Path planning for an unmanned aerial vehicle in dynamic environments

Unmanned Aerial Vehicles (UAVs) have wide potential to carry out many autonomous applications. Considering autonomous applications, one of the most basic requirements of an aerial vehicle when travelling from one point to another is absolutely a reliable obstacle avoidance mechanism. In this thesis, a path plan is designed for a quadrotor model, from a starting point to a target point in an environment with static and dynamic obstacles. A laser distance sensor is used to detect obstacles. While building a path plan with obstacle avoidance, A* and VFH+ are used together to ensure that the aerial vehicle reaches the target both by the shortest path and by avoiding static and dynamic obstacles. A* is a global path planning method where environmental information is available, while the VFH+ algorithm can only work in a local area according to sensor data. The Robot Operating System (ROS), which provides convenience in combining hardware and software tools, is used for the problem examined within the scope of the thesis. The tests of the algorithm are carried out in Gazebo simulator, which can work integrated with ROS. Algorithm codes written in Matlab are tested in the aerial vehicle in Gazebo environment using ROS Toolbox. The operation of the algorithm in different static and dynamic environments is evaluated. Compared to the algorithms built for the navigation of mobile robots (Navigation Stacks) on the ROS platform, the proposed method is successful in terms of the speed of implementation of the path plan.