# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 5 Experimental Verification  
The inspection robot utilized in this experiment is constructed on an R5 series Ackermann chassis, featuring a four-wheel drive system equipped with 125 mm rubber wheels. It is powered by an STM32 microcontroller and utilizes a JETSON NANO as the main control board. The robot is outfitted with various sensors, including a binocular camera, an inertial measurement unit (IMU), and a 2D lidar system, which collectively facilitate precise environmental perception and navigation. To rigorously assess the reliability and authenticity of the improved algorithm presented in this research, the algorithm was implemented within the Ubuntu 18.04 Melodic operating system, employing the ROS (Robot Operating System) for robotic control. A simulated environment for the four-wheel Ackermann robot was developed in Gazebo. Figure 5 illustrates the planned path. The physical verification of the improved inspection robot is shown in Figure 6 below. A real simulation environment incorporating multiple obstacles was constructed to evaluate the performance of the inspection robot. The robot was assigned 2D Nav Goal commands to autonomously navigate to specified target points. The time taken to complete the task was recorded at three intervals, clearly demonstrating the robot's ability to plan the most efficient path in a complex environment, while autonomously inspecting and avoiding obstacles. These results were further validated through comparison with the \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
**Algorithm** & **Optimal Path Length/m** & **Running Time/s** & **The Number of First Convergence Iterations** & **Compared with the Traditional Point** & **Compared with the Traditional Algorithm, the Path Reduction Rate of the Number of Inflection Points** & **Compared with the Traditional Algorithm, the Reduction Rate of the Number of Inflection Points** & **Compared with the Traditional Algorithm, the Reduction Rate of the Number of Inflection Points** & **Compared with the Traditional Algorithm, the Reduction Rate of the Number of Inflection Points** & **Compared with the Traditional Algorithm, the Reduction Rate of the Number of Inflection Points** & **Compared with the Traditional Algorithm, the Reduction Rate of the Number of Inflection Points** & **Compared with the Traditional Algorithm, the Reduction Rate of the Number of Inflection Points** & **Compared with the Traditional Algorithm, the Reduction Rate of the Number of Inflection Points** & **Compared with the Traditional Algorithm, the Reduction Rate of the Number of Inflection Points** & **Compared with the Traditional Algorithm, the Reduction Rate of the Number of Inflection Points** & **Compared with the Traditional Algorithm, the Reduction Rate of the Number of Inflection Points** & **Additional Algorithm, the Reduction Rate of the Number of Inflection Points** & **Algorithm, the Reduction Rate of the Number of Inflection Points** & **Algorithm, the Reduction Rate of the Number of Inflection Points** & **Reduction Rate of the Number of Inflection Points** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A comparison of experimental results of 50 \(\times\) 50 static grid environment. contents verified by the Robot Operating System (ROS), confirming the reliability and authenticity of the proposed algorithm. As shown in the comparison of positions in Table 6, the path length of the improved ant colony algorithm presented in this research was reduced by 33% compared to the traditional ant colony algorithm. Additionally, the quantity of turning points was cut by 50%, and the running time was shortened by 49%. The discrepancy between the simulation results and the tabulated values is within \(\pm\)5%. Therefore, the algorithm in this research has a significant advantage over the traditional ant colony algorithm. It can plan the most  
Figure 5: The ant colony algorithm planning path. (**a**) The traditional ant colony algorithm planning path.