Indoor Path Planning for Multiple Unmanned Aerial Vehicles via Curriculum Learning

TL;DR

This paper introduces a curriculum learning approach for multi-agent reinforcement learning to efficiently plan indoor paths for multiple UAVs, achieving high success rates while minimizing training time and avoiding UAV damage.

Contribution

It proposes a two-stage curriculum learning method for multi-UAV indoor path planning, improving learning efficiency and success rates over other strategies.

Findings

Achieved an 89.0% goal success rate with curriculum learning.

Outperformed other learning strategies with goal rates of 73.6% and 79.9%.

Demonstrated effective simulation-based training for multi-UAV navigation.

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%.