3D Autonomous Navigation of UAVs: An Energy-Efficient and Collision-Free Deep Reinforcement Learning Approach

Yubin Wang*, Karnika Biswas, Liwen Zhang, Hakim Ghazzai, Yehia Massoud

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Energy consumption optimization is crucial for the navigation of Unmanned Aerial Vehicles (UAV), as they operate solely on battery power and have limited access to charging stations. In this paper, a novel deep reinforcement learning-based architecture has been proposed for planning energy-efficient and collision-free paths for a quadrotor UAV. The proposed method uses a unique combination of remaining flight distance and local knowledge of energy expenditure to compute an optimized route. An information graph is used to map the environment in three dimensions and obstacles inside a pre-determined neighbourhood of the UAV are removed to obtain a local as well as collision-free reachable space. Attention-based neural network forms the key element of the proposed reinforcement learning mechanism, that trains the UAV to autonomously generate the optimized route using partial knowledge of the environment, following the trajectories from which, the UAV is driven by the trajectory tracking controller.

Original languageEnglish (US)
Title of host publicationAPCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages404-408
Number of pages5
ISBN (Electronic)9781665450737
DOIs
StatePublished - 2022
Event2022 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2022 - Virtual, Online, China
Duration: Nov 11 2022Nov 13 2022

Publication series

NameAPCCAS 2022 - 2022 IEEE Asia Pacific Conference on Circuits and Systems

Conference

Conference2022 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2022
Country/TerritoryChina
CityVirtual, Online
Period11/11/2211/13/22

Keywords

  • autonomous navigation
  • Deep reinforcement learning
  • energy efficiency
  • motion planning
  • unmanned aerial vehicles

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computer Science Applications

Fingerprint

Dive into the research topics of '3D Autonomous Navigation of UAVs: An Energy-Efficient and Collision-Free Deep Reinforcement Learning Approach'. Together they form a unique fingerprint.

Cite this