# A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm
## 5 Conclusions  
To enhance the safety and efficiency of the dual-AGV-ganged system, this study proposes a GA-based method for dual-AGV-ganged path planning. This method incorporates the pose information of starting and goal points and designs a safe turning radius for dual-AGV-ganged systems, effectively preventing steering collisions during global path planning. This allows dual AGVs to complete tasks swiftly, smoothly, efficiently, and safely. Simulation and real-vehicle experiments demonstrate that, whether in simple or complex scenarios, the proposed algorithm successfully plans globally collision-free and efficient paths for both leader and follower AGVs. Compared to traditional genetic algorithms, the path-planning capability is significantly improved, and its performance surpasses the widely used Astra and Dijkstra algorithms in ROS. By ensuring the safety, success rates, and optimal utilization of the cost map, the algorithm can plan paths that are both short and smooth. This provides a novel solution for dual-AGV-ganged path planning, showcasing significant practical value and promising application prospects.  
Compared to existing global path-planning algorithms for dual-AGV systems that mainly rely on setting a large inflation radius, the method proposed in this paper offers significant innovations. However, one of the potential challenges in future work is the introduction of dynamic obstacles into the path-planning process. The presence of dynamic obstacles in the path-planning process is a key issue in the path-planning process.  
Figure 21: A narrow area in a real environment.  
Figure 22: Comparison of the path-planning results using two of the algorithms in real narrow environments.  
obstacles could have a substantial impact on the proposed method, particularly in situations where the dual-AGV system needs to adapt to unforeseen changes in the environment. While our current approach effectively handles static obstacles, dynamic obstacles may increase the complexity of ensuring collision-free operation. A key challenge arises when the dual-AGV system is turning: if an obstacle appears on the side of the follower AGV, the leader AGV may successfully avoid it, but the follower AGV could still collide, leading to potential safety issues.  
Therefore, future research will focus on refining the algorithm to accommodate dynamic obstacles, ensuring that the dual-AGV system can respond in real-time without compromising safety. Furthermore, it is essential to explore multi-AGV-ganged path planning to fully leverage the advantages of our algorithm in balancing safety and cost map utilization. Additionally, we will delve into multi-AGV-ganged global dynamic path planning to enhance the overall efficiency and practicality of multi-AGV-ganged systems.  
[PERSON] and [PERSON] contributed to the original draft writing and conducted experiments. [PERSON] and [PERSON] contributed to optimizing the original draft writing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China (2021 YFB1716200) and the Research Funds for Leading Talents Program (048000514123686).  
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