Indoor Path Planning for an Unmanned Aerial Vehicle via Curriculum Learning

TL;DR

This paper presents a reinforcement learning approach with curriculum learning for indoor UAV path planning, achieving high goal success rates in obstacle avoidance tasks within simulated environments.

Contribution

It introduces a curriculum learning framework for UAV path planning that improves learning efficiency and success rates in obstacle-rich indoor environments.

Findings

Maximum goal rates of 71.2% and 88.0% achieved.

Reinforcement learning effectively enables obstacle avoidance.

Curriculum learning enhances training efficiency.

Abstract

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%.