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

Shiri, Hamid; Park, Jihong; Bennis, Mehdi (2020-02-13)
 
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URL:
https://doi.org/10.1109/LWC.2020.2973624

Shiri, Hamid
Park, Jihong
Bennis, Mehdi
Institute of Electrical and Electronics Engineers
13.02.2020

H. Shiri, J. Park and M. Bennis, "Remote UAV Online Path Planning via Neural Network-Based Opportunistic Control," in IEEE Wireless Communications Letters, vol. 9, no. 6, pp. 861-865, June 2020, doi: 10.1109/LWC.2020.2973624

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doi:https://doi.org/10.1109/LWC.2020.2973624
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https://urn.fi/URN:NBN:fi-fe2020100983556
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Abstract

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.

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