# Mobile Robot Path Planning Using a Laser Range Finder for Environments with Transparent Obstacles
## 6 Experimental Results  
### Experimental Environment  
Three environments for the experiments were configured. All the experimental environments were 4 m \(\times\) 3.53 m, and four sides were surrounded with opaque obstacles. Three transparent obstacles (objects 1-3) were used in all the experiment environments and as substitutes for window frames in a real building; an opaque rectangular obstacle (5 cm \(\times\) 4 cm) was placed in the gap between each transparent obstacle. The first experimental environment in Figure 11a and the obstacles (objects 1-3) were placed in line. In the second experiment represented by Figure 11b, object 3 was placed orthogonally to object 2 to configure a corner composed of transparent obstacles. Lastly, a diamond-shaped opaque obstacle was added to the first experimental environment, as shown by Figure 11c. Figure 12 shows the actual experimental environment. As can be seen on Figure 12, the transparent materials used are thick sheets of glass with 0.5 cm thickness. Figure 11: Experimental environment overview: (**a**) experimental environment with transparent obstacles arranged in a line; (**b**) experimental environment with transparent obstacles arranged vertically; (**c**) experimental environment with non-transparent obstacles added to (a). Figure 14 corresponds to environment maps generated in response to experimental environment 2. Figure 14a is the baseline for experimental environment 2. Figure 14b is the result generated by using only collected data from LRF. As the results show, a lot of obstacle candidates are generated on the opposite side of transparent obstacles as the result of experimental environment 2. The transparent obstacles placed vertically look as though the transparent obstacles had been found, but this is a result of a lot of reflection noises distributed by the transparent obstacle. Figure 14c is the result generated using the environment map generation algorithm in an environment with transparent obstacles. The boundaries of the transparent obstacles placed horizontally show a low probability of being an obstacle candidate. This phenomenon results from the process of the rasterization of the reflection noise for transparent obstacles in the vertical line, which also decreases their probability of being an obstacle candidate for cells belonging to transparent obstacles on the horizontal line. Figure 13: Environment maps corresponding to experimental environment 1 (the starting point, destination point, and the travelled paths of the robot are represented by the blue circle, the red triangle and the arrow, respectively): (**a**) map using the baseline; (**b**) map using the only collected data from LRF; (**c**) map using LRF and the algorithm. Figure 15 represents the three environment maps generated in accordance with experimental environment 3. Figure 15a is the baseline for the environment map. Figure 15b is the result of using only collected data from LRF. Although it detected the diamond-shaped opaque obstacle, it could not detect the boundaries of transparent obstacles, and many obstacle cells were represented on the opposite side. On the other hand, Figure 15c represented the diamond-shaped opaque obstacle well, and the surface of the transparent obstacles were detected as well. Figure 14: Environment map corresponding to experimental environment 2 (the starting point, destination point, and the travelled paths of the robot are represented by the blue circle, the red triangle and the arrow, respectively): (**a**) map representing the baseline; (**b**) Map using only collected data from LRF; (**c**) map using LRF and proposed algorithm. As the figures above demonstrate, the robot drives towards the wall of the transparent obstacle at an angle of 45 degrees, stops near the transparent obstacle, rotates, and resumes the movement alongside the wall of the transparent obstacle.