# Vision-Guided Six-Legged Walking of Little Crabster Using a Kinect Sensor
## 5 Experiment  
In order to verify the proposed landing position modification algorithm, we placed 25 mm-thick obstacles on a treadmill and conducted walking experiments using the LCR 200 [23]. First, as shown in Figure 20, the LCR 200 tried to land the swing leg on the obstacle's rear edge; this process had low reliability because the depth information at the back of the edge was unavailable due to an occlusion. Without the proposed algorithm, the landed foot slipped on the edge and dropped to the ground after landing, as shown in Figure 20a. On the other hand, when the proposed algorithm was applied to the robot, the landing position was modified from the rear edge to the stable surface, and the swing leg landed on the obstacle stably, as shown in Figure 20b. Table 4 shows the depth data collection rate and standard deviation of the ground height for the original landing position and eight alternative landing positions. It can be seen that the data collection rate of the original landing position was 57 %, which is less than the 70 % threshold. Hence, the robot sequentially searched for alternative eight landing positions and evaluated their reliabilities of them. Among alternative landing positions, the 1 st, 6 th, 7 th, and 8 th landing positions have reliabilities higher than the 70% threshold, and the robot chose the 6 th alternative landing position, which has the highest reliability. Moreover, the chosen alternative landing position is sufficiently safe because the standard deviation of the ground height is much less than 8 mm. Figure 19: Scanned 3D ground data and calculation of averaged ground height from ten sampled areas within overlapped area. Blue box means a middle section of the ground data. Figure 20: Reliability algorithm test: (**a**) without proposed algorithm; (**b**) with proposed algorithm [23]. Second, to test for a safe landing, we intentionally made the LCR 200 land the swing foot on the obstacle's front edge, which has low safety due to the sharp edge, as shown in Figure 21. It can be seen in Figure 21 that, without the proposed algorithm, the landed foot also slipped on the obstacle and dropped to the ground, whereas the robot landed the swing foot on the safe area when using the proposed algorithm, as shown in Figure 21b. Table 5 shows the standard deviations of the ground height for the original and eight alternative positions. The original landing position has a standard deviation of 17.64 mm, which is higher than the 8 mm threshold; hence, the robot determined the original landing position as an unsafe position. Consequently, the robot searched for eight alternative positions and chose the 1 st alternative landing position, which has the smallest standard deviation of 5.67 mm among eight alternative positions. Moreover, the 1 st alternative position has reliability sufficiently higher than 70%. \begin{table}
\begin{tabular}{r r r r r r} \hline \hline  & **X (mm)** & **Y (mm)** & **Z (mm)** & **Depth Data Collection Rate** & **Standard Deviation (mm)** \\ \hline Original landing position & 1036.35 & \(-\)336.35 & 45.12 & 57\% & 13.14 \\
1 st Alternative landing position & 1066.35 & \(-\)336.35 & 8.37 & 71\% & 6.35 \\
2 nd Alternative landing position & 1057.56 & \(-\)315.14 & 10.36 & 54\% & 17.32 \\
3 rd Alternative landing position & 1057.56 & \(-\)357.56 & 11.97 & 63\% & 7.84 \\
4 th Alternative landing position & 1036.35 & \(-\)306.35 & 45.21 & 63\% & 11.97 \\
5 th Alternative landing position & 1036.35 & \(-\)366.35 & 45.33 & 58\% & 13.29 \\
6 th Alternative landing position & 1015.14 & \(-\)315.14 & 44.84 & 87\% & 4.23 \\
7 th Alternative landing position & 1015.14 & \(-\)357.56 & 44.89 & 81\% & 2.19 \\
8 th Alternative landing position & 1006.35 & \(-\)336.35 & 44.97 & 79\% & 5.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Depth data collection rate and standard deviation of ground height. (Reliability algorithm test). Figure 21: Safety algorithm test: **(a)** without proposed algorithm; **(b)** with proposed algorithm [23].