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With the number of small Unmanned Aircraft Systems (sUAS) in the nationalairspace projected to increase in the next few years, there is growing interestin a traffic management system capable of handling the demands of this aviationsector. It is expected that such a system will involve trajectory prediction,uncertainty propagation, and path planning algorithms. In this work, we uselinear covariance propagation in combination with a quadratic programming-basedcollision 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 forchance-constrained path planning. Detailed numerical examples for bothfixed-wing and quadrotor sUAS models are presented.

Rapid Uncertainty Propagation and Chance-Constrained Path Planning for Small Unmanned Aerial Vehicles