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

arXiv: https://arxiv.org/abs/2010.06917 · HTML: https://mlsanity.com/paper/2010.06917/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks](https://mlsanity.com/paper/2207.06131/) (2022)
- [Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning](https://mlsanity.com/paper/2309.03157/) (2023)
- [ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting](https://mlsanity.com/paper/2405.11013/) (2024)
- [Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2601.14092/) (2026)

## Abstract (arXiv)

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.