3D Path Planning for Autonomous Aerial Vehicles in Constrained Spaces

Determining how an autonomous Unmanned Aircraft System (UAS) should reach a goal position amidst obstacles is a challenging and difficult problem. This thesis treats the subject of path planning and trajectory generation for UAS, while utilizing the ability to move in all three spatial dimensions. The primary contributions of this thesis are an approximate path planner and a geodesic path planner. The approximate planner determines an approximated shortest path by building and searching a visibility graph. The geodesic planner identifies continuously differentiable geodesic paths as parametric equations determined by surface primitives given from the configuration space. Both planners are model independent and operate on the surface of the configuration space to identify a length minimizing path. A method is developed for generating convex configuration space obstacles from any point clouds or geometric meshes in work space. Two approaches are used for generating a trajectory from an existing path. The first uses Dubins curves to find a collision free continuously differentiable trajectory from an approximated path. The second approach applies to both types of paths and relies on formulating and solving an optimal control problem (OCP) using a Legendre pseudospectral method. The trajectories are validated through simulated flight using a helicopter model.