# Navigating a Service Robot for Indoor Complex Environments
## 3 Experiment
### Obstacles Avoidance  
#### 3.3.1 Static Obstacle Avoidance  
In the static obstacles experiment, the testing site is a hallway. The obstacles are placed along the hallway, as shown in Figure 15.  
Sufficient gaps are left between the obstacles so that the robot should be able to reach its final destination. The following plot, Figure 16, shows the path the robot took. The robot's position was recorded at an interval of every 10 s.  
There are a total of 17 possible scenarios designed along the path that we think that the robot would need to make critical decisions about whether to incorporate sonar sensors in its decisions. The following figure, Figure 17, shows where each of the 17 scenarios, T\({}_{1}\)-T\({}_{17}\), took place, and Table 4 shows the input values and the robot's decisions for the 17 scenarios.  
Figure 16: The path the robot took with positions taken at 10 s interval.  
Figure 14: Examples of facial recognition and gender classification.  
Figure 15: Static obstacles for the robot.  
Figure 18 shows the fuzzy inputs and outputs at T\({}_{1}\)-T\({}_{17}\).  
Table 5 below illustrates the comparisons between our method, [PERSON]'s method [3], and [PERSON]'s method [4], using single-point analysis at each scenario point. Success is defined as being able to avoid collision.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Left Obstacle**} & \multirow{2}{*}{**Right**} & \multirow{2}{*}{**Cap**} & \multirow{2}{*}{**Left Sonar**} & \multirow{2}{*}{**Right Sonar**} & \multirow{2}{*}{**Final**} \\  & & & **Obstacle** & & **Normalized to** & **Normalized** & **Normalized** & **Decision** \\ \hline T\({}_{1}\) & N/A & 317 & 1.41 & 1 & 1 & Forward \\ T\({}_{2}\) & 97.6 & 10,000 & 1.67 & 0.2 & 1 & Right Turn \\ T\({}_{3}\) & N/A & 106.3 & 1.20 & 0.8 & 0.44 & Left Turn \\ T\({}_{4}\) & N/A & N/A & 0.0 & 0.22 & 0.22 & Forward \\ T\({}_{5}\) & 128.9 & N/A & 0.0 & 0.2 & 0.62 & Right Turn \\ T\({}_{6}\) & 122.1 & 125.6 & 1.64 & 0.4 & 0.4 & Forward \\ T\({}_{7}\) & 139.9 & 176.3 & 1.56 & 0.58 & 0.66 & Forward \\ T\({}_{8}\) & 272.2 & N/A & 2.24 & 0.4 & 0.36 & Forward \\ T\({}_{9}\) & N/A & N/A & 0.0 & 0.3 & 0.22 & Forward \\ T\({}_{10}\) & N/A & 188.8 & 1.67 & 1 & 0.5 & Forward \\ T\({}_{11}\) & 163.0 & 160.3 & 1.05 & 0.5 & 0.38 & Forward \\ T\({}_{12}\) & 100.1 & N/A & 1.33 & 0.08 & 0.13 & Right Turn \\ T\({}_{13}\) & 193.1 & 1000.0 & 0.84 & 0.4 & 1 & Right Turn \\ T\({}_{14}\) & 163.5 & 196.4 & 0.83 & 0.54 & 0.4 & Forward \\ T\({}_{15}\) & N/A & N/A & 0.0 & 0.04 & 0.22 & Forward \\ T\({}_{16}\) & N/A & N/A & 0.0 & 0.22 & 0.22 & Forward \\ T\({}_{17}\) & 115.9 & N/A & 1.13 & 0.22 & 0.42 & Right Turn \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensors Inputs and Decision for Scenarios T\({}_{1}\)–T\({}_{17}\).  
Figure 17: The seventeen scenarios (T\({}_{1}\)–T\({}_{17}\)) where decisions of the robot were recorded.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Correa** & **[PERSON]** & **Our Method** \\ \hline T\({}_{1}\) & Fail! & Success & Success \\ T\({}_{2}\) & Fail! & Fail! & Success \\ T\({}_{3}\) & Fail! & Success & Success \\ T\({}_{4}\) & Success & Success & Success \\ T\({}_{5}\) & Fail! & Fail! & Success \\ T\({}_{6}\) & Fail! & Fail! & Success \\ T\({}_{7}\) & Success & Success & Success \\ T\({}_{8}\) & Success & Success & Success \\ T\({}_{9}\) & Success & Success & Success \\ T\({}_{10}\) & Fail! & Success & Success \\ T\({}_{11}\) & Success & Success & Success \\ T\({}_{12}\) & Fail! & Success & Success \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons between [PERSON], [PERSON], and our proposed method on T\({}_{1}\)–T\({}_{17}\) scenarios.