# Autonomous UAV Navigation Using Reinforcement Learning

arXiv: https://arxiv.org/abs/1801.05086 · HTML: https://mlsanity.com/paper/1801.05086/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2018.

## Built on by (most cited first, 6 of 6)

- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization](https://mlsanity.com/paper/2105.11013/) (2021)
- [Reliable and Efficient Data Collection in UAV-based IoT Networks](https://mlsanity.com/paper/2311.05303/) (2023)
- [AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay](https://mlsanity.com/paper/2210.12892/) (2022)
- [Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning](https://mlsanity.com/paper/2303.09013/) (2023)
- [Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics](https://mlsanity.com/paper/2310.08830/) (2023)

## Abstract (arXiv)

Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. This paper provides a framework for using reinforcement learning to allow the UAV to navigate successfully in such environments. We conducted our simulation and real implementation to show how the UAVs can successfully learn to navigate through an unknown environment. Technical aspects regarding to applying reinforcement learning algorithm to a UAV system and UAV flight control were also addressed. This will enable continuing research using a UAV with learning capabilities in more important applications, such as wildfire monitoring, or search and rescue missions.