Towards monocular vision-based autonomous flight through deep reinforcement learning

Date

2022-03-09

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Department

Type

Article

ISSN

0957-4174

Format

Free to read from

Citation

Kim M, Kim J, Jung M, Oh H. (2022) Towards monocular vision-based autonomous flight through deep reinforcement learning, Expert Systems with Applications, Volume 198, July 2022, Article number 116742

Abstract

This paper proposes an obstacle avoidance strategy for small multi-rotor drones with a monocular camera using deep reinforcement learning. The proposed method is composed of two steps: depth estimation and navigation decision making. For the depth estimation step, a pre-trained depth estimation algorithm based on the convolutional neural network is used. On the navigation decision making step, a dueling double deep Q-network is employed with a well-designed reward function. The network is trained using the robot operating system and Gazebo simulation environment. To validate the performance and robustness of the proposed approach, simulations and real experiments have been carried out using a Parrot Bebop2 drone in various complex indoor environments. We demonstrate that the proposed algorithm successfully travels along the narrow corridors with the texture free walls, people, and boxes.

Description

Software Description

Software Language

Github

Keywords

Obstacle avoidance, Depth estimation, Vision-based, Deep reinforcement learning, Q-learning, Navigation decision making

DOI

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

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Relationships

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