A hierarchical navigation decision-making method for UAV swarms in unknown communication-constrained environments
- 1CETC 54th Research Institute, Shijiazhuang, China
- 2State Key Laboratory of comprehensive PNT Network and Equipment Technology, Shijiazhuang, China
Abstract
In recent years, Unmanned Aerial Vehicles (UAVs) have gradually been widely used in various fields such as regional search and disaster relief, and the development of related technologies has also experienced unprecedented growth. Compared to individual UAVs, the collaborative execution of tasks by UAV swarms has more advantages, but it is often difficult to achieve fast and accurate autonomous navigation and obstacle avoidance capabilities in unknown complex obstacle environments due to limitations in computing load and inter-UAV communication capabilities. To solve this problem, a hierarchical navigation decision-making framework for non-communication UAVs in unknown environments is proposed in this paper, which decomposes the UAV navigation planning task into an upper-layer global planning module and a lower-layer autonomous navigation and obstacle avoidance module. For the lower-layer module, an enhanced hybrid feature extraction network is designed, accompanied by a dual-stage training strategy that integrates traditional optimization methods with reinforcement learning. The upper-layer module incorporates a hybrid control strategy combining conventional search methods. Based on the ROS framework, simulation experiments for UAV swarm navigation were systematically conducted. The experimental results demonstrate that the proposed algorithm achieves autonomous navigation decision-making for multiple UAVs in complex unknown obstacle environments without relying on inter-UAV communication, showing significant advantages compared to existing approaches.
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BibTeX
@article{xiong2026hierarchical,
title = {{A hierarchical navigation decision-making method for UAV swarms in unknown communication-constrained environments}},
author = {Huajie Xiong and Baoguo Yu and Jingkui Zhang and He Dong},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0339225},
url = {https://doi.org/10.1371/journal.pone.0339225},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A hierarchical navigation decision-making method for UAV swarms in unknown communication-constrained environments
AU - Huajie Xiong
AU - Baoguo Yu
AU - Jingkui Zhang
AU - He Dong
PY - 2026
DA - 2026/09/09
JO - PLOS One
DO - 10.1371/journal.pone.0339225
UR - https://doi.org/10.1371/journal.pone.0339225
AB - In recent years, Unmanned Aerial Vehicles (UAVs) have gradually been widely used in various fields such as regional search and disaster relief, and the development of related technologies has also experienced unprecedented growth. Compared to individual UAVs, the collaborative execution of tasks by UAV swarms has more advantages, but it is often difficult to achieve fast and accurate autonomous navigation and obstacle avoidance capabilities in unknown complex obstacle environments due to limitations in computing load and inter-UAV communication capabilities. To solve this problem, a hierarchical navigation decision-making framework for non-communication UAVs in unknown environments is proposed in this paper, which decomposes the UAV navigation planning task into an upper-layer global planning module and a lower-layer autonomous navigation and obstacle avoidance module. For the lower-layer module, an enhanced hybrid feature extraction network is designed, accompanied by a dual-stage training strategy that integrates traditional optimization methods with reinforcement learning. The upper-layer module incorporates a hybrid control strategy combining conventional search methods. Based on the ROS framework, simulation experiments for UAV swarm navigation were systematically conducted. The experimental results demonstrate that the proposed algorithm achieves autonomous navigation decision-making for multiple UAVs in complex unknown obstacle environments without relying on inter-UAV communication, showing significant advantages compared to existing approaches.
SN - 1932-6203
ER -