# Target Recognition and Navigation Path Optimization Based on NAO Robot
## 5 Experimental Verification  
### Experimental Algorithm Verification  
To verify the performance of the IABC algorithm, the ant colony optimization algorithm, the Chaotic genetic algorithm, and the traditional artificial bee colony algorithm are used to compare with the improved artificial bee colony algorithm. Table 1 shows these parameters of the improved artificial bee colony algorithm.  
The fitness curve is shown, and the convergence speeds of the ABC algorithm and the IABC algorithm can be seen in Figure 10. The optimal path obtained with the improved artificial bee colony algorithm is better than that obtained with other algorithms. After 400 times, the traditional artificial bee colony algorithm converges to a stable value. However, only after 266 times, the improved artificial bee colony algorithm converges to a stable value.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Food sources numbers & 50 \\ Search times & 2000 times \\ No obstacle weight factor & 0.2 \\ Obstacle weight factor & 0.8 \\ Initialize temperature value & 10 \\ Temperature attenuation coefficient & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the artificial bee colony algorithm.  
Figure 9: Employed bee looks for the new food source.  
The optimized path obtained with the offline IABC algorithm is imported into the NAO robot for online experimental verification. The experimental process is as follows: the step length of the NAO robot is set to 0.04m, and the walking path of the NAO robot is set again according to Figure 11b and Table 2, with the previous termination point coordinates as the starting point coordinate and the starting point coordinate as the termination point coordinate. The comparison results of the verification are shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grid Coordinates** & **Actual Path Coordinates (m)** & **Route Setting** \\ \hline \((4,5)\) & \((0.64,0.80)\) & Along the upper right 45\({}^{\circ}\) walk 1.131 m \\ \((9,10)\) & \((1.44,1.60)\) & Turn right 45\({}^{\circ}\) and walk for 0.16 m \\ \((10,10)\) & \((1.60,1.60)\) & Turn left 45\({}^{\circ}\) and walk for 0.679 m \\ \((13,13)\) & \((2.08,2.08)\) & Turn right 45\({}^{\circ}\) and walk for 0.16 m \\ \((14,13)\) & \((2.24,2.08)\) & Turn left 45\({}^{\circ}\) and walk for 0.32 m \\ \((16,15)\) & \((2.56,2.40)\) & Turn left 45\({}^{\circ}\) and walk for 0.16 m \\ \((16,16)\) & \((2.56,2.56)\) & Arrive the destination target \\ \hline \hline \end{tabular}
\end{table}
Table 2: Path setting for the NAO robot.  
Figure 10: Fitness curve.  
Figure 11: Optimal path with the ABC algorithm and the IABC algorithm: (**a**) optimal path with ABC algorithm; (**b**) optimal path with IABC algorithm.  
It can be seen from Figure 12 that the optimal setting line in the path optimization simulation of the IABC algorithm does not encounter obstacles; so according to the set path, [PERSON] will not encounter obstacles when walking on this experimental platform. When the Nao robot reaches the position with coordinates of (2.56 m, 2.56 m), it stops walking. However, due to the material problems of the bottom of the NAO's foot and the floor, the insufficient friction between the bottom of the foot and the ground, and the accuracy of the deflection angle, Nao produces a certain offset during walking. Therefore, the actual effect of walking on the optimal path will differ from that under the ideal condition, but it is within the allowable range of error. The Nao robot can realize path optimization while avoiding obstacles.