# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 4 Experimental Simulation and Analysis
### 20 \(\times\) 20 Simulation Results and Analysis  
Figure 2 illustrates the results of path planning and convergence trends for the three algorithms tested in a 20 \(\times\) 20 static grid environment. In panel (e), the red path indicates the route prior to the application of triangle pruning optimization, while the blue route represents the refined path after unnecessary nodes have been removed. The convergence curve for the potential field ant colony algorithm shows the iteration path length on the vertical axis, allowing for a comparison of the iteration count when the paths are similar. The horizontal axis represents the iteration count, with shorter iteration counts indicating a tendency toward stabilization. The traditional ant colony algorithm demonstrates inadequate guidance during the initial stages due to the heuristic function, resulting in more turning points, longer search paths, and a higher likelihood of becoming trapped in local optima. The improved ant colony algorithm referenced in [12] updates and reassigns the pheromone concentration based on local path information to expedite local optimization, yielding relatively good planned paths. However, it still requires multiple iterations, resulting in a slower convergence speed. In contrast, the improved ant colony algorithm presented in this paper incorporates the influence of artificial potential field force, enhancing guidance toward the target. Additionally, it employs the triangle pruning method for secondary processing of the planned path, which facilitates faster convergence and fewer turning points. According to the data in Table 2, the optimal path length achieved by the traditional ant colony algorithm is 31.17 m, stabilizing after 23 iterations. The improved ant colony algorithm from [12] stabilizes after 12 iterations, with an optimal path length reduced to 29.79 m. In comparison, the improved ant colony algorithm proposed in this research stabilizes after just five iterations, achieving an optimal path length of 28.96 m. These results indicate that the improved ant colony algorithm presented in this study converges quickly and significantly reduces the number of turning points compared to the other two algorithms. As shown in the comparative results in Table 3, even though each of the three algorithms can successfully complete the path planning from the start to the destination, there are significant differences in algorithm performance as reflected by the indicators. Compared to the traditional ant colony algorithm, the improved ant colony algorithm in [12] shortens the best path length by 4.42%, the count of turning points by 13.63%, and the count of iterations by 10%. The improved ant colony algorithm presented in this research performs considerably superior to the traditional ant colony algorithm. It reduces the minimum path length by 7.09%, and more importantly, it decreases the count of iterations and turning points by 47.8% and 78.2%, respectively. This improvement enhances search efficiency and effectiveness, making it better suited to the practical requirements of inspection robot route planning. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum number of iterations \(K\) & 120 \\ Number of ants \(M\) & 60 \\ Pheromone importance factor \(\alpha\) & 1 \\ Expectation heuristic information importance factor \(\beta\) & 2 \\ Pheromone evaporation coefficient \(\rho\) & 0.8 \\ Intensity of pheromone \(Q\) & 10 \\ Number of elite ants \(b\) & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental parameter settings. Figure 2: A 20 \(\times\) 20 algorithm simulation comparison. (**a**) Path planning of the traditional ant colony algorithm. (**b**) Convergence curve of the traditional ant colony algorithm. (**c**) Literature [12] improved the ant colony algorithm for path planning. (**d**) Literature [12] improved the convergence curve of the ant colony algorithm. (**e**) This paper improves the path planning of the ant colony algorithm.