# Enhanced Ant Colony Algorithm Based on Islands for Mobile Robot Path Planning
## 7 Real Vehicle Verification and Results  
To validate the effectiveness of the EACI algorithm in a real-world environment, an experimental platform was constructed consisting of a Mini ROS vehicle (which produced by WHEELTEC Co., Ltd. of China in Dongguan) and a personal computer. As shown in Figure 31, the mini vehicle, measuring 265 mm \(\times\) 205 mm \(\times\) 195 mm, served as host computer, while the personal computer acted as the client computer. Both the host and the client were equipped with the 64-bit Ubuntu 18.04 operating system and the Robot Operating System (ROS), Melodic. Communication between the two was established via a Wi-Fi network using the SSH protocol, enabling remote control of the mini ROS vehicle through its IP address.  
The schematic diagram of the Mini ROS vehicle is shown in Figure 31. The main control system of the Mini ROS vehicle includes an STM32 control board (model STM32F407 VET6)equipped with an MPU9250 9-axis sensor, a Jetson Nano 4 GB board, a laser radar (M10P), four 12 V DC motors with Giant Magneto Resistance (GMR) encoders, a PlayStation 2 (PS2) wireless controller, and a wireless USB adapter. The laser radar is mounted on top of the vehicle at a height of 180 mm above the ground, and it is primarily used for environmental perception and obstacle detection. Detailed specifications are provided in Table 4.  
After starting the Mini ROS vehicle, the PC connects to it via Wi-Fi and remotely logs into the vehicle system using the Secure Shell (SSH) protocol. The mapping program is launched by entering the command roslaunch turn_on_wheeltec_robot_mapping.launch. Then, a new terminal window is opened on the PC to start the Rviz visualization tool, and the PS2 wireless controller is used to control the vehicle's movement. The Gmapping SLAM algorithm is employed to construct a grid map. The experimental area measures 480 cm \(\times\) 300 cm and includes various obstacles, such as L-shaped, U-shaped, and cubic structures. The inner grid regions of the L-shaped and U-shaped obstacles are prone to causing deadlock in the ant colony algorithm, thus providing a suitable test scenario for verifying the proposed algorithm's ability to avoid deadlocks. To ensure accurate map construction, each grid cell was set to a size of 5 cm \(\times\) 5 cm. After completing the mapping process, the experimental environment map was exported using the map-saving package, the experimental environment and a map model generated by Laser Radar are displayed in Figure 32, where black areas represent obstacles and gray areas represent navigable regions.  
In the experiment, the robot navigated fully autonomously using the algorithm proposed in this article. Its movement was controlled by a PID controller that followed the path generated by the algorithm. A position feedback system, based on the odometer, precisely measured the robot's displacement using encoders and relayed this information back to the controller to ensure accurate path tracking.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Detection Range (m)** & **Distance Accuracy (cm)** & **Angular Resolution** & **Scan Frequency (Hz)** & **Field of View** \\ \hline Laser radar (M10P) & 0.1–30 & \(\pm\)3 & 0.22 & 12 & 360 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detailed parameter settings of the laser radar.  
Figure 31: Core structure of the Mini ROS vehicle.  
The EACI algorithm was implemented as a plugin and integrated into the move_base package, with configuration managed through the move_base_params.yaml parameter file. After defining the start points and endpoints set on the map, the robot can execute path planning using the proposed algorithm. As shown in Figure 33, the DDMR successfully navigated along the designated navigation route and reached the destination without encountering deadlock grids. This finding highlights the robot's strong navigation capabilities and further validates the effectiveness of the algorithm in specialized environments.