Research on AGV task path planning based on improved A algorithm

Automatic guided vehicles (AGVs) have developed rapidly in recent years and have beenused in several fields, including intelligent transportation, cargo assembly, military testing, and others. A key issue in these applications is path planning. Global path planning results based on known environmental information are used as the ideal path for AGVs combined with local path planning to achieve safe and rapid arrival at the destination. Using the global planning method, the ideal path should meet the requirements of as few turns as possible, a short planning time, and continuous path curvature. We propose a global path-planning method based on an improved A* algorithm. The robustness of the algorithm was verified by simulation experiments in typical multiobstacle and indoor scenarios. To improve the efficiency of the path-finding time, we increase the heuristic information weight of the target location and avoid invalid cost calculations of the obstacle areas in the dynamic programming process. Subsequently, the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method. Because the final global path needs to satisfy the AGV kinematic constraints and curvature continuity condition, we adopt a curve smoothing scheme and select the optimal result that meets the constraints. Simulation results show that the improved algorithm proposed in this study outperforms the traditional method and can help AGVs improve the efficiency of task execution by planning a path with low complexity and smoothness. Additionally, this scheme provides a new solution for global path planning of unmanned vehicles.