{
  "id": "1911.02543",
  "version": "v1",
  "published": "2019-11-06T18:27:17.000Z",
  "updated": "2019-11-06T18:27:17.000Z",
  "title": "Rapid Uncertainty Propagation and Chance-Constrained Path Planning for Small Unmanned Aerial Vehicles",
  "authors": [
    "Andrew W. Berning Jr.",
    "Anouck Girard",
    "Ilya Kolmanovsky",
    "Sarah N. D'Souza"
  ],
  "comment": "Submitted to Advanced Control for Applications",
  "categories": [
    "cs.RO"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-11-06T18:27:17.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "small unmanned aerial vehicles",
      "chance-constrained path planning",
      "rapid uncertainty propagation",
      "validate declared flight plans",
      "programming-based collision detection algorithm"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}