Autonomous Drone Takeoff and Navigation Using Reinforcement Learning

Sana Ikli, Ilhem Quenel

2024

Abstract

Unmanned Aerial Vehicles, also known as drones, are deployed in various applications such as security and surveillance. They also have the key benefit of being able to operate without a human pilot, which make them suitable to access difficult areas. During autonomous flights, drones can crash or collide with an obstacle. To prevent such situation, they need an obstacle-avoidance solution. In this work, we are interested in the navigation with obstacle avoidance of a single drone. The goal is to autonomously navigate from an origin to a destination point, including takeoff, without crashing. Reinforcement learning is a valid solution to this problem. Indeed, these approaches, coupled with deep learning, are used to tackle complex problems in robotics. However, the works in the literature using reinforcement learning for drone navigation usually simplify the problem into 2-D navigation. We propose to extend these approaches to complete 3-D navigation by using a state-of-the-art algorithm: proximal policy optimization. To create realistic drone environments, we will use a 3-D simulator called Pybullet. Results show that the drone successfully takes off and navigates to the indicated point. We provide in this paper a link to our video demonstration of the drone performing navigation tasks.

Paper Citation

in Harvard Style

Ikli S. and Quenel I. (2024). Autonomous Drone Takeoff and Navigation Using Reinforcement Learning. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 63-71. DOI: 10.5220/0012296300003636

in Bibtex Style

@conference{icaart24,
author={Sana Ikli and Ilhem Quenel},
title={Autonomous Drone Takeoff and Navigation Using Reinforcement Learning},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={63-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012296300003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Autonomous Drone Takeoff and Navigation Using Reinforcement Learning
SN - 978-989-758-680-4
AU - Ikli S.
AU - Quenel I.
PY - 2024
SP - 63
EP - 71
DO - 10.5220/0012296300003636
PB - SciTePress