University of Oulu

H. Shiri, J. Park and M. Bennis, "Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach," 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019, pp. 1-6,

Massive autonomous UAV path planning : a neural network based mean-field game theoretic approach

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Author: Shiri, Hamid1; Park, Jihong1; Bennis, Mehdi1
Organizations: 1Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.7 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-05-04


This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting). Achieving their fast travel and low motion energy without inter-UAV collision under wind perturbation is a daunting control task, which incurs huge communication energy for exchanging UAV states in real time. We tackle this problem by exploiting a mean-field game (MFG) theoretic control method that requires the UAV state exchanges only once at the initial source. Afterwards, each UAV can control its acceleration by locally solving two partial differential equations (PDEs), known as the Hamilton-Jacobi- Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. This approach, however, brings about huge computation energy for solving the PDEs, particularly under multi-dimensional UAV states. We address this issue by utilizing a machine learning (ML) method where two separate ML models approximate the solutions of the HJB and FPK equations. These ML models are trained and exploited using an online gradient descent method with low computational complexity. Numerical evaluations validate that the proposed ML aided MFG theoretic algorithm, referred to as \emph{MFG learning control}, is effective in collision avoidance with low communication energy and acceptable computation energy.<7p>

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Series: IEEE Global Communications Conference
ISSN: 2334-0983
ISSN-E: 2576-6813
ISSN-L: 2334-0983
ISBN: 978-1-7281-0962-6
ISBN Print: 978-1-7281-0963-3
Pages: 1 - 6
Article number: 9013181
DOI: 10.1109/GLOBECOM38437.2019.9013181
Host publication: 2019 IEEE Global Communications Conference, GLOBECOM 2019
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was supported in part by Academy of Finland (Grant Nr. 294128), in part by the 6Genesis Flagship (Grant Nr. 318927), in part by the Kvantum Institute Strategic Project (SAFARI), and in part by the Academy of Finland thorough the MISSION Project (Grant Nr. 319759).
Academy of Finland Grant Number: 294128
Detailed Information: 294128 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
319759 (Academy of Finland Funding decision)
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