In this study, reinforcement learning was applied to learning two-dimensional path planning including obstacle avoidance by unmanned aerial vehicle (UAV) in an indoor environment. The task assigned to the UAV was to reach the goal position in the shortest amount of time without colliding with any obstacles. Reinforcement learning was performed in a virtual environment created using Gazebo, a virtual environment simulator, to reduce the learning time and cost. Curriculum learning, which consists of two stages was performed for more efficient learning. As a result of learning with two reward models, the maximum goal rates achieved were 71.2% and 88.0%.
@article{arxiv.2108.09986,
title = {Indoor Path Planning for an Unmanned Aerial Vehicle via Curriculum Learning},
author = {Jongmin Park and Sooyoung Jang and Younghoon Shin},
journal= {arXiv preprint arXiv:2108.09986},
year = {2022}
}