Edge computing assisted autonomous flight for UAV

Synergies between vision and communications

Journal article (2021)

Authors

Quan Chen National University of Defense Technology, Katholieke Universiteit Leuven
H. Zhu Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Lei Yang National University of Defense Technology
Xiaoqian Chen National University of Defense Technology
Sofie Pollin Katholieke Universiteit Leuven
Evgenii Vinogradov Katholieke Universiteit Leuven
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2021 Quan Chen, H. Zhu, Lei Yang, Xiaoqian Chen, Sofie Pollin, Evgenii Vinogradov
DOI
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https://doi.org/10.1109/MCOM.001.2000501
More Info
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

Autonomous flight for UAVs relies on visual information for avoiding obstacles and ensuring safe collision-free flight. In addition to visual clues, safe UAVs often need connectivity with the ground station. In this article, we study the synergies between vision and communications for edge-computing-enabled UAV flight. By proposing a framework of edge computing assisted autonomous flight (ECAAF), we illustrate that vision and communications can interact with and assist each other with the aid of edge computing and offloading, and further speed up UAV mission completion. ECAAF consists of three functionalities that are discussed in detail: edge computing for 3D map acquisition, radio map construction from the 3D map, and online trajectory planning. During ECAAF, the interactions of communication capacity, video offloading, 3D map quality, and channel state of the trajectory form a positive feedback loop. Simulation results verify that the proposed method can improve mission performance by enhancing connectivity. Finally, we conclude with some future research directions.