Indoor Path Planning for an Unmanned Aerial Vehicle via Curriculum Learning
This addresses efficient navigation for UAVs in obstacle-rich indoor settings, but it is incremental as it builds on existing RL methods with curriculum learning.
The study tackled indoor path planning for UAVs using reinforcement learning with curriculum learning, achieving maximum goal rates of 71.2% and 88.0% in a simulated environment.
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%.