Model-based navigation is a promising approach for autonomous navigation of small drones in challenging conditions such as GNSS denied flight scenarios. However, the lack of analysis of aerodynamic model structure for model-based navigation applications on delta-wing UAVs, characterized by a reduced number of control surfaces, has hindered its practical implementation. In this study, we propose a methodology for generalizing an aerodynamic model experimentally determined for a specific platform to a family of platforms sharing comparable physical characteristics by employing in-flight tuning. The experimental results show that the proposed methodology significantly improves navigation performance under GNSS outage, compared to traditional autonomous navigation approaches, for the model adapted to a second delta-wing platform. This indicates that the proposed methodology can be used to adapt aerodynamic models to different delta-wing UAV platforms of similar size, enabling reliable model-based navigation in challenging environments. This work contributes to the advancement of autonomous navigation technology for small drones, particularly in applications where GNSS signals are unavailable or unreliable.
On the scalability of experimentally determined aerodynamic model for model-based navigation on a delta-wing UAV
19.06.2023
1936133 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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