Improved UAV Path Planning in Urban Environment Based on A-Star Method
Jiaqi Song, Rongjun Zhou · Advances in engineering research/Advances in Engineering Research · 2025
With the development of technology, unmanned aerial vehicles (UAVs) are increasingly utilized in civil applications.Several provinces and cities in China are vigorously developing the low-altitude economy, with industrial environments gradually maturing.However, traditional A-star algorithms produce complex results in UAV applications, potentially leading to collision risks and energy waste.This study aims to enhance the A-star algorithm to address UAV autonomous flight challenges in complex environments, prevent collisions, expand application scenarios, and reduce costs through shorter path distances.By analyzing urban environments and obstacle-dense areas, improvements are made to the path planning algorithm based on an optimized Astar framework.Specifically, dynamic local path adjustments via the Artificial Potential Field (APF) method are integrated into the globally optimal paths generated by the A-star algorithm.Algorithm performance is evaluated and compared, including pre-and post-improvement data analysis, assessment metrics, and result interpretation.Validation confirms that the improved algorithm reduces path length by approximately 8% compared to the original Astar, while the shortest distance between obstacles increases by around 50%.These findings provide theoretical and technical support for efficient UAV applications in urban environments.