# A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm
## 1 Introduction
[18] conducted online path planning for a leader AGV using the artificial potential field method and adjusted the linear and angular velocities of the follower AGV using sliding-mode control to meet obstacle avoidance requirements, offering diverse technical solutions for dual-AGV-ganged path planning. [PERSON] et al. [19] utilized the A* algorithm to find the shortest path between the starting point and the destination. They used the instantaneous actual position of the leader AGV and the calculated coordinates of the target point as parameters to determine the optimal path. [PERSON] et al. [20] combined Monte Carlo simulation and Bayesian optimization to guide the leader AGV in identifying sampling locations, integrating the ant colony algorithm to guide follower AGV path planning and to prevent collisions. [PERSON] et al. [21] employed the A* algorithm to plan paths for virtual leaders. They then utilized the Weight-Leader-Vicek Model (WLVM) for multi-AGV path planning, guiding AGVs based on the current positions and angles of the virtual leaders. [PERSON] et al. [22] proposed a multi-AGV local path planner capable of traversing obstacles, suitable for scenarios where the relative distances and angles between AGVs can vary during path planning. [PERSON] et al. [23] enhanced path search quality in ant colony algorithms by introducing pheromones for leader and follower ants, achieving collision-free path planning for multi-AGVs through formation transformations. [PERSON] et al. [24] effectively identified and defined unobstructed workspaces by constructing convex polygon trees (CPTs*) and incorporating a neural network-based rapid sampling method. This approach provided robust constraint conditions for path optimization and obstacle avoidance. Subsequently, they utilized these constraints, along with quadratic programming tools, to successfully solve the optimal path problem for multi-robot formations. This research not only demonstrates advanced path-planning techniques, but also provides significant theoretical guidance and practical application value for the path planning of dual AGVs. [PERSON] et al. [25] computed AGV paths and controls using consensus protocols, and these are suitable for dual-AGV system path planning with fewer obstacles. However, most of the aforementioned studies do not sufficiently consider the poses of the starting and goal points. When encountering obstacles, they often use formation-changing methods to achieve safe and collision-free path planning. This approach is unsuitable for scenarios where the relative distance and angle between the dual AGVs need to remain constant. Considering that traditional path-planning algorithms cannot remain effective in complex environments [26], this study chose genetic algorithms, which offer high robustness and wide applicability among intelligent algorithms [27]. Firstly, the leader-follower AGV system was assumed to be rigid to maintain constant relative poses. Then, based on single-AGV path-planning strategies, longitudinal path planning for the leader AGV was conducted while taking into account the steering risks of the follower AGV to thoroughly study and optimize the dual-AGV-ganged paths. The main contributions are as follows:  
(1) A safety steering radius for dual-AGV-ganged systems was designed by integrating the shapes of the leader and follower AGVs and the safety distance information into the global path-planning algorithm. This approach prevents steering collisions while effectively balancing path-planning effectiveness and cost map utilization. (2) The pose information of the starting and goal points was introduced, and a dual-AGV-ganged steering cost function was established in the initialization stage of the GA path. This function predicts the steering conditions of the follower AGV and generates an initial path that is safe and collision-free.