Indoor Path Planning for an Unmanned Aerial Vehicle via Curriculum Learning

Abstract

Multi-agent reinforcement learning was performed in this study for indoor path planning of two unmanned aerial vehicles (UAVs). Each UAV performed the task of moving as fast as possible from a randomly paired initial position to a goal position in an environment with obstacles. To minimize training time and prevent the damage of UAVs, learning was performed by simulation. Considering the non-stationary characteristics of the multi-agent environment wherein the optimal behavior varies based on the actions of other agents, the action of the other UAV was also included in the state space of each UAV. Curriculum learning was performed in two stages to increase learning efficiency. A goal rate of 89.0% was obtained compared with other learning strategies that obtained goal rates of 73.6% and 79.9%.

An analysis on the role of school members for stable settlement of the High School Credit System

“…A pedestrian navigation system (PNS) in indoor environments [1], [2], where global navigation satellite system (GNSS) [3]- [10] signal access is difficult, is necessary, particularly for search and rescue (SAR) [11]- [13] operations in large buildings. Some systems rely on preinstalled infrastructure.…”