Rapid uncertainty propagation and chance‐constrained path planning for small unmanned aerial vehicles

With the number of small unmanned aircraft systems 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 small unmanned aircraft system models are presented.There is growing interest in a traffic management system capable of handling the demands of small Unmanned Aircraft Systems (sUAS) in the national airspace, including trajectory validation and path planning. In this work, we use linear covariance propagation in combination with quadratic programming‐based collision detection 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 of both fixed‐wing and quadrotor sUAS models.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154386/1/adc223.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154386/2/adc223_am.pd