Browsing by Author "Lochner, Jacobus Nicolaas"
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- ItemMotion planning for a rotary-wing UAV in dynamic environments(Stellenbosch : Stellenbosch University, 2020-03) Lochner, Jacobus Nicolaas; Van Daalen, Corne E.; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: In order for a fully autonomous unmanned aerial vehicle (UAV) to navigate safely in dynamic environments, a conflict free trajectory between an initial and goal state of a vehicle needs to be planned quickly and effectively. Most trajectory planning methods today only consider static environments and do not act on environmental changes that might occur. In order for a UAV to navigate through dynamic environments, a motion planning algorithm needs to be employed that is capable of dealing with these conditions. These motion planning algorithms are often applied in conjunction with a local planning method that ensures that the trajectories generated by the planning algorithm adhere to the dynamic constraint of the UAV. The goal of this project is to find a motion planning algorithm that will provide safe flight for a rotary-wing UAV from an initial to a goal state in environments where dynamic obstacles exist. The motion planning algorithm implemented is based on the rapidly-exploring random tree (RRT) that is altered for dynamic environments and is called the real-time optimal RRT (RT-RRT*). Important changes were made to the RT-RRT* to increase the performance of the algorithm. The motion planning algorithm should adhere to the constraint of the vehicle, which is solved by employing a generic local planning method that make use of geometric-based motion primitives to construct trajectories that adhere to the constraints of the vehicle. The complete motion planning algorithm is tested thoroughly in various simulated environments and the performance of the algorithm is analysed. The motion planning algorithm was proven to be effective in sparse environments but struggled in more cluttered environments with multiple dynamic obstacles. Obstacle estimation was then implemented to try and improve the motion planning algorithm for cluttered environments, which proved to be an effective solution for avoiding dynamic obstacles. The trajectories generated by the motion planning algorithm was given to a realistic vehicle model to verify if the rotary-wing UAV will be able to accurately follow the generated trajectory. The vehicle model and controllers was previously designed and verified to be accurate during practical flight tests, which means that this model is an accurate representation of a real world vehicle.