UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based UAV path planning method that effectively uses global and local map information, enabling adaptable, scalable, and generalizable navigation across diverse mission scenarios.

Contribution

The work presents a novel approach combining global and local maps with DRL, allowing UAVs to adapt to different missions without retraining and efficiently scale to large environments.

Findings

The method successfully applies to coverage and data harvesting missions.

It scales efficiently to large environments using combined map information.

The approach generalizes control policies across different scenarios without retraining.

Abstract

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.