Indoor Path Planning for an Unmanned Aerial Vehicle via Curriculum Learning

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%.

Submitted to ICCAS 2021

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• Indoor Path Planning for Multiple Unmanned Aerial Vehicles via Curriculum Learning
• Empirical Modeling of Variance in Medium Frequency R-Mode Time-of-Arrival Measurements
• Simulation of Medium-Frequency R-Mode Signal Strength
• Movement Detection of Tongue and Related Body Parts Using IR-UWB Radar
• Construction of the Radio Map with Defective GPS Position Information