# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 4 Experimental Simulation and Analysis
### 30 \(\times\) 30 Simulation Results and Analysis  
To further validate the advantages of the improved algorithm proposed in this research, path planning was conducted using all three algorithms in a more complex 30 \(\times\) 30 static grid. The resulting paths and convergence curves are illustrated in Figure 3. Due to the larger grid environment and the increased number of obstacles, the performance indicators of the algorithms have varied to different extents. As shown in Table 3, the traditional ant colony algorithm exhibited the poorest performance in the 30 \(\times\) 30 environment. It required 33 iterations to converge and stabilize, resulting in a path length of 66.18 m and 34 turning points. In comparison, the improved ant colony algorithm referenced in [12] introduces a mutation operation akin to those found in evolutionary algorithms. This adjustment allows for the reassignment of pheromone concentrations on certain paths, thereby enhancing its ability to evade local optima and improving global search capabilities. This algorithm converged after 21 iterations, achieving a path length of 44.52 m and 18 turning points. The improved ant colony algorithm presented in this study incorporates artificial potential field force into the heuristic function and employs a \"self-adjusting\" pheromone concentration update rule. This approach enables faster convergence, achieving an optimal path length of 43.46 m with only 15 iterations and 13 turning points. These results further demonstrate the efficacy of the improved ant colony algorithm proposed in this research. A comparison of the data presented in Table 4 reveals that the improved ant colony algorithm developed in this research achieved a path length reduction of 32.72% and 34.33% compared to the traditional ant colony algorithm and the enhanced ant colony algorithm in [12], respectively. Additionally, the proposed algorithm decreased the number of iterations by 36.36% relative to the traditional algorithm and by 54.55% compared to the algorithm in [12]. Furthermore, the volume of turning points was reduced by 47.06% and 61.76%, respectively. Although the improved ant colony algorithm in [12] significantly enhanced path planning length and execution time compared to the traditional algorithm, it still necessitates further refinement concerning the number of iterations. In contrast, the algorithm presented in this paper demonstrates marked superiority over both prior algorithms, showcasing its capability to plan optimal paths with exceptional stability and adaptability in complex environments. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Optimal Path Length/m**} & \multirow{2}{*}{**Running Time/s**} & \multicolumn{2}{c}{**The Number of First First Convergence**} & \multicolumn{2}{c}{**Compared with the Traditional Algorithm, the Path Length Reduction Rate of Iteration Times**} & \multicolumn{2}{c}{**Compared with the Traditional Algorithm, the Algorithm, the Reduction Rate of Iteration Times**} \\ \hline Traditional ant colony algorithm & 66.18 & 31.25 & 33 & 34 & — & — & — \\ Literature [12] algorithm & 44.52 & 20.64 & 21 & 18 & 32.72 & 36.36 & 47.06 \\ Improved ant colony algorithm & 43.46 & 15.66 & 15 & 13 & 34.33 & 79.16 & 54.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A comparison of the experimental results of the 30 \(\times\) 30 static grid environment.