# A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm
## 4 Experimental Results and Discussion
### Real Experiment Introducing Starting-Point and Goal-Point Poses  
This study employed two identical Bulldog-star ROS AGVs for experimental validation. Both leader and follower AGVs were equipped with Intel i11-1165G7 processors, 64 GB RAM, 2.8 GHz clock speed, and an Ubuntu 20.04 ROS Noetic system.The processor was manufactured by Intel Corporation, Santa Clara, USA. As shown in Figure 13, these AGVs were equipped with four omni-directional McNamee wheels. Additionally, they were equipped with USB 3.0 interfaces, RealSense D435i depth cameras (with a measurement accuracy of 0.2-1%), IMU Brick 2.0 six-axis accelerometers (with a measurement accuracy of 0.01\({}^{\circ}\)), and VLP-32C LiDAR (with a maximum detection range of 200 m, measurement accuracy of 3 cm, and angular resolution of 0.33\({}^{\circ}\)) for environmental perception. The resolution of the cost map was set to 0.02, and the Gmapping SLAM algorithm was employed to meticulously model the environment, which was then converted into a map suitable for path planning using the Map server package.The manufacturer of the corresponding hardware and the software version are consistent with those in the simulation. The dimensions of the Bulldog-star ROS AGV were approximately 85 cm in length, 60 cm in width, and 77 cm in height, with the laser radar positioned at a height of about 55 cm above the ground. Based on these dimensions, eight static obstacles were set up to create the experimental environment, as depicted in Figure 14. The global cost map constructed using the Gmapping algorithm, as visualized through Rviz, is shown in Figure 15. The information presented in this map aligns with the cost map constructed during simulation. As shown in Figure 16, without introducing the pose information of the starting and goal points, all three algorithms neglected the starting-point pose during path planning. This may lead to the follower AGV colliding with surrounding obstacles when turning at the starting point, as indicated by the blue dashed box at the starting point in Figure 17a. Similarly, since these algorithms do not consider the goal-point pose, the follower AGV needs to adjust its posture after reaching the goal point, which may result in collisions with nearby boxes, as indicated by the blue dashed box at the goal point shown in Figure 17b. Additionally, the Astra and Dijkstra algorithms may cause collisions when planning the  
Figure 13: Leader–follower Bulldog-star ROS AGV. middle section of the path, as indicated by the green dashed box. This significantly increases the difficulty of local path planning and the risk of collisions. Figure 14: Experimental setup with real vehicles. Figure 15: Global cost Map 3. As shown in Figure 18, with the introduction of the starting-point pose, the path planning takes into account the collision risks during turning. If it is not possible to turn safely, the system proceeds straight to a safe location before turning, thereby avoiding the risk of collision during the initial turning. However, due to the lack of consideration for the goal-point pose, collision risks still exist near the goal point. As shown in Figure 18, with the simultaneous introduction of the starting- and goal-point poses, the algorithm comprehensively considers the safety issues of turning at each position and can plan the path in advance according to the target-point poses. This ensures that both leader and follower AGVs operate safely and are collision-free throughout the journey, aligning with the results of simulation experiments. As shown in Figure 19, the global cost Map 4 closely resembles the results of the simulation experiments. Due to the lack of consideration for the poses of the starting and goal points in the global path planning of ROS, a larger global inflation radius needs to be set to avoid collisions between the dual AGVs. Figure 17: In real-world environment key-point steering scenarios: (**a**) Initial-point steering scenario. (**b**) Goal-point steering scenario.