Rapid Uncertainty Propagation and Chance-Constrained Path Planning for Small Unmanned Aerial Vehicles
This work addresses the need for a traffic management system to handle increasing numbers of small unmanned aircraft in the national airspace, representing an incremental improvement in path planning methods.
The paper tackled the problem of validating flight plans and planning paths for small unmanned aerial vehicles by developing a computationally efficient algorithm that combines linear covariance propagation, quadratic programming-based collision detection, and a Dynamic, Informed RRT* algorithm, resulting in detailed numerical examples for fixed-wing and quadrotor models.
With the number of small Unmanned Aircraft Systems (sUAS) in the national airspace projected to increase in the next few years, there is growing interest in a traffic management system capable of handling the demands of this aviation sector. It is expected that such a system will involve trajectory prediction, uncertainty propagation, and path planning algorithms. In this work, we use linear covariance propagation in combination with a quadratic programming-based collision detection algorithm to rapidly validate declared flight plans. Additionally, these algorithms are combined with a Dynamic, Informed RRT* algorithm, resulting in a computationally efficient algorithm for chance-constrained path planning. Detailed numerical examples for both fixed-wing and quadrotor sUAS models are presented.