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Remote UAV online path planning via neural network-based opportunistic control

journal contribution
posted on 2020-06-01, 00:00 authored by H Shiri, Jihong ParkJihong Park, M Bennis
This letter proposes a neural network (NN) aided remote unmanned aerial vehicle (UAV) online control algorithm, coined oHJB. By downloading a UAV's state, a base station (BS) trains an HJB NN that solves the Hamilton-Jacobi-Bellman equation (HJB) in real time, yielding a sub-optimal control action. Initially, the BS uploads this control action to the UAV. If the HJB NN is sufficiently trained and the UAV is far away, the BS uploads the HJB NN model, enabling to locally carry out control decisions even when the connection is lost. Simulations corroborate the effectiveness of oHJB in reducing the UAV's travel time and energy by utilizing the trade-off between uploading delays and control robustness in poor channel conditions.

History

Journal

IEEE wireless communications letters

Volume

9

Pagination

861-865

Location

Piscataway, N.J.

ISSN

2162-2337

eISSN

2162-2345

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

6

Publisher

IEEE