Autonomous UAV navigation: A DDPG-based deep reinforcement learning approach

Omar Bouhamed, Hakim Ghazzai, Hichem Besbes, Yehia Massoud

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous action space is designed to train the UAV to navigate through or over the obstacles to reach its assigned target. A customized reward function is developed to minimize the distance separating the UAV and its destination while penalizing collisions. Numerical simulations investigate the behavior of the UAV in learning the environment and autonomously determining trajectories for different selected scenarios.
Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781728133201
StatePublished - Jan 1 2020
Externally publishedYes

Fingerprint

Dive into the research topics of 'Autonomous UAV navigation: A DDPG-based deep reinforcement learning approach'. Together they form a unique fingerprint.

Cite this