Collision-free Navigation System for Robotic Helicopter

Tato práce je zaměřena na vytvoření bezkolízního navigačního systému pro robotickou helikopteru. Během této práce je odvozen a linearizován matematický model kvadrakoptéry. Regulátor pro UAV je navržen na základě tohoto modelu. Řešení problému s lokalizací je poskytnuto ve formě Kalmanova filtru. Pro uložení konfiguračního prostoru robota bude navržena prostorově efektivní struktura octree a pro navigaci v tomto prostředí je použit algoritmus A*. Implementace navržených algoritmů je provedena v programovacím jazyce C++ a testována v simulačním prostředí Webots.This work is focused on the creation of a collision-free navigation system for a robotic helicopter. During this work the matematical model of the quadracopter is derived and linearized. The regulator for the UAV is designed based on this model. The solution for the localization problem is provided in the form of Kalman filter. Space-efficient octree structure is proposed to store robot configuration space and A* algorithm is used for navigation in this environment. The implementation of the proposed algorithms is done in programming language C++ and tested in simulation environment Webots

Learning-based wildfire tracking with unmanned aerial vehicles

This project attempts to design a path planning algorithm for a group of unmanned aerial vehicles (UAVs) to track multiple spreading wildfire zones on a wildland. Due to the physical limitations of UAVs, the wildland is partially observable. Thus, the fire spreading is difficult to model. An online training regression neural network using real-time UAV observation data is implemented for fire front positions prediction. The wildfire tracking with UAVs path planning algorithm is proposed by Q-learning. Various practical factors are considered by designing an appropriate cost function which can describe the tracking problem, such as importance of the moving targets, field of view of UAVs, spreading speed of fire zones, collision avoidance between UAVs, obstacle avoidance, and maximum information collection. To improve the computation efficiency, a vertices-based fire line feature extraction is used to reduce the fire line targets. Simulation results under various wind conditions validate the fire prediction accuracy and UAV tracking performance.Includes bibliographical references