## 7 Conclusions  
In this study, an autonomous robot navigation system was developed for indoor environments using the fuzzy logic navigation system and potential field navigation system, and both systems utilized the fuzzy positioning system. The positioning system utilized chromaticity and frequency component ratio data, which were obtained from the LED lighting that was installed under the ceiling in the simulation environment.  
To evaluate the performance of the autonomous navigation system, various simulations that mimicked indoor environments were conducted. The navigation simulator generated the path of a mobile robot using the proposed systems until the robot reached  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Simulation Data**} & \begin{tabular}{c} **Simulation 1** \\ **(Figure 13a)** \\ \end{tabular} & \begin{tabular}{c} **Simulation 2** \\ **(Figure 13b)** \\ \end{tabular} & \
\begin{tabular}{c} **Simulation 3** \\ **(Figure 13c)** \\ \end{tabular} &
\begin{tabular}{c} **Simulation 4** \\ **(Figure 13d)** \\ \end{tabular} \\ \hline \multirow{2}{*}{Length of path} & Fuzzy logic navigation & 79.60 cm & 88.41 cm & 111.86 cm & 299.78 cm \\  & Potential field navigation & 77.36 cm & 105.11 cm & 128.90 cm & 333.40 cm \\ \hline \multirow{2}{*}{Navigation time} & Fuzzy logic navigation & 99.34 s & 133.33 s & 161.39 s & 312.21 s \\  & Potential field navigation & 57.43 s & 76.24 s & 92.74 s & 223.10 s \\ \hline \multirow{2}{*}{Mean of velocity} & Fuzzy logic navigation & 0.80 cm/s & 0.66 cm/s & 0.69 cm/s & 0.96 cm/s \\  & Potential field navigation & 1.35 cm/s & 1.38 cm/s & 1.39 cm/s & 1.49 cm/s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rules for avoiding obstacles in robot navigation.  
the target. When the robot navigated by the fuzzy logic navigation system through the unknown environment, it used eight virtual distance sensors, and the steering angle and moving distance were calculated using 81 behavior rules and fuzzy membership functions. When the robot navigated by the potential field navigation system, the repulsive and attractive forces on 36 points of the searching radius, which is a circle surrounding the mobile robot, were used to calculate steering angle. This also led the robot to the position of minimum potential. The moving distance in the potential field navigation system was calculated with a linear function that produces the moving distance corresponding to the minimum distance data to surrounding obstacles in the following step.  
The x and y coordinates of both navigation systems were calculated using the fuzzy positioning system based on pre-measured chromaticity and frequency component ratio data. The robot successfully reached the targets in various simulation environments using both navigation systems.  
In this study, the possibility of implementing LED lighting-based indoor navigation systems was evaluated. In the future, a practical test will be performed using practical robots.  
Conceptualization and methodology, J.-H.J.; K.P.; software, K.P.; validation, J.-H.J., K.P.; investigation, J.-H.J.; K.P.; resources, J.-H.J.; K.P.; data curation, J.-H.J.; K.P.; writing-original draft preparation, J.-H.J.; writing-review and editing, K.P.; visualization, J.-H.J.; K.P.; supervision, K.P.; project administration, K.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Youngsan University Research Fund of 2020.  
Not applicable.  
The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have greatly improved the quality of the paper.  
The authors declare no conflict of interest.