# A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm
## 1 Introduction  
Under the impetus of the Internet of Things, Artificial Intelligence, and Industry 4.0, the manufacturing industry is rapidly moving toward intelligence and interconnection. However, in complex automated transportation scenarios, many requirements are difficult to meet due to the limitations of a single AGV. In contrast, dual-AGV systems are more efficient and versatile in transportation and application, and they are capable of performing complex tasks such as collaborative assembly and handling, making them an important development direction [1]. Compared to single AGVs, the navigation system of dual AGVs is more challenging and complex. In addition to ensuring the navigation accuracy of individual AGVs, it is also necessary to ensure the relative spatial pose accuracy of dual AGVs during motion to avoid collisions and other issues [2]. The AGV navigation system typically comprises three modules: information perception, path planning, and motion control. Among these, path planning acts as the bridge connecting information perception and motion control [3]. Its objective is to search for viable paths from the starting point to the goal point while circumventing obstacles [4]. Thus, realizing ganged transportation with dual AGVs hinges significantly on path-planning technology. Common path-planning algorithms include traditional methods such as the A* algorithm (pronounced \"A-star\"), which is known for its completeness in graph traversal and pathfinding [5]; the artificial potential field method, which plans paths by simulating the repulsive force of obstacles and the attractive force of target points [6]; the rapidly-exploring random tree (RRT) algorithm, which is effective for efficient path searches in non-convex high-dimensional spaces [7]; and the Dijkstra algorithm, which finds the shortest path between nodes in a weighted graph [8]. In addition to these traditional methods, intelligent optimization algorithms have also been widely applied. These include the ant colony optimization (ACO) algorithm, which mimics the behavior of ants in finding the optimal path [9]; neural network approaches that use interconnected nodes or neurons to model the way the human brain processes data through machine learning [10]; the particle swarm optimization (PSO) algorithm, which iteratively improves candidate solutions [11]; and the GA, a metaheuristic inspired by natural selection for solving optimization problems [12]. Nevertheless, the current research has predominantly concentrated on path planning for single AGVs, with fewer studies focusing on dual-AGV-ganged path planning, which requires in-depth research. In the path planning of dual-AGV-ganged systems, a critical issue is to ensure that the leader AGV can plan collision-free paths for both AGVs. This requires comprehensive consideration of the turning collisions and positional changes in the dual AGVs to avoid collisions during turning maneuvers. To address these challenges, [PERSON] et al. [13] introduced the artificial potential field method to enhance the global optimization accuracy of ant colony algorithms, thereby identifying the optimal path for the leader AGV to guide the path planning of the follower AGV. [PERSON] et al. [14] simulated the relationship between AGVs and obstacles, utilizing spring forces to adjust paths and achieve multi-AGV path planning. [PERSON] et al. [15], based on the predicted distance between leader-follower AGVs and obstacles, planned paths for a leader AGV and adjusted formations to achieve obstacle avoidance. [PERSON] et al. [16] improved the evaluation function by obtaining the relative distances and angles between AGVs through mutual communication, resolving the multi-AGV path-planning problem. [PERSON] et al. [17] proposed a distributed rolling-time domain local-motion planner to avoid collisions with static and dynamic obstacles. [PERSON], [PERSON] et al.