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A Multi-constrained Trajectory Representation Framework for Predictable and Low Error UAV Missions
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AI
DOI：10.1007/s10846-026-02384-y.png)
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Abstract
En 中文
The rapid growth of Unmanned Aerial Vehicle (UAV) applications across various sectors has underscored the need for advanced trajectory planning methods that ensure safe and predictable UAV operations in increasingly regulated airspace, such as the European U-space. This paper presents a novel approach to UAV trajectory planning that incorporates multiple constraints on the flight path, allowing desired speed and acceleration parameters to be defined for specific waypoint positions and times. By enabling precise control over dynamic parameters at each waypoint, our solution ensures smoother and more reliable UAV trajectories, minimizing the risks of abrupt or unpredictable manoeuvres that typically result from waypoint sequences with sudden changes. Experimental results based on use cases with varying requirements demonstrate that our constrained trajectory planning approach indeed provides more predictable and efficient UAV navigation, thereby meeting U-space mandates for safe and coordinated air traffic management. Depending on the experiment, the proposal reduces the error in following the planned mission by 20% to 90%. In addition, a moderate reduction in flight time has been observed in all experiments, reaching up to 40%.
Keywords:
Unmanned aerial vehicle
Trajectory planning
Unmanned aircraft system traffic management
U-space
Journal
J
IF:
2.8
Papers:
3.8K
Citations:
6.9K