Adapting Random Tree Search to Fixed Wing Aerial Vehicles with Closed-Loop Prediction and Hybrid Control

Path planning for mobile robots is a developing and evolving area of robotics research to improve their autonomous capabilities.Unmanned Aerial Vehicle (UAV)s present difficult challenges to path planning algorithms that require unique solutions to overcome.This work presents improvements to existing planning algorithms for fixed-wing UAVs.We propose three aims as follows: 1) utilize hybrid control to improve flight planning and performance capabilities, 2) leverage closed-loop prediction of fixed-wing aerial vehicles for path planning decisions, and 3) implement Reinforcement Learning (RL) techniques to learn control policies for fixed-wing aerial vehicles.This thesis will cover the methods to further these aims, including control architecture, planning algorithms, preliminary results, and the final objectives we hope to achieve.The key contributions to highlight in this work are 1) novel control schemes for fixed-wing UAVs, 2) improvements made to closedloop path planning that leverage the forward simulation for prediction, and 3) integrating reinforcement learning for improving the hybrid control performance.xii