|Description:||Unmanned Aerial Vehicles (UAVs) are expanding from open outdoor environments into more constrained indoor locations thanks to improvements in accuracy and precision of localization, navigation and control algorithms in recent years. New possibilities for deployment of autonomous UAV swarms into indoor environments emerge, leading to the development of high-level mission-oriented algorithms. The community achieved significant progress in the development of simultaneous localization and mapping (SLAM) algorithms that estimate the position of the robot in a gradually built map. However, it has turned out, that the position estimate in the map is not necessary for particular tasks, and the position relative to obstacles currently detected by the onboard sensors is more important. One of such missions is the so-called search-and-rescue, in which the UAV has to find a specific object (or person) in an unknown cluttered environment.
The goal of this project is to develop a navigation method that accepts a user input consisting of approximate direction of the searched object, and the UAV autonomously flies in the specified direction while modifying its trajectory to avoid obstacles in its vicinity. The area around the UAV is continuously scanned by a rotating laser rangefinder (LIDAR) that generates 360° planar laser scans of the surroundings. The solution should use the most recent laser scan to obtain the nest control input by employing potential field planning or adaptive model predictive control. The mission is accomplished when the searched object is detected by a passive camera or an emergency signal detecting device.