UAV Autonomous Navigation System Based on Air–Ground Collaboration in GPS-Denied Environments
This paper explores breakthroughs from the perspective of UAV navigation architectures and proposes a UAV autonomous navigation method based on aerial–ground cooperative perception to address the challenge of UAV navigation in GPS-denied and unknown environments. The approach consists of two key components. Firstly, a mobile anchor trilateration and environmental modeling method is developed using a multi-UAV system by integrating the visual sensing capabilities of aerial surveillance UAVs with ultra-wideband technology. It constructs a real-time global 3D environmental model and provides precise positioning information, supporting autonomous planning and target guidance for near-ground UAV navigation. Secondly, based on real-time environmental perception, an improved D* Lite algorithm is employed to plan rapid and collision-free flight trajectories for near-ground navigation. This allows the UAV to autonomously execute collision-free movement from the initial position to the target position in complex environments. The results of real-world flight experiments demonstrate that the system can efficiently construct a global 3D environmental model in real time. It also provides accurate flight trajectories for the near-ground navigation of UAVs while delivering real-time positional updates during flight. The system enables UAVs to autonomously navigate in GPS-denied and unknown environments, and this work verifies the practicality and effectiveness of the proposed air–ground cooperative perception navigation system, as well as the mobile anchor trilateration and environmental modeling method.
Authors
- Chao Zhang (ORCID: https://orcid.org/0000-0003-0382-4833)
- Wei Chen (ORCID: https://orcid.org/0000-0001-5279-3266)
- Pengyu Yue (ORCID: https://orcid.org/0000-0002-1806-6337)
- Yan Huang (ORCID: https://orcid.org/0000-0003-3831-0007)
- Mao Shan (ORCID: https://orcid.org/0000-0002-5032-6581)
- Jing Xin (ORCID: https://orcid.org/0000-0002-1547-0991)
- Jiahang Zhao
Institutions
- The University of Sydney (AU)
- Australian Centre for Robotic Vision (AU)
- Xi'an University of Technology (CN)
- China XD Group (China) (CN)
- McGill University (CA)
Publication Details
- Journal
- Drones
- Published
- 2025-06-16
- DOI
- https://doi.org/10.3390/drones9060442
- Citations
- 11
- Primary Topic
- Robotic Path Planning Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 14.04
Funders
- National Natural Science Foundation of China