Improved UAV Path Planning in Urban Environment Based on A-Star Method

DOI

10.2991/978-94-6463-821-9_110How to use a DOI?

Keywords

UAV; Urban Environment; Path Planning; Control Staategies

Abstract

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 A-star 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 A-star, while the shortest distance between obstacles increases by around 50%. These findings provide theoretical and technical support for efficient UAV applications in urban environments.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiaqi Song
AU  - Rongjun Zhou
PY  - 2025
DA  - 2025/08/31
TI  - Improved UAV Path Planning in Urban Environment Based on A-Star Method
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 1157
EP  - 1167
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_110
DO  - 10.2991/978-94-6463-821-9_110
ID  - Song2025
ER  -