, the moving process was re-calculated and the path positions of the wheelchair with the axes of X and Y were re-drawn for the purpose of the comparison with the simulation paths (blue arrows) as shown in Figure 14a. The starting point of the wheelchair is random and the wheelchair automatically determines its position on the map by identifying landmarks in the environment.