| Summary: | Background: In recent years, automatic guided vehicles (AGVs) have developed rapidly and been widely applied in intelligent transportation, cargo assembly, military testing, and other fields. One of the key issues 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 fast arrival at the destination. The global planning method planning results as the ideal path should meet the requirements of as few turns as possible, short planning time, and continuous path curvature. Methods: We propose a global path-planning method based on an improved A * algorithm. And the robustness of the algorithm is verified by simulation experiments in typical multi obstacles and indoor scenarios. To improve the efficiency of pathfinding time, we increase the heuristic information weight of the target location and avoided the invalid cost calculation of the obstacle areas in the dynamic programming process. Then, the optimality of the number of turns in the path is ensured based on the turning node backtracking optimization method. Since the final global path needs to satisfy the AGV kinematic constraints and the curvature continuity condition, we adopt a curve smoothing scheme and select the optimal result that meets the constraints. Conclusions: Simulation results show that the improved algorithm proposed in this paper outperforms the traditional method and can help AGVs improve the efficiency of task execution by efficiently planning a path with low complexity and smoothness. Additionally, this scheme provides a new solution for global path planning of unmanned vehicles.
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