Remote UAV online path planning via neural network-based opportunistic control
Shiri, Hamid; Park, Jihong; Bennis, Mehdi (2020-02-13)
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
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2020100983556
Tiivistelmä
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.
Kokoelmat
- Avoin saatavuus [29917]