グローバル・ローカルマップ情報を用いた深層強化学習によるUAV経路計画
UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning
圧縮したグローバルマップと近傍の非圧縮ローカルマップを組み合わせた深層強化学習により、広域環境でも効率的に動作するUAV経路計画手法を提案し、被覆調査とデータ収集の異なるミッションで同一アーキテクチャのDDQNを訓練して有効性を示した。
著者: Mirco Theile, Harald Bayerlein, Richard Nai, David Gesbert, Marco Caccamo
分類: cs.RO, cs.LG
原文アブストラクト
Path planning methods for autonomous unmanned aerial vehicles (UAVs) are typically designed for one specific type of mission. This work presents a method for autonomous UAV path planning based on deep reinforcement learning (DRL) that can be applied to a wide range of mission scenarios. Specifically, we compare coverage path planning (CPP), where the UAV's goal is to survey an area of interest to data harvesting (DH), where the UAV collects data from distributed Internet of Things (IoT) sensor devices. By exploiting structured map information of the environment, we train double deep Q-networks (DDQNs) with identical architectures on both distinctly different mission scenarios to make movement decisions that balance the respective mission goal with navigation constraints. By introducing a novel approach exploiting a compressed global map of the environment combined with a cropped but uncompressed local map showing the vicinity of the UAV agent, we demonstrate that the proposed method can efficiently scale to large environments. We also extend previous results for generalizing control policies that require no retraining when scenario parameters change and offer a detailed analysis of crucial map processing parameters' effects on path planning performance.
