Unmanned air vehicles (UAVs) have become an important area of research. UAVs are used in many environments which may have previously unknown obstacles or sources of danger. This research addresses the problem of obstacle mapping and path planning while the UAV is in flight. Online obstacle mapping is achieved through the use of a multi-resolution map. As sensor information is received, a quadtree is built up to hold the information based upon the uncertainty associated with the measurement. Once a quadtree map of obstacles is built up, we desire online path re-planning to occur as quickly as possible. We introduce the idea of a quadtree rapidly-exploring random tree (RRT), which will be used as the online path re-planning algorithm. This approach implements a variable sized step instead of the fixed-step size usually used in the RRT algorithm. This variable step uses the structure of the quadtree to determine the step size. The step size grows larger or smaller based upon the size of the area represented by the quadtree it passes through. Finally this approach is tested in a simulation environment. The results show that the quadtree RRT requires fewer steps on average than a standard RRT to find a path through an area. It also has a smaller variance in the number of steps taken by the path planning algorithm in comparison to the standard RRT.
College and Department
Ira A. Fulton College of Engineering and Technology; Mechanical Engineering
BYU ScholarsArchive Citation
Millar, Brett Wayne, "Multi-Resolution Obstacle Mapping with Rapidly-Exploring Random Tree Path Planning for Unmanned Air Vehicles" (2011). Theses and Dissertations. 2620.
UAV, map building, quadtree, rapidly-exploring random tree, path planning